Sustainable Operations

Accepted Manuscript

Sustainable Operations Florian Jaehn PII: DOI: Reference:

S0377-2217(16)30099-6 10.1016/j.ejor.2016.02.046 EOR 13551

To appear in:

European Journal of Operational Research

Received date: Revised date: Accepted date:

11 April 2015 25 February 2016 28 February 2016

Please cite this article as: Florian Jaehn, Sustainable Operations, European Journal of Operational Research (2016), doi: 10.1016/j.ejor.2016.02.046

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Highlights • We present terms and definitions to clarify what is to be understood by "Sustainable Operations". • Sustainable Operations is demarcated from its neighboring topics. • Sustainable Operations is structured into various areas.

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• Future research perspectives are elaborated

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• For each area, we present examples of applications and refer to existing literature.

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Sustainable Operations February 2016

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Florian Jaehn

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University of Augsburg, Sustainable Operations and Logistics, Universitaetsstr. 16, D-86159 Augsburg, Germany [email protected]

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The field of “Sustainable Operations” and the term itself have arisen only in the last ten to twenty years in the context of sustainable development. Even though the term is frequently used in practice and research, it has hardly been characterized and defined precisely in the literature so far. For reasons of clarity and unambiguity, we present terms and definitions before we demarcate Sustainable Operations from its neighboring topics. We especially focus on the interactions between economic, social and ecological aspects as part of Sustainable Operations, but exclude the development of a normative ethics, instead focusing on the use of quantitative methods from Operations Research. Then the broad subject of Sustainable Operations is structured into various areas arising from the typical structure of an enterprise. For each area, we present examples of applications and refer to the existing literature. The paper concludes with future research directions.

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Keywords: Sustainable Operations, Sustainable Development, Operations Research, Computational Sustainability, Triple Bottom Line

1 Introduction and Definitions

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Sustainable development has become widely spread since the Brundtland report (Brundtland (1987)), especially due to Agenda 21, which was established in order to put the ideas of the Brundtland report into action. A great research field emerged, including normative aspects of the factors of a system’s sustainability and the implementation of such norms with qualitative and/or quantitative concepts. In the course of this, Sustainable Operations emerged, which rather focuses on the implementation and interactions of given policies than on developing the corresponding (ethical or natural scientific) norms. However, the notion of a sustainable operation has hardly been characterized and defined in the literature so far, and therefore the term is used in different contexts. It should be noted that often, even each of the words, “sustainable” and “operations” are used differently. Traditionally, the term “sustainability” comes

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from ecology but nowadays it is also used in the sense of the triple bottom line concept (Elkington (1998)) and therefore equally referring to environmental, economic, and social systems. Many authors interpret “Sustainable Operations” in the sense of sustainable actions (Gimenez et al. (2012), Simaens and Koster (2013)). However, others consider the term “operations” in the sense of Operations Research (Tang and Zhou (2012)). Furthermore, the phrase “Sustainable Operations Management” is frequently used (Kleindorfer et al. (2005), Gunasekaran and Irani (2014), Walker et al. (2014)), which in our eyes is only part of the more general concept of Sustainable Operations. We therefore start with some formal disambiguations in order to distinguish Sustainable Operations from neighboring topics. The adjective “sustainable” is used to describe a system and we restrict ourselves to using this adjective in connection with some kind of system. Thus, we first have to define “system,” which is already difficult as a look at sociology shows (Parsons (1971); Luhmann (1984)). Without dwelling on systems theory, we use the following definition of a system.

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Definition 1.1 (System) A set of elements having a mutual, functional relation to each other, which can thereby be delimited from their environment, is called a system.

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Note that the environment mentioned in this definition refers to the surroundings of the set of elements but not to the environment in the sense of “nature.” We may distinguish between open systems and closed systems. In the former, the environment can interact with the elements and can affect the elements. In a closed system, such an interdependence does not exist. For example, the (ecological) system of a forest can be interpreted as the area in which the trees grow, including all living and non-living beings permanently located in that area. Then, for example, humans and the atmosphere are part of the environment, having interactions with the system. Closed systems are mostly used in theoretical considerations. An example of a closed system could be a miniature biosphere in a glass (though one could question whether there is really no interaction with the environment). In the following, we will focus on open systems. We consider this concept of a system especially from an economic point of view (e.g., economic systems or companies), from an ecological point of view (e.g., ecological systems or biotopes), and from a social point of view (e.g., social systems such as families or the workforce of a company). Other systems explicitly mentioned by Brundtland (1987) (the conclusion of chapter 2) include political systems, production systems, technological systems, and administrative systems. Furthermore, this very general definition of a system also includes completely theoretical systems as well as physical systems, mental systems, etc., although they will not be considered explicitly in this paper. Definition 1.2 (Sustainable System) A system is called sustainable if the environment influences the system in such a way that it can exist permanently. The “permanent existence” of a system is not to be understood in a strict sense, but rather in the sense of a very long time horizon. A very strict interpretation would e.g. to not allow for any sustainable systems on earth, as the earth will very likely be destroyed by falling into the sun in 8 billion years (Schröder and Connon Smith (2008)). Yet, influences of the environment need to be considered not only in a static way, but also in the sense of all future effects that might be caused by today’s influences.

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Obviously, a closed system (which is not doomed by itself) is always sustainable as there are no effects from the environment that could harm the existence of the system. In general, we will assume that human interactions (i.e., the decision making process) are in the environment of any system under investigation. If the decision making process was included in the system, there would only be possible a descriptive analysis of the potential sustainability of the system (the chicken or the egg dilemma). This distinction is important in order to distinguish between the anthropogenic influences on a system and the characteristics of the system itself. Note that men may still be part of the systems that we analyze, but not the decision making process. In Figure 1, we can see that more than one system can be analyzed at the same time. Furthermore, the systems may but need not overlap, and interactions between the systems, which are denoted by arcs, are prevalent.

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Figure 1: Interactions of systems with their particular environment. Based on the above definition, a system is therefore sustainable if the environment

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1. only adds as much (harmful) things within a period of time to the system as can be absorbed by the system within this time, and

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2. only removes as much (essential) things from the system within a period of time as can be renewed by the system in this time.

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In the discussion on strong and weak sustainability (see Neumayer (2003); Ott and Döring (2004)), this definition should be classified as a concept of strong sustainability, even though we directly connect it to the triple bottom line. Let us transfer these theoretical definitions to some applications. The concept of sustainability appeared first in the forestry sector, when wood was scarce at the beginning of the 18th century. The then-proclaimed maxim “only lumber as much wood as can grow again” was supposed to make the (ecological) forest system sustainable. In this context, the environment of the forest system is the set of human beings exerting an influence on the system by lumbering, but potentially also by planting trees. As already mentioned, we focus on economic, environmental and social sustainability, i.e., the sustainability of corresponding systems. Since the United Nations Conference on Environment and Development in Rio de Janeiro in 1992, from which the Agenda 21 also emerged, these three areas are seen as equally important pillars of sustainable development (the Triple

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Bottom Line, 3BL). It is assumed that the sustainability of one of these three systems can only be achieved by way of the sustainability of the other two. For example, the earth’s ecological system is influenced by the economy and by the social system, i.e., the latter two are within the environment of the earth’s ecological system. Similarly, the economy and society are influenced by nature. Of course, the interdependency of economic, ecological, and social systems also exists for smaller systems. For example, a small company can be seen as an economic system as well as a social system, both interacting with the (regional) ecological system and a decision maker, e.g., from the company’s management, should keep these systems sustainable. However, we may see that some decision processes do not influence systems from all three areas. For example, workforce scheduling has a great impact on the economic system “company” and on the social system “staff,” but the decision process has little interaction with environmental systems. So let us take a closer look at the sustainability of systems from the three areas. An enterprise operates sustainably in the economic sense if it solely lives on its returns, but not on its substance. In general, it is the goal of all businesses to act economically sustainably. Accordingly, business administration as a whole deals with enabling economically sustainable business and moreover maximizing profit. A company or a public enterprise living solely on the sale of premises and real properties owned by the company or the public enterprise is certainly not sustainable. Obviously, such a pure focus on the economic interest of an enterprise defines ecological and social systems to be in the environment of the economic system and neglects their sustainability. We categorize economic systems based on three major groups of decision makers coming from business (including trade associations), public institutions, and nongovernmental organizations. With this, important economic systems to be considered in this paper can be subsumed as

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1. companies, conglomerates, specific industries 2. public institutions (e.g. municipal authorities), economic regions, countries 3. non-governmental organizations (NGO, e.g. the Red Cross).

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In contrast to economic and social systems, the issue of sustainability in ecosystems is much discussed in public. A major focus is on the consumption of scarce resources, such as wood, oil, coal, water, and rare earth minerals, which “regrow” (in part) extremely slowly. The impairment of a local or global ecosystem by emissions or other influences is included here as well. We categorize ecological systems by

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1. regional ecosystems in the surrounding of the decision maker (e.g. a forest, fauna flora habitats), 2. raw material sources of the primary sector (e.g. coal deposits), 3. the global atmosphere, 4. regional and global waterbodies (e.g. runlets, meadows, oceans).

For social systems, it is often hard to determine the factors which make the system sustainable. According to the Brundtland report, two aspects are crucial. Firstly, equal opportunities within the system should be guaranteed at all times. This refers to the local and global distribution of resources (poverty reduction) and the satisfaction of basic needs. Secondly, the temporal aspect of assuring equality of opportunity for various times (inter-generational fairness) is essential as well. These two aspects are in line with the definition of a system’s

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sustainability if we assume that not guaranteeing them leads to an accumulation of dissatisfaction that will be unleashed at some point in time and threaten the system. For more recent works on social responsibility in Operations Research and Operations Management, see Drake et al. (2011) and Sodhi (2015). Social systems analyzed in Sustainable Operations are staff of an enterprise or one of its divisions (e.g. shift workers), families all or some inhabitants of a residential area (e.g. the people of a certain town) people in general (all human beings on earth).

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1. 2. 3. 4.

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We will not explicitly distinguish social and societal aspects, the latter being covered in the fourth group. Being in line with the general assumption that environmental, economical, and social sustainability require each other, a reflection on only one of these aspects should not be considered as part of Sustainable Operations. Especially a pure focus on economic goals is part of traditional business administration and should not be labeled as Sustainable Operations. We will therefore only consider questions that involve at least one of the two further dimensions of sustainability in addition to economic sustainability. Considering the different interpretations of the term “operations” with regard to Sustainable Operations, we suggest adapting the idea of Operations Research (OR) or Management Science (MS). The reasons are simple. If Sustainable Operations were interpreted in the sense of “sustainable actions,” it could hardly be distinguished from the very general topic of sustainable development and it would barely embrace the topic as it is used in the literature. The same holds true for Sustainable Operations Management, which in contrast to traditional Operations Management focuses on the operational aspects of production and services and therefore reflects a rather small area of economic activities. We summarize the previous considerations in the following definition of Sustainable Operations.

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Definition 1.3 (Sustainable Operations) Sustainable Operations is a field of research that models the quantitative aspects of business administration, which in addition to economic objectives aims equally at sustainability in the environmental and/or social sense, and applies methods from Operations Research to solving these models.

1.1 Demarcation

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The above definition of Sustainable Operations describes a strong connection to business administration (which should not hide the fact that Sustainable Operations is certainly multidisciplinary). Therefore, we consider some enterprise, which also sets ecological and/or social goals. For our considerations, it is irrelevant whether these additional goals are intrinsically motivated, chosen due to external requirements (especially laws), or whether the enterprise thereby gains a competitive advantage. The latter point is not to be underestimated, since any kind of “sustainable actions” can be perfectly well used for marketing purposes and reducing the likelihood of scandals that could threaten the long-term survival of a company. With the focus on enterprises, the consideration of the economy as a whole, as does, e.g., Macroeconomics, appears in Sustainable Operations only in exceptional cases. Thus, topics like the development of a strategy for reducing national or global greenhouse gas emissions

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are left out, whereas the reduction of emissions of a company is indeed part of Sustainable Operations. Sustainable Operations focuses on methods that aim at the sustainability of various systems assuming that information is given on how the sustainability of these systems can be achieved. For example, we may consider problems of minimizing staff costs in line with maximum worker satisfaction based on the assumption that satisfaction levels are known to guarantee the sustainability of the corresponding systems. However, Sustainable Operations does not aim at determining norms that are agreed on to make a system sustainable (e.g., the question on how much workers should be satisfied). Let it be mentioned that we exclude a large and very important part of sustainable planning if we do without value judgments. For example, in decision making, we again and again face the question of what actions are to be considered economically, ecologically, morally, etc. acceptable. These considerations are often subjective and philosophical, yet indispensable for an equal consideration of economic, environmental and social sustainability (when does equality exist?). In this sense, Sustainable Operations considers questions that do not develop standards, but rather tries to reconcile given requirements and objectives of the different systems. For example, it is not to be determined how many emissions are acceptable, but rather, for a given limit of emissions (which could be defined as a “soft” or generic constraint), it should be decided how this limit can be reached. Norms for systems are mostly obtained by legislation (e.g. accounting regulations, carbon taxes, minimum wages), by politics (e.g. subsidies for certain sectors, for regenerative energy sources, for families), by social norms (e.g. acceptance of attitudes), and of course by science (e.g. business administration for the sustainability of companies, climatology for the atmosphere, medicine for acceptable working conditions). Note that according norms on the long time existence of a system also help to evaluate decisions that rather focus on a short term. Compared to Sustainable Development in general and to Sustainable Operations Management in particular, the methodology of Sustainable Operations is somewhat restricted when considering the quantitative concepts of Operations Research and Management Science. Purely conceptual work is therefore not part of Sustainable Operations. Going even further, there are quantitative approaches which cannot be seen as part of Operations Research or Management Science, and are not considered either. This includes approaches that deal mainly with data collection and interpretation. The wide area of calculating carbon footprints and interpreting such data using statistical methods particularly falls in this category. Still, we consider Operations Research and Management Science in its broadest sense, including prescriptive (optimization) models, descriptive models (e.g. simulation) and (algorithmic) game theory. Sustainable Operations strongly overlaps with the new field of Computational Sustainability (Gomes (2009)). Both place their main emphasis on quantitative methods for achieving sustainability. If one wants to see a difference between these fields at all, it is most likely the fact that Sustainable Operations rather emerges from the field of business administration, whereas Computational Sustainability has its origins in Computer Science. However, Sustainable Operations and Computational Sustainability are both highly interdisciplinary fields. For an adequate description of economic, environmental and social systems, the participation of scientists from biology, chemistry, medicine and the social sciences is usually unavoidable. To process the immense amount of data, knowledge from computer science and statistics is required. Solution procedures often arise from Mathematics. Microeconomics, especially game theory, plays an important role as well. As mentioned before, Sustainable Operations focuses on the interactions between economic,

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environmental, and social sustainability. Let us point out again that a focus solely on economic aspects cannot satisfy sustainability in the sense of the Brundtland report. Hypothetically this would be true if economic, environmental, and social objectives are strictly complementary. However, such a situation appears only in very exceptional cases, as most often some conflicts appear between any two dimensions. For example, consider the very common idea of reducing emissions by reducing fuel consumption. If we considered fuel consumption as the only economic goal (which is usually not true), this approach still would completely neglect rebound effects (see, e.g., Greening et al. (2000)). More generally, even though it is reasonable from an economic and ecological viewpoint to minimize the required resources for production, the economic point of view reduces resources required per item whereas ecologically, the total consumption of resources matters. In general, we believe that rebound effects are insufficiently considered in many studies, which is most likely caused by the challenges of quantifying these effects. Besides these drawbacks, we consider studies explicitly focusing on two dimensions of sustainability to belong to Sustainable Operations, even if they presume complementary goals. Note that the simultaneous consideration of conflicting goals does not necessarily imply multi-objective approaches. It is only required that these goals find some consideration, e.g., as satisfactory goals, which could then be considered as constraints. Especially the implementation of certain ecological or social standards requires models and solution procedures such that these standards can be reached at minimum cost. Such models should obviously not be seen as purely economic models. If the conflicting goals can be expressed in monetary values, which often holds true for economic goals but also, e.g., for emissions within an emission trading system, the goals can easily be incorporated into a single objective. After structuring the field of Sustainable Operations, various examples will be presented giving an even deeper insight into the demarcation of Sustainable Operations.

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1.2 The Structure of Sustainable Operations

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Sustainable Operations, being part of business administration, can most easily be structured using the functional structure of an enterprise. This means that we rather use the structure of the decision making unit than the structure of the systems to be analyzed. Furthermore, we refrain from structuring the topic using the planning horizon, as often done in, e.g. supply chain management (see the supply chain planning matrix by Stadtler (2005), see also Meyr et al. (2015)). The reason is that many areas of Sustainable Operations, such as personnel management, contain strategic, tactical, and operational tasks. A structure based on the planning horizon would split these homogeneous areas. By way of example, we use the structure of a manufacturing company, the most important areas of which can be seen in Figure 2. However, this representation can easily be generalized so that it also applies to companies in the service sector, to public enterprises, and to nonprofit organizations. For the different areas listed in Figure 2, an incomplete list of subareas is given. Wherever applicable, we indicate the JEL classification codes. Logistics are shown as a cross-sectional function occurring in procurement, production and distribution. Logistics is located outside the company itself, in terms of tasks like supply chain management. In the following we will assign the fields of Sustainable Operations to these areas, which can also be seen as decision making units. On the management level, numerous decisions have not only economic, but also social and ecological importance. Whereas in some areas, such as organization or control, the use of

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Management (JEL M1)

Company

- Organization (JEL L2) - Planning - Human Resources (JEL M5)

Finance (JEL G3)

Accounting (JEL M4)

- Portfolio Optimization (JEL G31)

Financial Flows

Production (JEL D2,M11)

- Purchasing

- Machine Scheduling

- Inbound Logistics

- Production Management

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Distribution

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Material Flows

Procurement

- Marketing (JEL M3) - Outbound Logistics

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Figure 2: Common structure of manufacturing companies (based on Domschke and Scholl (2005))

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quantitative models plays a minor role, it is different in the area of planning and at least to some extent also in the area of human resources. In Section 2.1, we highlight the management issues belonging to Sustainable Operations. In the (strategic) planning area, we highlight facility location, which is just as present in public enterprises and non-profit organizations as in companies. Certainly, one could argue that this is part of logistics, but as the corresponding decisions are usually taken on a higher management level, we decided to list it here. In facility location planning, cost minimization is the prevalent objective for the sustainability of the economic system. Environmentally, local ecosystems but also the atmosphere are under consideration. In the latter, for example, this is the case if locations for facilities for renewable energy sources are to be determined. People from the region affected by the location define the social system. A quantitative problem in the field of human resources which is inevitably linked to social sustainability is workforce scheduling. Here, the enterprise and its staff are the economic and social system under investigation. On the management level, we also discuss risk management. In finance, especially investment decisions may raise environmental and moral concerns. Again, these questions are often answered on a qualitative, normative basis. However, there are portfolio optimization approaches, which we will address in Section 2.2. Whereas the economic system under observation is easy to define by the enterprise making the investments, the environmental and social systems may vary from asset to asset. The area of accounting is linked to sustainability problems as well. However, topics like “Sustainability Accounting” (Bebbington et al. (2014)) do not have any relation to Operations Research. Therefore, we ignore accounting in our considerations of Sustainable Operations. Similarly, applications in procurement and distribution mostly fall under Sustainable Operations if they are connected with logistics. We will address these issues in Section 2.3, when we deal with the logistical applications of Sustainable Operations. The small intersection of Sustainable Operations with procurement, which especially includes sustainable supplier selection, will be treated in Section 2.4. Logistics allows the integration of social and environmental aspects for numerous applica-

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tions. It includes the handling of used products (reverse logistics and waste management) and transport with respect to emissions. The latter is discussed with regard to vehicle routing but also with a focus on intermodal transport. Accordingly, the systems to be analyzed are local or global ecosystems affected by waste, and the atmosphere that is harmed through greenhouse gases. Besides environmental aspects, social issues appear when it comes to shared transport such as car sharing or ride sharing, and especially in humanitarian logistics. In practice, the logistical challenges of agglomeration have been tackled with so called city logistics systems. It seems that the Sustainable Operations literature on this topic is lagging behind. Again, the social systems to be considered can be defined by the people living in communities that are affected. In Section 2.5, we consider problems related to production. Here, ecological issues are much more prevalent compared to social issues. The latter are only regarded in the context of the continuous availability of essential goods or by minimizing injury rates. Sustainable Operations plays a role in the production process (including inventory), which is often analyzed in the economic sense (minimizing costs or time) and concurrently minimizing energy consumption and/or emissions. A further field is product recovery in which inventory management becomes more complex due to stochastic inputs to the inventory and which bears further challenges such as disassembly line problems. We may note that Sustainable Operations covers an enormous number of questions related to specific industries or even products. The functional differentiation presented above implies that, for example, production related problems may differ strongly between industrial sectors. It would be possible to further categorize Sustainable Operations based on specific industries. We refrain from doing so as it is not possible to cover all the peculiarities of single industry sectors. However, due to their significant impact on economic, ecological, and social sustainability and their high challenges for OR, we will consider issues of the primary sector and a specific problem from the energy sector in more detail. In the primary sector, we focus on agriculture, forestry, and fishing, which are the cradle of Sustainable Development and deliver us the essential goods for living. The energy sector has its relevance due to its high carbon emissions and nuclear waste. Both are seen as a considerable threat to the global environment and they are to be reduced by turning from fossil and nuclear power to renewable energy sources. At the same time, a continuous supply of (cheap) energy has to be ensured, as it is of high economic and social relevance. Finally, we briefly treat water resources planning, health care, and OR in developing countries.

2 The Fields within Sustainable Operations

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As Sustainable Operations has not been defined in the literature so far, an overview of this topic is lacking. The closest work to mention here is the paper by Tang and Zhou (2012). They provide a review of most recent papers tackling problems within Sustainable Operations. The papers are classified into strategic and operational problems. They cover topics from production (including product design), logistics, and supply chain management. Their focus is therefore more narrow than ours, which includes public institutions and non-profit organizations. Thus, we update and extend their literature review. However, as there are thousands of papers that come under Sustainable Operations, the scope of this section is rather to give examples of applications than to provide a complete literature review. The cited papers are selected in a way that they should give the reader a starting point for getting access to the

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topic. If there is a recent survey, we usually restrict ourselves to listing that and possibly complementing it with some other important or more recent papers. If there is no survey or just an outdated one, we list a few publications with high relevance to the topic. Besides Tang and Zhou (2012), we would like to draw the reader’s attention to a few further papers with great relevance to the general framework of Sustainable Operations. Already in 1995, Greenberg (1995) presented an overview of optimization models for environmental quality control. The papers surveyed by Greenberg (1995) certainly fall within Sustainable Operations. White and Lee (2009) describe a general framework for how social aspects can be integrated with OR approaches. Ormerod and Ulrich (2013) give a great overview of Operations Research approaches that include ethical questions and the ethics of OR itself. In the following, we list the most relevant topics of Sustainable Operations found in the literature. The literature search was performed using “Scopus” and “Google Scholar” (including the “cited by” option), using many different search terms for each area. Furthermore, we searched in various journals, the selection of which, again, depended on the topic. Let us mention again that the listed literature is to be understood as a first access to the topic instead of a complete literature review.

2.1 Management

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2.1.1 Facility Location

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Even though Operations Research models more often treat ecological aspects than social aspects (White and Lee (2009); Tang and Zhou (2012); Seuring (2013)), this hardly holds true for the management area. If other objectives besides purely economic ones are considered, then social aspects play a major role. Important areas of incorporating social and/or environmental issues are facility location and workforce scheduling. After taking a look at these topics, we consider risk management as a further, miscellaneous topic.

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Location planning stems from business administration and goes back to the 19th century (see, e.g., Launhardt (1882)). Today, issues of business administration are still dominant in the field of facility location, as can, for instance, be seen by a look at the publications in this area (ReVelle and Eiselt (2005); Melo et al. (2009)). Nevertheless, the proportion of publications also considering social objectives (e.g., in the planning of public utilities such as hospitals) or environmental objectives (e.g., systematic settlement of endangered animals and plants) is increasing. To start with, we look at the objectives of location planning. It should be mentioned that the social and particularly ecological goals almost always depend on the specific application and can only be exemplified here. Objectives of location planning from an economic perspective: Even though location decisions might have some influence on revenue (see, e.g., Hübner and Kuhn (2012)), the main focus is on cost reduction. These usually include running costs and investments for a location to be selected as well as transportation costs to and from the location to be selected. Other objectives, which can hardly be quantified by costs directly, include the proximity to supply and sales markets, potentials and risks of the surroundings, and workforce availability for different qualification levels. Objectives of location planning from an environmental perspective: In the context of ecology, a very prevalent though certainly not exclusive topic is the cost efficient location

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planning of facilities that serve some ecological benefit (e.g., positioning of solar plants or cost-efficient establishment of nature reserves). However, the specific objective depends – as mentioned earlier – very much on the application.

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Objectives of location planning from a social perspective: Here, the accessibility of facilities where goods (e.g., food) or services (e.g., doctors) are provided to the public is essential. Accessibility can be measured, for example, by the average or maximum distance from the individuals to the facilities. Yet, it can also mean positioning certain locations (such as toxic waste dumps) as far away as possible from certain areas.

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A typical method of searching for a suitable location is the following. First, potential locations are pre-selected and these are already rated with regard to cost–benefit considerations (excluding transportation). All potential locations and all other places that have a relation to the desired location(s) are registered in a list. Then the distance between any two of these places is calculated. This usually leads to a representation of the location problem as a graph problem. Already the very simple graph theoretical location problems such as p-median problems, p-center problems, and warehouse location problems have found application to determining the locations for public institutions, and therefore consider social aspects. For example, school location can be modeled as a p-median problem (Pizzolato (1994); Pizzolato et al. (2004)), although other modeling approaches, such as integer linear programming, exist (Araya et al. (2012); Haase and Müller (2013)). Especially Haase and Müller (2013) is worth mentioning, as it considers the utility functions of the students and therefore explicitly addresses social aspects. Similarly, the location of hospitals can also be considered as a p-median problem (Sinuany-Stern et al. (1995)), although again, mixed integer programming (MIP) formulations exist (Chu and Chu (2000)) as well as network design approaches (Mestre et al. (2015)). A very specific problem of hospital location in areas affected by natural disasters is considered by Paul and Batta (2008). Similar approaches could be applied to waste location (Erkut et al. (2008)). However, in this context it should be mentioned that the waste location is often treated using combined location–routing approaches (Zografros and Samara (1989); Alumur and Kara (2007); Samanlioglu (2013)), which could rather be seen as part of logistics than management. As one can imagine, the methodology for locating other public utilities, such as ambulances or emergency departments, fire departments, police stations, or even bus stops and stations, is similar. Besides some important papers from recent years, let us only refer to a few selected publications. Pirkul and Schilling (1988), Brotcorne et al. (2003), and Li et al. (2011) treat the location of ambulances and emergency departments. Fire departments are considered by Badri et al. (1998) and Aktaş et al. (2013). The wide-spread topic of bus stop locations is treated by Gleason (1975), Moura et al. (2012), and Wei and Ma (2014). Surprisingly, the above mentioned problems have rarely been considered as warehouse location problems, which would allow an integration of (current) facility costs. Another problem with similarities to the warehouse location problem is the following. Due to increasing urbanization, deforestation, and increased agricultural use of land, the habitat of many animal and plant species has declined. In order to ensure adequate habitats for these species, one option is, for example, to create nature protection areas or similar reservations. This results in a location problem of determining the appropriate areas, see, e.g., Polasky et al. (2008). Modeling this as a WLP could include the effort of relocation (or also the probability of relocation)

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as transport costs and the cost of provision as well as an evaluation of the suitability of the location for the species as location costs. In line with the above mentioned ‘classical’ location problems, let us mention the maximum availability location problem, which was introduced by ReVelle and Hogan (1989). It is certainly a representative model for locating public utilities. In contrast to finding locations that are easily accessible, certain facilities such as waste dumps are for obvious social reasons not to be placed close to residential areas. A survey of locating solid waste facilities is given by Eiselt and Marianov (2015). In order to act ecologically sustainably, in many areas it is essential to gather comprehensive data to be able to identify the need for action and to evaluate the alternatives. Appropriate biological, chemical, climatological, or similar data can often be collected via sensor networks, see Krause and Guestrin (2009) and Zhou et al. (2015). The determination of the locations for the sensors can, in the most simple version, be interpreted as a p-center problem. In this context, see also Wei et al. (2014). Facility location problems are often considered in the course of reverse logistics (Louwers et al. (1999); Lu and Bostel (2007)). Reverse logistics, including location problems, will be considered below. A further aspect closely related to location planning is the topic of network design (Fleischmann et al. (2001); Srivastava (2008)), which could also be seen as part of logistics or supply chain management. A review on supply chain network design with a focus on competitive environments is given by Farahani et al. (2014). Eskandarpour et al. (2015) present a review on sustainable supply chain network design. The above mentioned facility location problems are based on graph representations. Other classical location problems, such as placing facilities in a plane (e.g., the Steiner–Weber problem) or layout planning, have hardly been used in Sustainable Operations. To some extent, however, layout planning also applies to the preservation of a species, since sometimes habitats connected by corridors for animals must be ensured. See Önal and Briers (2005) and Wang and Önal (2015), who however use an integer programming approach. Dong et al. (2012) consider the layout planning for an urban wastewater system.

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2.1.2 Workforce Scheduling

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Most areas of human resource management are rather related to organization, industrial and organizational psychology, and/or the social sciences. Only when it comes to workforce scheduling are quantitative questions related to Sustainable Operations common, and therefore we limit ourselves to this aspect. Workforce scheduling is a prime example of a field which allows the integration of social aspects. However, human resources as a whole and workforce scheduling in particular were at first considered from a purely economic point of view (see in particular Taylor (1911)). Social goals, if any, were only considered if they directly contributed to the economic objectives. This has changed over time. On the one hand, this is due to the many legal regulations. But on the other hand, an awareness has developed that the economic and the social system of an enterprise are highly interdependent in terms of human resources (“Only a satisfied employee is a good employee”). So let us start with the objectives of workforce scheduling. Objectives of workforce scheduling from an economic perspective: In workforce scheduling, the costs caused by the staff are to be minimized in compliance with a (possibly non-deterministic) given, necessary workload to be processed by the staff. In addition

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to pure labor costs, these also include the costs resulting from employees’ actions (e.g., rejects, theft, waste of working hours, etc.), although these costs are usually difficult to quantify.

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Objectives of workforce scheduling from a social perspective: All social objectives can be summarized under the objective of maximizing job satisfaction. Job satisfaction includes quantifiable benefits, such as wages, retirement funds, number of leave days, etc., on the one hand. On the other hand, areas which cannot be measured that easily have a larger effect on job satisfaction. This includes the design of the workplace, measures of accident prevention, ergonomic stress, development and career opportunities, cafeteria food, flexibility of working times, etc.

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Already the very basic concepts of shift scheduling with cyclic demand and a (legally) given shift length can be seen as part of Sustainable Operations, as it requires OR techniques and has a socially motivated restriction. In this context let us mention the ideas of Bechtold (1981) and for more flexible shift patterns the heuristic “First Period Principle,” which was developed by Nanda and Browne (1992). For a more recent approach to problems with cyclic workforce demand, see Rocha et al. (2013). Even though one can find recent publications which focus on economic aspects and neglect the needs of the staff by circumventing the laws on working time, most papers in this area give at least clear attention to legal constraints. In the following, we will therefore only consider some certain parts of workforce scheduling with a strong connection to Sustainable Operations and the reader is referred to numerous survey papers for a general overview of personnel scheduling (De Causmaecker et al. (2004); Brucker et al. (2011); Van den Bergh et al. (2013); De Bruecker et al. (2015)). Besides integrating legal working time constraints into the optimization models, there are many approaches that include the preferences of the staff. In this context let us mention the dissertation of van der Veen (2013), which deals with personnel preferences in workforce scheduling. Spengler (2006) presents a very general approach for integrating personnel preferences using fuzzy logic. Other papers take into account the high value of free weekends, see, e.g., Yura (1994), van der Veen et al. (2015) and Rong (2010). The latter paper also deals with tour scheduling, a field in between logistics and workforce scheduling. Brusco and Jacobs (2000) present a tour scheduling model allowing for meal breaks and start-time flexibility. For a general overview of tour scheduling, see Alfares (2004) and Castillo-Salazar et al. (2014). The continuous repetition of certain tasks may cause severe ergonomic risks for the staff. Therefore, job rotation problems have been developed that minimize these risks (AsensioCuesta et al. (2012); Otto and Scholl (2013)). Some approaches even consider the assembly line balancing problem with the objective of minimizing ergonomic risks at the workstations (Otto and Scholl (2011); Bautista et al. (2016)). As the requirements of the staff differ considerably in different working sectors, workforce scheduling models are often specified for certain occupational groups. Not only do the legal restrictions differ in these sectors, but also the preferences of the staff might change. For example, traveling employees will generally consider a day off at the home base more valuable than one away, whereas locally used employees will not see such a difference. For some sectors, the regulations are very specific and detailed in probably most countries, leading to complex workforce scheduling problems. One of the first such approaches was for police patrol (Taylor and Huxley (1989)). Further areas of application include airline crew scheduling (Barnhart

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et al. (2003); Kohl and Karisch (2004); Medard and Sawhney (2007)), nurse rostering (Cheang et al. (2003); Burke et al. (2004); De Causmaecker and Van den Berghe (2011)), physician schedules (Stolletz and Brunner (2012); Fügener et al. (2015)), truck driver scheduling (Kopfer and Meyer (2010); Goel et al. (2012)), workforce scheduling at call centers (Dietz (2011); Defraeye and Van Nieuwenhuyse (2016)), in railway operations (Abbink et al. (2005); Alfieri et al. (2007)), at postal services (Bard et al. (2007); Brunner and Bard (2013)), and in retail chains (Talarico and Duque (2015)).

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2.1.3 Risk Management

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Actions of any enterprise may harm social and/or environmental systems such that the existence of the enterprise can directly be threatened. Especially these kinds of risks allow risk management to be a part of Sustainable Operations. In their review, Brandenburg and Rebs (2015) come to the conclusion that risk aspects are still underrepresented in the context of sustainable supply chain management and that the formalization of risk aspects is a promising branch for future research. Heckmann et al. (2015) present a review on supply chain risks. Whereas many risk management approaches focus on purely economically driven risks such as supply and demand uncertainty, or supply chain coordination risks (Fahimnia et al. (2015b)), Giannakis and Papadopoulos (2016) develop a risk management framework for sustainability related risks.

2.2 Finance

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Environmental and social aspects are considered in the financial sector under the slogan “Socially Responsible Investment” (SRI). Reviews on SRI are provided by von Wallis and Klein (2015) and van Dijk-de Groot and Nijhof (2015). Besides the question on which investments are socially and/or environmentally responsible and the development of appropriate indicators, the portfolio selection problem belongs to SRI. The latter is part of Sustainable Operations and discussed in the following.

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Objectives of portfolio selection from an economic perspective: The classical objectives of portfolio selection are maximizing returns and minimizing risks (by diversification). At least to some degree, liquidity is also respected.

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Objectives of portfolio selection from an environmental and social perspective: As it is possible to invest in a great many enterprises worldwide, the environmental and social impacts are manifold: they depend on the underlying enterprises, and they are hard to define. To overcome this information problem, there are various indicators used in order to rank the enterprises’ ethical values (Knoll (2002)). Therefore we assume that the relevant information of the companies’ environmental and social standards exists. The first approach (to the best of our knowledge) that incorporated SRI into portfolio selection problems was presented by Hallerbach et al. (2004). Here, all investment options are described in terms of various attributes, amongst which are some that “capture the effects on society.” Depending on the decision maker’s preferences, the portfolio is then determined using a multicriteria decision method. A similar problem setting is tackled by Jessen (2012), who solves the problem with two approaches: one is based on utility theory and the other is a mean-variance analysis with the additional constraint of portfolio responsibility.

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There are quite a few papers that not only consider the financial returns of an investment as being stochastic, but also the social returns. Dorfleitner and Utz (2012) tackles the corresponding portfolio selection problem with a safety first model. Bilbao-Terol et al. (2012) and Calvo et al. (2014) make use of fuzzy sets, and Bilbao-Terol et al. (2015) additionally consider behavioral aspects. Ballestero et al. (2012) partition all potential investments into two categories. In the first one, there are assets which have been evaluated financially and ethically and in the second category assets are located that have only been evaluated financially. In their bi-criteria model, the first goal corresponds to the classic financial objective of maximizing the returns whereas the second goal is to invest in as few assets that have not been evaluated ethically as possible. For the classical Markowitz portfolio selection problem, which aims at maximizing expected returns and minimizing variance, the set of non-dominated portfolios can be calculated in closed form. Hirschberger et al. (2013) show that the non-dominated set can still be computed even if a third (linear) criterion is added. Based on this result, the same authors (Utz et al. (2014)) choose maximizing social responsibility of assets as the third criterion and apply the new model to mutual funds. Interestingly, they state that after the screening process of assets, there seems “to be no significant difference in how assets are allocated in socially responsible and conventional mutual funds.” Adopting the same methodology, Utz et al. (2015) come to the conclusion that sustainable mutual funds available on the market could increase their sustainability quotients without any negative effects on the financial side.

2.3 Logistics

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In this section, we will focus on the area of transport logistics (see Psaraftis (2016)) and certain parts of supply chain management. Topics in the intersection of logistics and production, especially inventory models, are treated in the section on production (due to its similarities with dynamic lot sizing). Transport logistics are especially important for the procurement and distribution of a company. This branch of logistics is gaining relevance. For example, in Germany, forecasts project a doubling of freight traffic by 2050 (see Sachverständigenrat für Umweltfragen (2013)). Freight traffic, especially road transport, significantly contributes to the unresolved problems of climate policy due to CO2 emissions. If we want to consider the impact of road transport in social and environmental terms, we must take into account that most people (at least in industrialized countries) live in urban agglomerations. On the one hand, agglomerations have an immense advantage over sparsely populated areas, since land, infrastructure, and energy can be used efficiently. Central facilities such as schools, hospitals and shopping centers are easily and quickly accessible. Yet, at the same time, the question of the quality of life in densely populated areas arises, including environmental and social aspects. The goal of sustainable logistics especially in urban agglomerations must therefore be the pursuit of certain quality goals. These include, for example, land use, noise and air pollution caused by traffic, but also safety aspects or the maintenance of a nice cityscape. Thus, sustainable logistics comprises considerably more than the pure reduction of CO2 emissions. The objectives can be summarized as follows: Objectives of logistics from an economic perspective: The number and type (e.g., in terms of fuel consumption) of vehicles used, the length of the distance traveled, and the number of drivers required per vehicle have significant impact on the costs. Especially in freight traffic, the frequency and length of empty trips is a relevant cost factor. A combined routing and order management can even yield additional revenue.

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Objectives of logistics from an environmental perspective: The transport of goods has a negative impact on the environment in almost all modes of transport. In addition to the emission of harmful greenhouse gases, there are numerous ecologically undesired effects of transport that are to be reduced as part of a logistics planning. Examples are emissions of particulate matter and noise. In addition, the sealed surfaces needed for transport are to be kept as small as possible.

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Objectives of logistics from a social perspective: From a social perspective, transport has many negative influences. In addition to physical hazards such as noise or accidents due to excessive traffic, the negative side effects of transport also have a significant impact on the quality of life. These negative aspects are to be minimized by the appropriate transport concepts and the elimination of unnecessary trips. Nevertheless, from a social point of view, logistics should help provide goods and services to people and allow the freedom to move.

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Concerning the objectives, once more we would like to point out that the commonly considered approach of reducing emissions by reducing fuel consumption has to be viewed critically. In fact, and most intuitively, both objectives seem to be correlated in most applications. However, for a company, the fuel cost per product is the relevant decision criterion whereas for the ecology, the total amount of emissions is relevant. Therefore, rebound effects seem to exist in fuel consumption just as is the case in energy use in general (Greening et al. (2000)). At least theoretically, rebound effects can go so far that the fuel savings (per product or per distance) lead to an increasing demand for transportation, so that the total emissions increase. Such a situation is referred to as “backfire.” Even though Sorrell et al. (2009) state that for energy consumption, backfire is unlikely, we are not aware of any study that ensures that backfire does not appear. We start with some general discussions on sustainable supply chain management and closed loop supply chains. Let us note that by listing supply chain management (SCM) in the section on logistics, we do not want to argue for SCM being part of logistics (the “traditionalist” view according to Larson and Halldorsson (2004)). SCM usually comprises many tasks, such as location planning, workforce scheduling, production planning, inventory, etc., that are also found in single enterprises. Therefore, if these aspects fall under Sustainable Operations, they are treated in the corresponding sections of the present paper. As SCM is highly connected to logistics, we use this section to give the reader a starting point for the literature on the intersection of SCM and Sustainable Operations. This intersection especially comprises quantitative methods for sustainable SCM and closed loop supply chains. An introduction to sustainable SCM is given by Linton et al. (2007). Quantitative models for sustainable SCM have been reviewed by Seuring (2013) and Brandenburg et al. (2014). They both cover a huge amount of the literature and the latter considers the integration of social and ecological aspects. In contrast, the review by Fahimnia et al. (2015a) rather focuses on the “green” aspect of SCM. After considering closed loop supply chains, when going into more detail, we start with topics that rather focus on economic and ecological aspects. Afterwards we give examples of topics that combine economic and social aspects before finally coming to city logistics, which is a topic involving all three pillars of the triple bottom line.

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2.3.1 Closed Loop Supply Chains

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In closed loop supply chains, the consideration of the supply chain does not end with the product’s being delivered to the customer. The “reverse supply chain” is also examined, which consists of flows of goods and information in the context of reusing, remanufacturing, recycling, and disposing products. Important topics are the collection and transport of such products, inventory aspects, and production planning. A good introduction to this topic can be found in Guide Jr. and Van Wassenhove (2009) or more recently in Govindan et al. (2015b). See also the review of Sahamie et al. (2013) on transdisciplinary research in Sustainable Operations with application to closed-loop supply chains and the description on how to design sustainable closed loop supply chains in Hasani et al. (2012) and Devika et al. (2014). 2.3.2 Green Logistics

2.3.3 Reverse Logistics

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Closely linked to sustainable logistics is green logistics. The latter concentrates on the interactions between the economic and environmental aspects of logistics, but rarely consider social aspects. Green logistics also includes conceptual work and often enough it is hard to draw a clear line between green logistics and (sustainable) SCM. There are several review papers on green logistics (Murphy and Poist (2000); Sbihi and Eglese (2010); Dekker et al. (2012)). It should be mentioned that Sbihi and Eglese (2010) highlight combinatorial optimization problems in green logistics and therefore strike into the heart of Sustainable Operations. The paper by Dekker et al. (2012) highlights various problem settings in green logistics, which could (or should) be tackled using OR methods, but which have not appeared in the literature so far.

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“Reverse logistics encompasses the logistics activities all the way from used products no longer required by the user to products again usable in a market” (Fleischmann et al. (1997)). The idea of reusing products or product parts is generally considered to be environmental friendly because of its waste preventing character. Thus, tasks of reverse logistics, i.e., reverse distribution planning, inventory management, and production planning are part of Sustainable Operations. After the first review paper by Fleischmann et al. (1997), several other surveys of reverse logistics have appeared (Bostel et al. (2005); Meade et al. (2007); Pokharel and Mutha (2009); Govindan et al. (2015b)). We would like to especially highlight the paper by Ramos et al. (2014), who address tactical and operational planning decisions of a reverse logistics system. There are some reviews exclusively dedicated to reverse distribution planning (Jayaraman et al. (2003); Sasikumar and Kannan (2008b)). Inventory management and production planning with respect to used products will be considered in Section 2.5. 2.3.4 Waste Management Waste management is closely related to reverse logistics and often enough even being seen as part of reverse logistics. Even though this topic includes many conceptual aspects, parts of it belong to Sustainable Operations. Ghiani et al. (2014) review tactical and strategic ORproblems of solid waste management. On the strategic level, this includes the decision for each waste type on how it is disposed of (e.g., incineration vs. landfill vs. recycling), and the location and operation of the corresponding facilities. Tactical aspects are mostly connected

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to single service districts. Besides defining such districts, for each district one must decide about the associated facilities, the fleet of collection vehicles, and the collection days for each waste type. In this context, the waste-flow allocation problem has been intensively studied in literature. From an operational point of view, waste collection problems are most prevalent (Teixeira et al. (2004); Ghiani et al. (2005); Kim et al. (2006); Bautista et al. (2008); Battarra et al. (2014)). See also the review of vehicle routing problems (VRPs) for municipal solid waste collection by Beliën et al. (2012). Slightly different from solid waste management is the treatment of hazardous waste. In the most recent literature, the disposal of hazardous waste is mostly tackled with location–routing approaches (Alumur and Kara (2007); Emek and Kara (2007); Samanlioglu (2013); Ardjmand et al. (2015); Zhao and Verter (2015)). An application for infectious medical waste being produced by patients in self-treatment is considered by Nolz et al. (2014). Here, besides the distribution and inventory costs, satisfaction of pharmacists and patients and public health risks are considered as social objectives. 2.3.5 Green Vehicle Routing Problems

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VRPs traditionally focus on purely economic goals such as the minimization of the traveled distance, the travel time, the number of vehicles used, etc. If environmental aspects are integrated into these models, this is often referred to as Green VRP (this general term should not be confused with the specific VRP called the “Green Vehicle Routing Problem,” which was introduced by Erdoğan and Miller-Hooks (2012)). There are a few recent survey papers on Green VRP (Lin et al. (2014); Park and Chae (2014); Bektaş et al. (2016)). Besides the above mentioned distribution planning parts of reverse logistics, Green VRP have two main directions, namely the integration of the objective of minimizing emissions into VRPs and VRPs dedicated to electric vehicles. When minimizing emissions in VRPs, models usually take into account that emissions are directly linked to the fuel consumption. Fuel consumption in turn is usually assumed to be highly affected by the travel speed and distance traveled. However, the influencing factors of fuel consumption are manifold and they have recently been reviewed by Demir et al. (2014b). Even though fuel consumption is also an economic goal, there is usually a trade-off with travel time, which can have a severe economic impact not only due to the drivers’ costs. A specific VRP minimizing fuel consumption is the Pollution Routing Problem (PRP), which has been introduced by Bektaş and Laporte (2011) and which has been studied and quickly extended thereafter (Demir et al. (2014a); Koç et al. (2014); Kramer et al. (2015b); Kramer et al. (2015a)). The dissertation by Demir (2012) is worth mentioning in this context. The main idea of this problem is a graph theoretical approach in which the edges are weighted with a function returning travel time and fuel consumption in dependence on the traveled speed on this edge. Besides that, there are other approaches for minimizing emissions in routing. Jabali et al. (2012) determine routes and speeds in order to minimize emissions with respect to time dependent travel times. Qian and Eglese (2014) consider a similar approach with time dependent congestion levels. Time and load dependent emissions are tackled by Ehmke et al. (2016). The fleet composition is the focus of the paper by Kopfer et al. (2014), who consider different vehicle sizes and therefore determine the maximum load but also minimum fuel consumption per distance traveled. The use of electric vehicles is seen as an approach for reducing greenhouse gas emissions. However, operating a fleet of electric vehicles gives rise to several challenges. The two most important are their smaller range and considerably larger time required for recharging compared

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to the refueling of combustion engines. Problem formulations considering these challenges are called Electric Vehicle Routing Problems (EVRP) (see Pelletier et al. (2014) for a review). One of the first such problems was introduced by Erdoğan and Miller-Hooks (2012). This model has been extended so that time windows and vehicle capacity constraints are included (EVRPTW, see Schneider et al. (2014); Desaulniers et al. (2014)), and one paper additionally considers the fleet mix in the EVRPTW (Hiermann et al. (2016)). Juan et al. (2014) consider a problem with a fleet of electric vehicles that have different driving ranges, whereas Goeke and Schneider (2015) consider the routing of a mixed fleet with conventional and electric vehicles. Gacias and Meunier (2015) describe the interesting application of an electric taxi fleet. Chung and Kwon (2015) show how recharging stations can be located gradually over time. Instead of letting electric vehicles wait during recharging, often battery switching stations are implemented (see Avci et al. (2015)). This raises the question of a combined routing and location of such stations. This problem is treated by Mak et al. (2013), Yang and Sun (2015), and Goeke et al. (2015). Let us mention that similar approaches exist for vehicles with an electric engine powered by a hydrogen fuel cell so that hydrogen refueling stations need to be located (Kang and Recker (2015)). 2.3.6 Intermodal Transport

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The different modes of transporting goods differ in their environmental impact (e.g., emissions, sealed surfaces), costs, accessibility, and speed. Transport modes which are considered to be environmental friendly, such as rail or ship/barge, often have the drawback of poor accessibility. Therefore, intermodal transport in which the main haulage is performed in an “environmental friendly” mode can be seen as a way of reducing emissions but for all that guaranteeing a good accessibility even on the “last mile.” Recent surveys of intermodal transport have been presented by Caris et al. (2013) and SteadieSeifi et al. (2014). A typical example is intermodal rail/road transportation in which the prehaulage and endhaulage is performed by truck and the mainhaulage is performed by train (see Boysen et al. (2013)). Kirschstein and Meisel (2015) present models for calculating the greenhouse gas emissions of such transport and Bauer et al. (2010) show how emissions can be reduced. If different modes are available for transportation, the modal choice can be made in terms of socio-environmental considerations. Leal Jr. and D’Agosto (2011) make such an analysis for bio-ethanol in Brazil, which can be transported by pipeline or by trucks. The case of general cargo is analyzed by Gonçalves et al. (2014). Bouchery and Fransoo (2015) consider an intermodal network design problem with respect to costs and carbon emissions. Even though a seaport container terminal is not a standard intermodal terminal, let us mention the work of Geerlings and van Duin (2011). They conduct research on assessing carbon emissions at the Rotterdam container terminal and ways of reducing them. 2.3.7 Shared Transport There are a variety of shared transport modes, such as car sharing, bike sharing, or ridesharing. The idea is to make more intensive use of the vehicles, so that the costs and the environmental impact of these vehicles are reduced. Such a reduction might appear indirectly, for example because shared transport allows for a better accessibility to public transport systems, which might not have been used if an individual had their own car. The literature on car sharing includes the development of such systems (Boyacı et al. (2015)), the relocation

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problem of the cars (Clemente et al. (2013); Weikl and Bogenberger (2013)), and parking reservation policies (Kaspi et al. (2014); Kaspi et al. (2016)). In bike sharing, the relocation problem is mostly in focus (Shu et al. (2013); Hu and Liu (2014); Ho and Szeto (2014); Forma et al. (2015); Erdoğan et al. (2015)). The relocation problems are certainly different for cars and bicycles, as one person can only move one car at a time, whereas bikes are usually relocated using trucks. When looking at ride-sharing, we have to distinguish between the case in which privately owned cars are used (i.e., some of the riders are also the drivers) and the case in which publicly available cars, such as taxis, are used (also called taxi-sharing). For the former, two recent surveys exist (Agatz et al. (2012) and Furuhata et al. (2013)). For the latter, see Li et al. (2014); Hosni et al. (2014); Martinez et al. (2015). An interesting approach is in Ghilas et al. (2013), who discuss the combined transport of passengers and freight. Demand Responsive Transport (DRT) systems can be considered as shared transport, but they are certainly also overlapping with VRPs (dial-a-ride) and city logistics. These systems generalize public transport concepts in such a way that the fixed stops and/or the fixed itineraries are relaxed. An introduction to the topic and an application for the Lisbon metropolitan area is given by Martínez et al. (2015). In a simulation study, Archetti et al. (2016) compare scenarios in which only private cars are available, in which public transportation is additionally available, and in which an on-demand public transportation service is additionally available. They conclude that the on-demand service dominates the conventional public transport and the system’s costs and emissions are decreased. 2.3.8 Humanitarian Logistics

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As a response to natural or anthropogenic disasters, humanitarian aid is certainly an act of high social value. However, due to the adverse conditions for providing humanitarian aid, very specific logistical challenges arise, which are part of the research field of Humanitarian Logistics. Just lately, several review papers have appeared that consider different aspects of humanitarian logistics and disaster operations management (Caunhye et al. (2012); Galindo and Batta (2013); Hoyos et al. (2015); Özdamar and Ertem (2015); Gutjahr and Nolz (2016)). Worth mentioning are also the review paper on wildfire management (Minas et al. (2012)) and the paper on humanitarian logistics network design (Tofighi et al. (2016)). All these papers have in common that they concentrate on optimization models. Even though these papers exhaustively cover the Sustainable Operations aspect of Humanitarian Logistics, let us mention a few papers on this topic. Holguín-Veras et al. (2013) present a mathematical model for obtaining an appropriate objective function in humanitarian logistics models, aiming at welfare economic principles. This paper is a great example on how a given socially motivated goal (deprivation costs, which are defined as the economic valuation of human suffering) is combined with logistical costs in an optimization model. Other approaches deal with transportation problems in disaster response (Berkoune et al. (2012); Talarico et al. (2015); Duque et al. (2016)) or with the location of shelter areas (Kılcı et al. (2015)). Finally, Pedraza-Martinez and Van Wassenhove (2013) consider fleet management for the Red Cross. 2.3.9 City Logistics The efficient and sustainable distribution of material goods in urban agglomerations is a major challenge for transport services providers. In addition to crowded streets and too little loading and unloading areas, access restrictions in city centers, which are either general or

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limited to specific time windows, exist. Time windows can also exist for the supply of clients, for example if the goods receiving department is open during limited hours only. Yet, even the municipalities have, for the reasons discussed above, an interest in a sustainable supply of city centers, which is why, for example, Low-Emission Zones or periods for delivery traffic are introduced. Therefore, a frequently discussed measure is the introduction of a city logistics concept. The initial ideas for city logistics already arose at the beginning of the 1990s and have also been introduced in some cities. Yet, many of these projects have not survived the pilot phase. In recent years, however, new projects for city logistics, whose concepts are mostly different from classical city logistics, have started in numerous cities. In our eyes, it is rather surprising that city logistics have received a rather limited attention in the scientific literature, especially from an Operations Research perspective. For an overview of city logistics, see the book by Taniguchi et al. (2001) or the more recent review article by Anand et al. (2012). Some OR-related chapters can be found in the book by Gonzales-Feliu et al. (2014). Crainic et al. (2009) present models for planning and evaluating city logistics systems. Cattaruzza et al. (2015) address the vehicle routing aspect of city logistics. Finally, Gianessi et al. (2015) consider a location-routing problem in which urban distribution centers (UDC) are to be located and to be connected by a ring in which flows of goods circulate. The UDCs denote transshipment points to and from which goods are moved by electric vans to and from the customers in the city.

2.4 Procurement

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Besides inbound logistics, most tasks of procurement are of a qualitative nature or are concerned with data collection. This comprises market analysis, tenders, solicit bids, and the implementation of contracts. Obviously, these topics do not come under Sustainable Operations even if they consider aspects of sustainability. We will therefore restrict ourselves to supplier selection—a problem for which quantitative models exist (see Chai et al. (2013)).

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Objectives of supplier selection from an economic perspective: As procurement has little influence on revenue, the prevalent economic goal is to minimize costs. Costs that can directly be measured usually consist of unit costs and delivery costs. However, product quality, lead times, warranties, etc. may also influence costs, but cannot be measured as easily.

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Objectives of supplier selection from an environmental perspective: Environmental aspects are related to the products to be purchased and the supplier itself. That means that the environmental impact is usually measured by the environmental impact of the product and the production process. Objectives of supplier selection from a social perspective: Suppliers are often evaluated concerning their social standards. This comprises abstaining from child labor, minimum wages, safety at work, etc.

Obviously, for each sustainable supplier selection model, some data is required on the environmental and social characteristics of the potential suppliers. In this context, sustainable supplier selection is somewhat similar to portfolio optimization. An approach to evaluating suppliers concerning their environmental efforts is proposed by Nielsen et al. (2014). Based on a fuzzy inference system, Amindoust et al. (2012) present a ranking model for supplier selection that integrates the decision maker’s opinion on the relevance of environmental and social

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criteria. Review papers on sustainable supplier selection usually not only treat the supplier selection models but also the problem of supplier evaluation. Here, we mention Igarashi et al. (2013), Appolloni et al. (2014), and Govindan et al. (2015a). Even though the cited review papers cover the literature on sustainable supplier selection, we would like to mention two of the most recent works in this area. Sarkis and Dhavale (2015) present a supplier selection model using a Bayesian framework and Monte Carlo Markov Chain simulation. They state that this approach is still effective even for smaller or missing data sets. A combined supplier selection and order lot-sizing model is proposed by Azadnia et al. (2015). They combine the different dimensions of sustainability in a multi-objective setting and they make use of fuzzy sets.

2.5 Production

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If a company’s production is aimed at economic, environmental, and social sustainability, this commonly comprises new product development, product design, the production process, working conditions, and product recovery or disposal. We have at least partially treated the latter two in the sections before. Many of these issues are of a rather normative, qualitative nature, or deal with quantitative problems, which is more about the collection of data (e.g., for carbon footprints). However, especially the production process (including inventory aspects) and product recovery raise challenges for Sustainable Operations. Here, especially environmental aspects are predominant (since working conditions are either qualitative or have been treated before).

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Objectives of production from an economic perspective: Production itself has little impact on revenue, thus minimization of cost is the primary objective. Significant cost factors are procurement costs, fixed machine costs (which are usually represented by utilization factors), energy demand, etc. Nevertheless, it should be mentioned that in product recovery, some pricing problems arise, which can be considered as revenue maximization problems.

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Objectives of production from an environmental perspective: The production of almost all products has a certain negative influence on the local or global ecosystem. Besides the emissions resulting during the production process, this includes the products themselves, which sooner or later have to be recovered or disposed. We will focus on the emissions during the production process, partially represented by the surrogate goal of energy consumption during production, and on product recovery.

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Objectives of production from a social perspective: A social aspect to be considered in production planning is the availability of certain products for the community. The steady availability, especially of essential goods, is commonly seen as socially important. This objective is suited for being modeled as a satisfactory constraint. Working conditions are a further aspect, which however is rarely related to production planning from an Operations Research point of view. The topics of Sustainable Operations in production are manifold and it is probably hard to cover them in their entirety. This is due the magnitude of the research which is devoted to the (sustainable) production planning of a single product type only. As there are hundreds of products, for each of which the production planning is part of Sustainable Operations, we

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refrain from listing topics which are dedicated to a special product type. Similarly, we leave out some rather technical aspects of energy efficiency, such as energy harvesting, energy recovery, and at least partially green computer processor scheduling, to name just a few. Therefore, our focus will be on concepts dedicated to production in general. For a general, qualitative overview of sustainable manufacturing, see Garetti and Taisch (2012). The engineering point of view of sustainable manufacturing is described by Haapala et al. (2013).

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2.5.1 Production Planning with Respect to Emissions and Energy Efficient Scheduling

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Most papers include ecological aspects into the production planning rather by using the surrogate objective of minimizing energy consumption than by considering emissions directly. If emissions are considered directly, this is often done with some emissions trading scheme in which allowances for emissions must be provided and in which the planner may decide between production modes which differ concerning costs and emissions. For example, in the model of Gong and Zhou (2013), the manufacturer may decide between a conventional and a green production technology, whereas Hong et al. (2012) allow for an arbitrary number of technologies in a similar setting. Just as a side note, we would like to mention that Jaehn and Letmathe (2010) show that an intentional increase of emissions could be a (rational) strategic behavior in an emissions trading market. Various papers have been published on energy efficient (machine) scheduling. Certainly, one could question whether the minimization of energy consumption is an appropriate environmental goal or whether the cost savings and potential rebound effects make one come to the conclusion that it is rather an economic objective. However, very often, other economically motivated objectives, such as makespan or tardiness, are considered besides energy minimization. Thus, we generally consider this topic as being part of Sustainable Operations but we still encourage researchers to include rebound effects into their work (Orea et al. (2015)). A very up-to-date review of this topic can be found in Gahm et al. (2016). Papers on this topic not only cover single machines (Mouzon et al. (2007)) but almost all of the classical machine configurations, such as identical parallel machines (Rager et al. (2015), related parallel machines (Khuller et al. (2010)), unrelated parallel machines (Moon et al. (2013)), (flexible) flow shops (Bruzzone et al. (2012)), job shops (May et al. (2015)), and open shops (Bampis et al. (2012)). In this context, an interesting topic is the speed scaling of machines in order to reduce energy consumption. This topic has mostly attracted theoretical computer scientists, although its practical relevance seems obvious. Originally, the problem consists of scheduling a set of jobs, each of which has a release date, a deadline, and a given workload. The workload is to be processed by some machine, which can perform this workload at an arbitrary speed, but increasing the speed polynomially increases energy consumption (see, e.g., Albers et al. (2014); Bampis et al. (2015)). However, there seems to be a trend to incorporate other traditional objective functions into these models, such as maximum lateness (Bampis et al. (2012)) or flow time (Albers and Fujiwara (2007)). 2.5.2 Lot Sizing and Inventory Management with Respect to Emissions Economic order quantity (EOQ) is a core concept for both lot sizing and inventory management. If emissions are to be considered in these topics, the EOQ model is often used as the basic model, to be extended with an emissions constraint or objective. Even though the

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number of papers is significant, we are not aware of any review paper of EOQ models with respect to emissions. We therefore provide a list of some publications for initial reading on this topic. The models presented mostly focus on either lot sizing or inventory management, although the models can usually be applied to either. Several fundamental models for integrating carbon emissions into lot sizing have been presented by Benjaafar et al. (2013). Absi et al. (2013) consider a lot sizing problem with limited carbon emissions per product. These two fundamental papers have been recognized in the dissertation by Retel Helmrich (2013), who also considers an emission constraint (the corresponding chapter of the book appeared in Retel Helmrich et al. (2015)). The model of Absi et al. (2013) has been extended by Absi et al. (2016). He et al. (2015) discuss lot sizing based on the EOQ model for two emissions regulations (emissions trading and carbon tax). Inventory management based on the EOQ has been extended for emissions in the context of a cap-and-trade emissions market (Hua et al. (2011)) or with various other regulations that do not contain interactions with other companies (Chen et al. (2013); Digiesi et al. (2016)). The paper by Battini et al. (2014) goes further and even aims at considering the environmental impact of transport and disposal. A very interesting generalization of the EOQ integrating various environmental cost factors is proposed by Wensing and Kuhn (2010). There are also some papers that not only integrate ecological aspects, but also social aspects. In their multiobjective approach, Bouchery et al. (2012) use the injury rate as a social criterion and they determine Pareto efficient solutions. In contrast, Arslan and Turkay (2013) extend the EOQ model concerning emissions and working hours. They describe several regulations and how they can be incorporated into the model. Besides the EOQ, there are some other inventory models that are analyzed with respect to emissions, for example the classical newsvendor problem (Arıkan and Jammernegg (2014)). Bozorgi et al. (2014) describe an inventory model for cold items and they compare this model to the EOQ. 2.5.3 Product Recovery

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As we have highlighted the distribution part of reverse logistics in Section 2.3.3, now we consider the remaining topics of product recovery, especially related to inventory management and production planning. The recovery of end-of-life products to return (parts of) the products back to the market is seen as an effective way to reduce waste, and therefore as being environmentally friendly. In addition, economic aspects due to decreased procurement costs have also driven this research field to broad attention. Depending on the degree of dismantling the end-of-life product, we may distinguish product recovery starting from “reuse” (no dismantling), through “repair,” “refurbishing,” and “remanufacturing,” to “recycling” (see Thierry et al. (1995)). A lot of research on product recovery is of a rather qualitative nature. For example, this mostly holds true for sustainable product design, which allows for remanufacturing (see the reviews by Ramani et al. (2010) and Hatcher et al. (2011)). For the quantitative aspects, several review papers can be found (Sasikumar and Kannan (2008a); Ilgin and Gupta (2010); Akçalı and Çetinkaya (2011); Morgan and Gagnon (2013); Govindan et al. (2015b); Ilgin et al. (2015)). Nevertheless, we will give a brief description of the most relevant challenges of Sustainable Operations in this area. An important challenge when recovering products is to answer the question of when the products will be returned, and in what quantity. To answer this question, some forecasting models have been presented in the literature (Clottey et al. (2012); Krapp et al. (2013)).

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The work by Witek (2015) additionally accounts for the quality of the returned items. If the products are collected at specific collection points, the question arises when to pick up the returned products. This problem has been addressed by Ruiz-Benítez et al. (2014) and Zaarour et al. (2014). The case that the number of returns can be influenced by (financial) incentives is analyzed by Mukhopadhyay and Setaputra (2011). Contrary to traditional inventory models, in which replenishment is part of the decision or at least deterministically given, the consideration of product recovery leads to stochastic input to the inventory. This fact has been integrated into inventory models (see for example Alinovi et al. (2012); Yuan et al. (2015)) and it has been extended by further aspects such as different return conditions (Zhou et al. (2011)) or emissions trading (García-Alvarado et al. (2015)). Several approaches have combined the stochastic inventory model with production planning aspects such as lot sizing (Kenné et al. (2012); Pal et al. (2013); Pan et al. (2013); Corum et al. (2014)). On the other hand, there are quite a few approaches to production planning which assume that the return rates are known or that precise forecasts exist (see Steeneck and Sarin (2013) for a review). A seminal game-theoretic approach was presented by Ferrer and Swaminathan (2006). They analyze a multi-period model in which a company may sell new products and returned products in a monopoly setting. Additionally, a duopoly is studied in which a competitor only sells remanufactured products. Further game-theory related papers on the topic can be found in Xiong et al. (2013); Bulmus et al. (2014); Lechner and Reimann (2014); De Giovanni et al. (2016). In addition, production planning with respect to the recovered products mostly focuses on the question of when to produce new products and when to remanufacture products (see, e.g., Shi et al. (2011); Minner and Kiesmüller (2012)). Such models may assume given return rates (Retel Helmrich et al. (2014)), return rates dependent on the price (Corominas et al. (2012)), or return rates dependent on the efforts for stimulating returns (Pishchulov et al. (2014)). A very recent work also considers behavioral aspects concerning the influence of the existence of remanufactured products on the perceived value of new products (Agrawal et al. (2015)). The production planning for disassembling products is a further topic of Sustainable Operations (see Tang et al. (2002) for an early review). Many publications can be found on the disassembly line balancing problem, which differs from the traditional assembly line balancing problem mostly due to its stochastic character. Here, it is often not clear which parts a product consists of, much less the quality of the parts. The problem has been introduced by Gungor and Gupta (1999). Even though we are not aware of a survey on disassembly line balancing problems, they are covered in the line balancing survey of Battaïa and Dolgui (2013). Some more recent works on the disassembly line balancing problem include Özceylan and Paksoy (2013); Bentaha et al. (2013, 2015); Hezer and Kara (2015); Kalaycılar et al. (2016). The classical machine scheduling area has also included disassembly aspects. For example, Cheng et al. (2013) consider a two stage flow shop for dismantling and refurbishing. Abdeljaouad et al. (2015) consider reverse flows (representing disassembly) in a job shop environment. Finally, let us mention the paper by Tian et al. (2012), who evaluate the costs for disassembly. For recovered products, pricing is a challenge at the intersection of Operations Research and marketing. A review of this topic is provided by Steeneck and Sarin (2013). After that review, some works on dynamic pricing have appeared (Chen and Chang (2013); Xiong et al. (2014)). Gönsch (2014) analyzes whether negotiations on the price for used products make sense. Finally, Li et al. (2015) distinguishes between pricing recovered products before remanufacturing and after remanufacturing.

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2.5.4 Further Fields Related to Production

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2.6 Sustainable Operations in Specific Sectors

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In Life Cycle Assessment (LCA), a product’s environmental impact is analyzed “from cradle to grave,” i.e., starting from the product design, through its time in the market, to recovery and or disposal. LCAs have been presented for various products. Even though this topic mostly delivers qualitative approaches or ecological footprints, there are some approaches from an Operations Research perspective. We may refer the reader to the survey by Pahl and Voß (2014). A further application in which the environmental and economic systems seem to have complementary objectives arises in cutting stock problems. Again, one may question whether environmental objectives are sufficiently treated without considering rebound effects. Nevertheless, as two dimensions are concurrently treated, this topic may be considered being part of Sustainable Operations. An introduction to this big research area with many variations is given by Haessler (2013).

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Even though it is impossible to cover all the industries related to Sustainable Operations, we would like to draw the reader’s attention to some of them. We focus on the primary sector and energy industry, which is certainly somewhat arbitrary. However, we identify these areas as key challenges for Sustainable Operations. For the primary sector, this is justified by the fact that it facilitates the satisfaction of the very basic needs of people, especially food. The energy sector is of great importance as it causes high carbon emissions and faces new Operations Research challenges when turning to renewable energy sources. Finally, we consider water resources planning, health care, and OR in developing countries, as each of these topics is intensively discussed in the OR literature and certainly belongs to Sustainable Operations. 2.6.1 The Primary Sector of the Economy

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In this section we focus on the living part of the primary sector, i.e., on agriculture, forestry, and fishery, and we exclude mining. All these areas have been intensively studied also from an Operations Research perspective. The reader may be referred to the review of OR in agriculture and forestry by Weintraub and Romero (2006).

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Objectives of the primary sector from an economic perspective: Financial aspects include both costs and revenue so that profit maximization can be seen as the prevalent economic goal.

Objectives of the primary sector from an environmental perspective: The primary sector heavily influences various environmental systems. The reduction of emissions (especially greenhouse gases, synthetic fertilizers, pesticides, etc.) in the production processes, preserving biodiversity, and adequate water use are most important goals. Objectives of the primary sector from a social perspective: The continuous and long-lasting supply of food probably is the most important social goal, which is usually modeled as a demand-satisfaction constraint. Disputed is the question whether the social perspective should include animals and therefore consider their welfare in livestock production.

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We start our overview of this topic with the paper by Polasky et al. (2008), which is worth mentioning not only for the fact that OR models can be found in a journal on biology. The authors propose a general spatial land management model determining how each parcel is going to be used. The options are various agricultural uses, forestry, rural-residential use, and conservation use. Different objectives are considered that take into account financial returns and biodiversity. The model is applied to a region in Oregon, USA. For a review of mathematical optimization approaches aiming at the conservation of biodiversity, see Billionnet (2013). A most typical application of Sustainable Operations in agriculture is that of cropping systems. These include the cropping plan, i.e., the question of which crops to cultivate and the spatial distribution of the crops. Both questions are highly dynamic, especially as cultivating fields as a monoculture is often not optimal from an economic and environmental point of view. As, in this context, the economic and environmental perspectives are the same, crop rotation planning itself can be seen as part of Sustainable Operations. The reader may be referred to the review on harvesting and processing in agricultural by Kusumastuti et al. (2016) and the review of cropping systems by Dury et al. (2012). However, the latter state that most often the papers reviewed only consider one monetary objective. Therefore, a limit on the use of pesticides and synthetic fertilizers, which have a severe negative impact on the environment, or the cultivation of crops for green manuring, are rarely considered. Nevertheless, there are some crop rotation approaches that explicitly consider these aspects. For example, dos Santos et al. (2010) and dos Santos et al. (2011) both consider a 0–1 optimization problem so that a crop rotation of equal length for each plot in the cropping area is determined. In order to maximize plot occupation, they use column generation. In a further paper by some of these authors (Costa et al. (2014)), it is additionally considered that harvested crops are perishable but still can be stocked for a limited time. Again, column generation is used and applied to instances based on real-world data and complemented by randomly generated data. Fotakis and Sidiropoulos (2014) also consider the cultivation options in the cells of a two dimensional area with respect to water extraction and transport costs. The objective is purely monetary and as an ecological constraint, the groundwater at selected places must be kept at a minimum level. Rădulescu et al. (2014) study crop planning with an approach from portfolio theory, taking into account economic and environmental risks. The mixed integer problem is studied for three objectives: minimizing environmental risks, minimizing financial risks, and maximizing expected returns. Finally, Alfandari et al. (2015) present an in-depth analysis of a multi period crop planning problem modeled as a 0–1 linear program, which was originally presented by Alfandari et al. (2011). They prove the NP-hardness of the problem, show the polynomial time solvability of a subproblem, and present a branch-and-price-and-cut algorithm (BPC), which is tested in a numerical study. A BPC is also used by Santos et al. (2015), who solve a more general mixed integer problem, additionally deciding on the plot sizes. Closely related to cropping systems is forestry management. As mentioned earlier, the concept of sustainability has its origins in the forestry sector. Segura et al. (2014) provide a review of decision support systems for forest management. Forest planning concepts can be divided into spatial planning approaches and non-spatial planning approaches. A review of spatial forest planning is provided by Baskent and Keles (2005). Besides the classical objective of preserving the forest itself, other environmental aspects, such as conserving biodiversity, have been considered in spatial forest planning (Nalle et al. (2004); Orsi et al. (2011); Mönkkönen et al. (2014)). Non-spatial planning approaches in forestry management are rather of

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a tactical or operational nature. Worth mentioning here is the harvest scheduling problem, which determines when to lumber certain parcels of the forest. Constraints on the size of the harvested areas (including previously harvested areas in which trees have yet to regrow) make this problem difficult (see Könnyű and Tóth (2013)). An approach involving consolidating the farmland and woodland of farmers in Bavaria by land-lease agreements is proposed by Borgwardt et al. (2014). Their geometric clustering model leads not only to cost savings, but also reduces the need for pesticides and fosters biodiversity. In livestock production, Operations Research is applied in order to determine that blend of ingredients (diet formulation) such that the livestock receives all nutritions required at minimum costs (see Waugh (1951) for a first application or Peña et al. (2009) and Babić and Perić (2011) for more recent ones). In recent models, formulations include the environmental impact caused by nitrogen and phosphorus excretions (Castrodeza et al. (2005); Dubeau et al. (2011)). Other than this, sustainable aspects in livestock production are rarely tackled with Operations Research. This surprises the author just like the fact that besides economic and environmental goals (e.g., minimization of greenhouse gas emissions by the animals), the animal welfare norms lag behind. From the author’s perspective it seems that the prevailing opinion in public and research supports the contention that animal welfare is part of social sustainability, yet this is almost completely neglected in practice. No matter in which direction a discussion of environmental and ethical questions of intensive livestock farming might lead to (see Ilea (2009)), Sustainable Operations could or even should show how new norms (e.g., on the available space for animals, rest periods during transport, etc.) can be incorporated into livestock production. In fishing, it is an environmental and economic goal to preserve the fishing resources to be harvested. Thus, relevant concepts related to Operations Research can be seen as part of Sustainable Operations. An introduction to the topic is given by Arnason (2009). However, many other animals are affected by fishing, even if they have no economic value (e.g., as being by-catch). In this context, we are not aware of many publications dealing with sustainable fishing. Worth mentioning here is the paper by Chiou et al. (2005). They develop a fuzzy multi-criteria decision making framework for a sustainable industry and they apply it to fishery. 2.6.2 The Energy Sector

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There are several OR problems in the energy sector, which can be found in many review articles (Pohekar and Ramachandran (2004); Banos et al. (2011); Fazlollahi et al. (2012); Wang and Poh (2014)). Among these problems are predictions for energy demand and irradiation, energy storage models, combined heat and power (CHP) approaches, and many others. However, we will focus on optimal power flow, an optimization problem mostly analyzed in the engineering literature which gains relevance due to the Energiewende (German for “energy transition”) in many countries. Power flow optimization determines how much electricity must be produced by the particular power plants in a power network with given consumption vertices and respective demands in order to minimize costs and fulfill availability guarantees. This issue is highly complex due to numerous technical conditions, particularly the non-trivial determination of the power flow, and the dynamics included as the demands change over time. The parameters of this problem include the generators, demand points, and power and voltage limits. Decision variables regulate each generator’s voltage and output, the influence of the power flows using special control systems (the position of the load switches, disconnector and load-break switches, etc.),

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and if necessary the position of the transformer tabs and phase shifter tabs. From the decision variables, the status variables for the voltages of each line and other technical properties such as the voltage angles are indicated directly. It should be mentioned that unlike classical network flow models, energy cannot be sent through the network with arbitrary intensities on each line, as the power flow is completely determined by the resistances of the lines. Optimal power flow pursues economic, environmental and social objectives.

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Objectives of power flow optimization from an economic perspective: Power flow optimization has no direct impact on revenue, thus the primary goal is to reduce the cost of electricity production. Besides the energy generation costs, power losses are to be minimized and often enough, it is aimed at making as few changes to the status variables as possible.

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Objectives of power flow optimization from an environmental perspective: From an environmental perspective, harmful substances arising during power generation (e.g., carbon emissions, nuclear waste) shall be limited to an acceptable level. Objectives of power flow optimization from a social perspective: Since electrical power has no quality differences, continuous power supply is the most important social goal. Therefore, voltage drops and power outages, especially blackouts, are to be avoided.

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The Energiewende is to ensure a sustainable energy supply of electricity by means of a significant increase in the share of renewable energies for energy generation. The corresponding programs and laws have been ratified in several European countries, particularly in Germany. Even though the renunciation of fossil and nuclear power is deemed to be environmentally friendly, this might cause economic and social burdens. Undoubtedly, new challenges for power flow optimization emerge. The currently prevailing thermal power plants can adjust their energy production relatively easily over time, as the related energy carriers, such as coal, oil, natural gas, and uranium, can be stored and converted into energy when needed. For some renewable energy sources (e.g., from pumped storage hydroelectric stations and biogas power plants) this is also the case, yet it is not for solar and wind energy, which are currently indispensable for providing sufficient renewable energy. Besides the fact that these energies are only available at certain times (when the sun shines, or when wind is present), matters are complicated further by the fact that these specific moments in time are hard to predict. It also has to be taken into account that there are fewer possible locations for power plants if restricted to renewable energies. Moreover, volatility makes a permanent power supply based on renewable energies almost impossible unless ways of energy storage can be used. Energy storage, in turn, leads to further challenges for optimal power flow. Obviously, after each power storage, less energy can be reclaimed than has been supplied before. With an efficiency of about 80%, pumpedstorage hydroelectric power stations have one of the highest efficiencies of all storage options suitable for the required dimensions. Disregarding the fact that currently in almost all parts of the world there is a lack of appropriate storage possibilities due to the poor energy density of water (position energy), those storage facilities require hilly ground and can thus only be built in certain regions. Therefore, additional facilities lead to increasing power flows and thereby to an increasing size of the power flow optimization problems. Finally, let us note that compared to thermal power plants, renewable energy is rather produced in a decentralized manner. Instead of a few thermal generators, power flow optimization

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has to deal with a great number of smaller generators in the future, leading to even bigger optimization problems. Even though power flow optimization is a classical problem of Operations Research, it is almost exclusively discussed in the engineering literature. An extensive survey, also including some very few publications in OR-related journals, is provided by Frank et al. (2012a) and Frank et al. (2012b). However, especially for the reasons mentioned above, we believe that this topic should gain more scientific attention. It is thus welcome that recently, some publications on optimal power flow have appeared in OR-related journals (Gouveia and Matos (2009); Phan (2012); Soler et al. (2012); Coffrin and Van Hentenryck (2014)). 2.6.3 Further Sectors

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The intersection of the broad field of water resources planning with Operations Research is part of Sustainable Operations. In water resources planning, economic objectives include the cost-efficient supply of water (possibly of different quality levels) for commercial purposes. Social aspects not only include the vital permanent access to fresh water for everybody, but also water-based recreation and protection against floods. From an environmental point of view, habitat conservation in its broadest sense, water pollution including containment detection and water quality management as well as waste water treatment are important issues. Being aware of the possibility that in the vast amount of papers on water resources planning, many might use OR techniques without referring to Operations Research, we restrict ourselves to mentioning three survey papers (Labadie (2004); Hajkowicz and Collins (2007); D’Ambrosio et al. (2015)) and the multi-attribute utility theory approach by Scholten et al. (2015). In health care, management tasks are irrevocably connected to ethical and social needs. Compared to other service branches, not only customer satisfaction is affected, but even the livelihood of the patients. The application of quantitative methods is very present in health care management. This is due to its resources, which are mostly very expensive, and to the seemingly ever increasing demand for health care services caused by demographic changes and medical advances. The economic goals therefore focus on an efficient usage of these expensive resources, which also include the staff, in order to reduce costs. Besides the social needs of the staff, which we discussed before, the social and ethical aspects of the patients focus on sufficient, timely and equal treatment. Since hospitals are heavy waste producers, waste reduction is an ecological goal of health care management. An access to the topic is given by the survey papers of Rais and Viana (2011), Fakhimi and Probert (2013), and Bhattacharjee and Ray (2014). A special attempt to achieve (global) social sustainability is to use Operations Research in developing countries. After this initiative started in the 1990s, it has become a constant branch of Operations Research. However, its applications are almost as wide as those of Operations Research itself, as this branch is primarily defined by the location of its application. Therefore, papers connected to OR in developing countries can often also be classified using the scheme presented in this paper. However, the problem of food aid distribution in developing countries (Rancourt et al. (2015)) seems to best fit into this category. For a general overview of OR in developing countries, see White et al. (2011).

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3 Conclusions and Outlook

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Methods from Operations Research (OR) and Management Science (MS) are often applied to problems aiming at sustainable development. This is often referred to as Sustainable Operations. However, the field has hardly been defined so far. The key aspects of our definition of Sustainable Operations are its focus on OR and MS, and a definition of sustainability which includes more than purely economic goals, yet excludes normative statements on the sustainability of a particular system. Even with these restrictions, Sustainable Operations is still a huge field of research, especially if the peculiarities for specific branches of industry are taken into account. Besides some exceptions, we refrained from considering specific industries. The proposed taxonomy of Sustainable Operations based on the functional structure of an enterprise and the classification of systems to be analyzed is summarized in Table 1. Obviously, in both dimensions (functional structure and systems), more detailed descriptions are possible. We especially propose that papers treating sustainability issues should clearly mention the systems aimed at being sustainable. In each of the topics presented, future research is required, as a closer look at the relevant literature shows. However, we would like to only highlight future research directions which we consider to be relevant for Sustainable Operations in general. Firstly, in our eyes, environmental impact can only be evaluated correctly if rebound effects are taken into account. This holds true especially for transport logistics and production. The simplest approach would be to incorporate into existing objective functions that fuel or energy reductions do not linearly reduce emissions. Advanced models could integrate market demands in order to represent rebound effects. Certainly, a link to game theoretic approaches seems to be a promising approach for understanding rebound effects in logistics and production. Even though not being part of Sustainable Operations, empirical work on rebound effects is desperately needed, especially in logistics. Secondly, the concepts of city logistics possess great potential for Sustainable Operations, especially as such concepts are more present in practice than in research. Shared transport and modal changes are simple concepts which could be used to guarantee quick transport for people and goods in a dense area. Here, visionary transport solutions such as on-demand public transport or even ones requiring a different infrastructure than the existing one might be of great value in future years. A reduction of the (visible) space required for transport could be a further social objective that has not been considered yet. In production, we believe that social aspects should be integrated to a higher degree within production planning problems. First attempts of considering working hours or injury rates might be of less importance in industrialized countries with high standards for working conditions and safety aspects. However, the appropriate models could deliver a better understanding of such concepts in general and might find their application in countries with lower standards, especially if the appropriate concepts are enforced in a supply chain by the focal firm. Finally, let us mention once more the challenges raised by the Energiewende. The current supply of (electric) energy by thermal power plants leaves unresolved environmental problems such as carbon emissions and nuclear waste. The turn to renewable energies can be supported by Sustainable Operations. Here, optimal power flow plays an important role, but it is not the only challenge from a Sustainable Operations point of view.

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x x

Crop Rotation Planning Forestry Management Livestock Production Fishing Power Flow Optimization

Energy Sector

x x x x x x x x x x

x x x x

x

unspecified x x x x x x x x x x x x x x x

x x

x x unspecified x x x x x x

x

Families

Staff

Waterbodies

Atmosphere

Raw mat. sources

Regional Ecosystem

NGO x

x

x x

x

People in general

x x x x

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Exemplary sectors

Procurement Production

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Functional classification

Finance Logistics

x x x x x x x x x

Social Systems Inhab. of resid. area

Loc. Plan. of Public Utilities Waste Location Workforce Scheduling Socially Responsible Investment Closed Loop Supply Chains Reverse Logistics Waste Management Green VRP Intermodal Transport Shared Transport Humanitarian Logistics City Logistics Supplier Selection Prod. Plan. and emissions Lot Sizing / Inv. management Product Recovery

Ecologic Systems

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Management

Publ. institution

Company

Economic Systems

x x

x

x

x

x x unspecified

x x x x x unspecified x

x x x x

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Table 1: Fields of Sustainable Operations and (classes of) systems being considered

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