Development of an integrated decision-making method for an oil refinery restructuring in Brazil

Energy 111 (2016) 197e210

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Energy journal homepage: www.elsevier.com/locate/energy

Development of an integrated decision-making method for an oil refinery restructuring in Brazil Alberto Pavlick Caetani a, Luciano Ferreira b, Denis Borenstein c, * a

Petrobras, Brazil School of Science and Technology, UFRN, Natal, Brazil c Management School, UFRGS, Porto Alegre, Brazil and Prometeo Researcher, Universidad de Cuenca, Cuenca, Azuay, Ecuador b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 April 2015 Received in revised form 21 May 2016 Accepted 24 May 2016

This paper describes an integrated decision analysis method to support the managing board to define a restructuring plan for a small oil refinery in south Brazil. The refinery is facing an extreme crisis situation due to the obsolescence of its production plant. The decision approach proposed in this paper consists of four phases. Initially, potentially performing restructuring alternatives were identified, as well a set of criteria covering the dimensions of corporate sustainability. Based on the relative importance evaluation of each criteria given by a group of decision-makers, and on the performance of the alternatives in each of the criteria, a fuzzy decision-making method, called FETOPIS, was applied for ranking the alternatives. The information resulting from this analysis, along with economic data, was used in a portfolio optimization model to identify restructuring investment projects (RIPs), a combination of feasible restructuring alternatives. FETOPSIS was used again to generate overall performance scores of each RIP, aggregating the individual performances of the constituent alternatives. The application of the method in the refinery demonstrated the efficiency and efficacy of the proposed approach to facilitate the understanding and exploitation of the problem, considering simultaneously several distinct and conflicting dimensions. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Restructuring Oil refinery Fuzzy TOPSIS ELECTRE Portfolio optimization

1. Introduction Extreme crisis situations in industries, in which both the core business and the assets are under threat of becoming obsolete, require the implementation of a radical change process and productive restructuring [1]. Falling or threatened sectors must seek, as an alternative for restructuring, the alteration of their market niches, the ennoblement of their production staff, and the incorporation of more elevated levels of technological density. The Riograndense Oil Refinery (RPR, from the Portuguese “Refinaria de
leo Riograndense”), a small oil refinery located in the southPetro ernmost state of Brazil, Rio Grande do Sul, is facing such a crisis context. Its industrial complex is very old, with only small improvements in more than 75 years since its inauguration, making it not competitive in the near future. Once this situation was identified, the managing board of the company has started a restructuring process in which the company will need to implement a deep

* Corresponding author. E-mail address: [email protected] (D. Borenstein). http://dx.doi.org/10.1016/j.energy.2016.05.084 0360-5442/© 2016 Elsevier Ltd. All rights reserved.

transformation that will, at the same time, optimize the utilization of its assets and will make possible the overcoming of its technological and business limitations. These productive transformations will be large, thus demanding high investments that may become viable through the capital market, equity changes (acquisitions and mergers), or by using financing programs from governmental development agencies. An industrial restructuring strategy must identify feasible business lines, ensuring the consistency of the required investments with the company's global strategy. Differently from a new enterprise, the restructuring is characterized by the search for a better use of an existent industrial asset through the modernization, expansion, or even deactivation of production lines, as well as the implementation of industrial plants, generating a new business model. In modern economic systems, it is no longer acceptable that such problems are analyzed through the simple economic logic, thus requiring a wider and more integrated evaluation that includes social and environmental aspects [2]. As pointed out by Ref. [3], strategies are implemented through projects. The strategy selection problem can be formulated as a portfolio selection, defined by Ref. [4] as the selection of a group of

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alternatives, from a wider set of possibilities, taking into account not only the characteristics of each alternative, as well as their interactions and consequently their synergies (positive and negative). In the case of industrial strategies, the potentially applicable alternatives require the resource coordination and execution of different and interrelated projects. While a relevant part of the strategic decisions, the selection and prioritization of projects is a complex process, characterized by multiple and conflicting objectives, normally of difficult measurements. Moreover, the decisionmaking process is characterized by uncertainties arising from incomplete and imprecise information, budget limitations, technical and strategical conditions, and interdependencies between projects [5]. Additionally, many governmental development agencies and external investors demand detailed studies, emphasizing sustainability requirements, which end up representing an expressive challenge for managers. Decisions related to large restructuring investments therefore demand the incorporation of political, social and environmental aspects, justifying the application of multiple-criteria analysis tools. Investment projects selection is a classic multiple-attribute decision-making (MADM) problem [6]. MADM is a sub-discipline of the multiple-criteria decision-making (MCDM) class [7], consisting of developing models that support the following three problems: (i) to identify the best alternative; (ii) to rank the alternatives from the best to the worst; and (iii) to classify, sort, or discriminate the alternatives into predefined homogeneous groups. In situations where decision-makers (DMs) face many problems with incomplete, unqualifiable, vague, and unquantifiable information, fuzzy set theory (FST) was introduced on MADM [8]. Zimmermann [9] defines FST as a very powerful modeling language that deals with a large fraction of uncertainties of real-life situations, since much knowledge in the real world is fuzzy rather than precise. Several MADM methods and techniques, including hybrid approaches, were employed to help DMs to analyze a set of alternative
s-Beltr
investment projects in the energy sector. Aragone a et al. [10,11] applied AHP (Analytic Hierarchy Process)/ANP (Analytic Network Process) for the selection of photovoltaic solar thermal plant projects. Kahraman et al. [12] applied Fuzzy ANP to select renewable energy alternatives. Kaya and Kahraman [13] integrated VIKOR (abbreviation for the Serbian translation of Multicriteria Optimization and Compromise Solution) and AHP for a similar problem. Amiri [14] used an AHP/Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to select oil-fields development projects, while Choudhary and Shankar [15] employed a similar integrated method to thermal power plant location. We refer to Hwang and Yooon [7] and Ribeiro [16] for good reviews of MADM and fuzzy-MADM, respectively. However, MADM techniques and methods have some shortcomings. Greiner and et al. [6] have identified that MADM techniques are not adequate to solve problems that involve the optimization of resources and interdependency between projects, requiring the application of complementary analysis techniques. MADM techniques are capable of determining priority measures for each of the investment projects under consideration. They are not, however, capable of determining the optimal mix of those development projects in light of a set of resource constraints or other constraints (i.e., strategic constraints and/or mandated constraints) [17]. These constraints usually impose a combinatorial nature to the problem. One of the most frequently used approaches found in the related literature to address these situations are characterized by a two-stage process [6]. In the first stage, the relative benefits of each project are calculated by determining an individual score, which enables the ordering of alternatives through the application of methods of multiple-criteria analysis, such as AHP and TOPSIS. In the second stage, a mathematical model is built to optimize the

overall value of the portfolio using individual scores calculated in the previous stage, including restrictions such as the factors related to interdependencies between projects [18]. The portfolio optimization is in general implemented as a “knapsack problem”, a classical 0e1 linear programming problem in which the goal is to maximize the benefits provided by each project under technical and budget constraints [6]. Some authors proposed the application of a two-stage process in portfolio optimization models. Kearns [19] used scores obtained through AHP in the objective function of an integer linear programming model (ILPM) in order to select an optimum combination of information systems. Mavrotas et al. [20] conducted a study for the selection of candidate companies to public financing in Greece, applying PROMETHEE V to obtain a global measure of performance, and an ILPM to consider budget constraints and implementation of public policies such as regional distribution. These research studies indicate that a two-stage process, combining MADM methods and portfolio optimization can offer the appropriate treatment for technical, budget, and interdependence constraints between alternatives. The main objective of this paper is to describe the integrated decision analysis method developed to support the managing board of RPR to evaluate possible restructuring project investments. This research proposes a hybrid approach, integrating a fuzzy ELECTRE/TOPSIS (FETOPSIS) method with a 0e1 integer portfolio optimization model. On one hand, the FETOPSIS component has allowed the managing board to develop a stakeholder relationship by establishing evaluation criteria and deriving criteria weights. On the other hand, the optimization model has allowed the managing board to analyze the interdependencies among the investment projects, considering technical and budget constraints. The proposed strategic restructuring of the decision-making process consisted of four phases. In the first phase, the problem was structured by the managing board, in terms of acceptable business lines (called as “restructuring alternatives”), criteria and criteria weights. In the second phase, the alternatives were ranked by FETOPSIS, evaluating the alternative projects based on the established criteria. In the third phase, the results of FETOPSIS were integrated with the portfolio optimization model, allowing the managing board to evaluate the synergy among the alternatives given a set of constrained resources. At this phase, the restructuring alternatives were aggregated or combined into restructuring investment projects (RIPs), characterizing the company portfolio. In the last phase, the RIPs were prioritized based on the individual evaluation of each alternative carried out in the first phase, reapplying FETOPSIS. In this paper, we make the following contributions: (i) We develop a hybrid decision-making method to select a restructuring plan for an energy based company, not only based on various economic, social, and environmental criteria, but also taking into consideration the synergy between the possible investment projects within the plan; (ii) We discuss how companies might use Global Reporting Initiative (GRI) practices, a leading organization in the sustainability field, to define criteria to select investment projects on a fuzzy approach; (iii) We propose an innovative method that combines elements of Fuzzy-ELECTRE and Fuzzy-TOPSIS, taking advantage of the best characteristics each method has to offer; and (iv) We include in the method a systematic way to overcome the well-known bias towards low cost project which is generally caused by portfolio optimization techniques. To our knowledge, this paper pioneers the simultaneous consideration of all these aspects for industrial restructuring in the petroleum industry. Further, systematic searches, using the following terms: “industrial restructuring”, “investment”, “project”, “petroleum industry” or “sustainability”, conducted in the ISI Web of Knowledge database

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during 2015 showed no similar research to this study. The remainder of this paper is organized as follows. Section 2 characterizes the problem, describing the context in which the decision-making process has occurred. Section 3 describes in details the methodological aspects of each phase of the developed restructuring decision-making process, introducing FETOPSIS. The application of the method to the restructuring decision-making process of the RPR is described on Section 4. Finally, Section 5 presents some final remarks. 2. Application context RPR was founded in 1937 under the name of Ipiranga Oil Refinery (IOR), in Rio Grande, a medium sized city in the state of Rio Grande do Sul; it was the first oil refinery in Brazil. In 1953, a federal law established the state monopoly of oil production and refining. While allowing the continuation of the operations of private refineries, this law prohibited increases in production capacity, so that, by 1997, IOR operated with the same production capacity that it did in the 1950s. After the loosening of the state monopoly that same year, OIR had increased its production capacity by around 40%, from processing 12,500 barrels/day in 1997 to 17,000 barrels/ day in 2002. In 2004, rising oil prices and the non-monitoring of fuel prices in the domestic market made the activities of private refining in Brazil impossible, culminating in temporary shutdowns during 2005 and 2006. In 2006, IOR signed a protocol with the Brazilian government, which ensured tax advantages for the production of petrochemical naphtha, allowing the refinery to resume its activities. In 2007, a consortium of petrochemical companies, including the state oil company Petrobras, and two prominent Brazilian groups in the energy sector, Ultrapar and Braskem, took control of IOR, changing its name to RPR. As an alternative to prohibitive conditions resulting from high oil costs, RPR signed a refining service contract with Petrobras, in which RPR only processes the oil from this controller. This agreement allowed the continuity of the refinery operations and enabled Petrobras to increase its production of oil products to meet the domestic market. RPR has three main production units, an atmospheric distillation unit, a vacuum distillation unit, and a fluidized bed catalytic cracking unit, with daily capacities of 2700 m3/day, 795 m3/day, and 540 m3/day, respectively, and Nelson complexity indexes of 1.0, 0.9, and 1.4, respectively. The Nelson refinery complexity index is 3.3 [21]. In 2012, RPR completed the installation of a new solvents unit that transformed the refinery in the only national manufacturer of pentane, an important raw material for the chemical and petrochemical industry. In 2014, the RPR was responsible for the average refining of 13,850 barrels/day of light crude oil imported from Saudi Arabia and Angola, with API gravities of 33
API and 31
API, respectively. The refinery has the following portfolio of products: gasoline, petrodiesel, naphta, asphalt, LPG, and special solvents. A strong feature of the company is its production flexibility, a peculiarity of small refineries, which aims to develop products according to the needs of each client, such as special solvents, and highstandard gasolines produced with customized formulations to meet the various requirements and needs of vehicle manufacturers. Nowadays, the production capacity of the refinery is just over 1% of the total volume processed in the country, mainly supplying the demand of local consumers. Although RPR has shown positive economic results in the last four years, its medium-term prospects are not promising, since its facilities are incompatible with the new specifications of fuels that became effective in Brazil since 2014. Thus, RPR will very soon lose its internal market supply function, seriously compromising the continuity of the refinery operations and, consequently, causing severe impacts on the region's economy. The configuration of a

199

crisis scenario motivated the board of RPR to begin a review process of its business strategy. The discussion has necessarily involved the evaluation of industrial restructuring alternatives, composed of new business strategies and their respective technologies that ensure the company's sustainability in the long term. Several workshops, coordinated by the Board of RPR and facilitated by external consultants, were carried out to raise possible restructuring alternatives. The strategic review was consolidated in a SWOT matrix [22] constructed for each possible business line and compatible with the potentialities identified in the refinery. The SWOT analysis, which analyzes a project's Strengths, Weaknesses, Opportunities, and Threats, is the basic input for the restructuring decision-making process. The unarguable regional importance of RPR, both in the economic and socio-cultural aspects, expands the analysis of its restructuring beyond simple economic logic. In addition to 400 direct jobs, RPR generates about 1200 indirect jobs in the region; it is also responsible for a significant portion of the tax revenues of the city of Rio Grande. Such decisions involve a high degree of complexity and its effectiveness requires detailed studies at the strategic level. 3. Method This section presents how the decision-making process was structured, describing the methodological procedures employed. The process was divided into four interconnected phases (see Fig. 1), reducing the complexity of the entire problem by solving smaller subproblems one at a time. The first phase comprises the definition and scope of the problem, the second phase uses a fuzzy

Fig. 1. Four-stage integrated decision analysis method for the refinery restructuring process.

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MADM called FETOPSIS to rank the restructuring alternatives according to the DMs' preferences, while the third phase uses a 0e1 portfolio optimization model to compose a set of restructuring investment projects, using as input the FETOPSIS0 closeness coefficient computed in the second phase and the scenarios/constraints defined by the DMs. Lastly, the scores of each generated RIP are aggregated to be ranked appropriately, again using FETOPSIS. As the method deals with a wide spectrum of factors characterized by vagueness and uncertainties, its successful application highly depends on an active cooperative teamwork between the DMs and the analysis and evaluation method. Keeping the DM actively engaged during the entire decision-making process is the major foundation of our method, with which all other activities interact. DMs must define the decision hierarchy, define a set of independent criteria, derive criteria weights, evaluate projects against the criteria, and define projects' synergy and constraints. While the DMs assigns some problems to solve, the method takes on the role of advisor and facilitator. Thus, during the decision process the method should stimulate, support, and suggest actions in order to help the DMs to better understand the problem. Further, the ability to conduct sensitivity analysis is designed into the method and provides the DMs with additional insight regarding the robustness of the defined restructuring projects. Therefore, the correct use of the method will call for a multidepartamental group within a company with ability to translate strategic objectives in terms of production, financial, social, and ecological capabilities. These capabilities define the evaluation criteria for an analysis and its correct measurement is a key factor for a strategic-based evaluation. For the application of the methodology it is assumed that: (i) the DMs are willing to input the extra effort required to use a non routine method to analyze and evaluate a strategic decisionmaking problem; and (ii) the company has a defined corporate strategy for the future that will guide the decision-making process. Before describing the method, we first introduce some definitions and notation. 3.1. Preliminaries This section briefly reviews the required main concepts to understand the notation and the mathematical operations further used in the method. ~ defined in ℝ is denoted Definition 1. (Zimmermann [9]) A fuzzy set A
~ ¼ fðx; m ~ ðxÞÞ

x2ℝg, where m ~ is the membership function used to as A A A ~ is associate each element x to a real number in the interval [0,1]. A normal if there is at least one point x in ℝ with mA~ ¼ 1 and is convex if mA~ ðlx þ ð1  lÞyÞ  minfmA~ ðxÞ; mA~ ðyÞg for any x; y2ℝ and l2[0,1]. Definition 2. (Zimmermann [9]) A triangular fuzzy number (TFN) ~ ¼ ða ; a ; a Þ is a fuzzy number represented with three points. Its A 1 2 3 membership function mA~ ðxÞ can be defined as follows:

mA~ ðxÞ ¼

8 > 0; > > > > > x  a1 > > >
1

x  a3 > > > ; > > a > 3  a2 > > > : 0;

x < a1 a1  x  a2 a2  x  a3 x > a3

Definition 3. (Chen et al. [23]) The fuzzy sum 4 and fuzzy subtraction . of any two TFNs are also TFNs by the extension principle [24]. However, the multiplication of any two TFNs 5 is only an ~ ¼ ða1 ; a2 ; a3 Þ and approximate TFN. Given two positive TFNs, a

~ ¼ ðb ; b ; b Þ, and a non-fuzzy number r  0, where 0  a  a  a b 1 2 3 1 2 3 and 0  b1  b2  b3, the fuzzy operations of sum, subtraction, multiplication and multiplication by a scalar can be expressed by, respectively:

~ ¼ ða þ b ; a þ b ; a þ b Þ ~4b a 1 1 2 2 3 3 ~ ¼ ða  b ; a  b ; a  b Þ ~.b a 1 3 2 2 3 1 ~ ~5bzða a 1  b1 ; a2  b2 ; a3  b3 Þ ~5rzða1  r; a2  r; a3  rÞ a

Definition 4. (Zadeh [25]) Fuzzy numbers may be associated with linguistic variables. A linguistic variable is characterized by a ~ U; MÞ, where X is the name of the variable, T~ is the set quadruple ðX; T; of linguistic terms of X, U is the universe of discourse, and M is the ~ The pertinence of any x2U membership function for each element of T. in relation to the membership functions of M is computed according to Definition (1). ~ between two TFNs a ~; bÞ ~¼ Definition 5. (Chen [26]) The distance dða ~ ða1 ; a2 ; a3 Þ and b ¼ ðb1 ; b2 ; b3 Þ is a fundamental measure to rank two or more alternatives, and is computed as follows:

  ~ ¼ ~; b d a

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi i 1h ða1  b1 Þ2 þ ða2  b2 Þ2 þ ða3  b3 Þ2 3

~ and A ~ 2~ For any pair of fuzzy number A where i j S, ~ ; /A ~ n g, the fuzzy number ranking has the following ~ ;A ~ S ¼ fA 1 2 properties ([27]): ~ ; OÞ; ~ A ~; ~ < dðA ~ then A  if dðA i j i j ~ ; OÞ ~ ; OÞ; ~ ¼A ~; ~ < dðA ~ then A  if dðA i j i j ~ ¼ ð0; 0; 0Þ is the origin. where O ~ is a fuzzy matrix if, at least, one Definition 6. (Chen [26]) A matrix M ~ ¼ ½m ~ ij mn , fully of its elements is a fuzzy number. A fuzzy matrix M composed of TFNs, can be normalized to interval [0,1] as follows:

~ ij ¼ m



 

 

 !  m þ
d
mij2 þ
d
mij3 þ
d

ij1
 ; 

 ; 


d
þ dþ
d
þ dþ
d 
þ d þ

~ ij , where mij1, mij2, and mij3 represent the three points of the TFN m dþ is the maximum value of mij3 and jdj is the absolute value of the minimum value of mij1. In order to normalize no positive TFNs, d is a necessary parameter. When the matrix is fully composed by positive TFNs, d is assigned to zero. 3.2. Phase 1 e problem structuring This first phase is responsible for structuring the problem, and identifying objectives, alternatives, and eliciting preferences. A committee, composed of company directors and experts, was formed to analyze the problem. There are several models to identify and analyze elements of strategy. A strategy-generation table [28] is a suitable tool to treat, in a simple manner, the combinatorial analysis of the multiple aspects related to each identified strategy. This tool was used to formulate the restructuring alternatives, using the strategies defined in the SWOT analysis.

A.P. Caetani et al. / Energy 111 (2016) 197e210

The set of criteria was defined according to three dimensions of corporate sustainability: economic, social, and environmental [2], aiming to represent the following view of corporate sustainability and sustainable development: (1) add value to controller companies; (2) contribute to regional development; and (3), act with environmental responsibility. Lastly, data were collected through interviews, questionnaires, and focus groups with DMs and experts, formalizing the decision-making model in terms of criteria hierarchy and their corresponding weights; and their corresponding scores under each criterion.

3.3. Phase 2 e evaluation of restructuring alternatives The industrial restructuring problem is essentially an MADM process, as for any strategic decision-making process. Several MADM approaches have been employed in the energy sector to cope with such decisions (see section 1). As pointed out by Ref. [10], there is no optimal model. Based on an analysis of the relevant literature about MADM [7], we initially applied fuzzy TOPSIS as presented by Refs. [26] and [29]. However, Fuzzy-TOPSIS assigned narrow gaps between the scores of two or more alternatives. In the MADM field, the lack of discrimination of alternatives is a consequence both of biased rankings due to relative values or ratio scale used in the evaluation and/or the operation of normalized composite values, between zero and one [30]. As pointed out by Ref. [31], this is a common occurrence in choice and ranking problems, and may lead to ineffective decision-making processes or inappropriate decisions, as DMs always wish to know which option is the best ranked one. We decided to enhance the discrimination power of fuzzy TOPSIS by integrating some aspects of the fuzzy ELECTRE method, capturing the advantages of each method towards the development of a more effective MADM method. ELECTRE is an MADM that uses outranking relations with the purpose of finding a set of alternatives dominating over other alternatives, while they cannot be dominated [32]. On the other hand, fuzzy TOPSIS is a compensatory approach that allows trade-offs between criteria. Outranking methods take ordinal scales into account without converting the original scales into abstract ones, minimizing the phenomenon of lack of discrimination of alternatives observed in compensatory methods [33]. Our approach considers that the non-compensatory effects of the concordance and discordance indexes as introduced by Ref. [34] in the context of fuzzy ELECTRE, coupled with the positive and negative ideal solution offered by TOPSIS, can improve the decision-making process analysis, offering a better set of alternative scores for DM's analysis and evaluation. What follows it is a brief description of the developed method. Supposing that the decision problem involves m alternatives evaluated on n criteria, the judgment matrix of decision maker k ¼ 1,2,…,K can be written as follows:

2

~ x11k 6~ ~ ¼ 6 x21k D k 4 « ~ xm1k

~ x12k ~ x22k « ~ xm2k

/ / 1 /

3

~ x1nk ~ x2nk 7 7 « 5 ~ xmnK mn

where ~ xijk ¼ ðaijk ; bijk ; cijk Þ is the performance of alternative i on criteria j by the DM k, aijk, bijk, and cijk are the three points that characterize a TFN (see Definition 2). The importance criteria weight of the kth decision-maker can be represented as follows:

~ ¼ ½w ~ 1k W k

~ 2k w

/

~ nk n w

Based on this notation, FETOPSIS can be outlined as follows.

201

~  ~ ¼ ½d Step 1. Construct the aggregated matrix D ij mn from ~ ¼ ða ; b ; c Þ is a TFN (see Definition 2) ~ ¼ ½~ D x  , where d ij ij ij ij k ijk mn computed as follows:

n o aij ¼ min aijk ;

bij ¼

k

K 1 X bijk ; K

n o cij ¼ max cijk k

k¼1

~ ¼ ½w ~ j n, where Step 2. Construct the aggregated vector W ~ ¼ ½w ~ j ¼ ðwj1 ; wj2 ; wj3 Þ, obtained from W ~ w  , where each k jk n ~ jk ¼ ðwjk1 ; wjk2 ; wjk3 Þ is a TFN (see Definition 2). w ~ j is w computed as follows:

n o wj1 ¼ min wjk1 ; k

wj2 ¼

K 1 X wjk2 ; K

n o wj3 ¼ max wjk3 k

k¼1

Step 3. Using elements from Fuzzy ELECTRE [33], compute the fuzzy concordance index ð~cða;bÞ Þ and fuzzy discordance index ~ ðd ða;bÞ Þ for all pair of alternatives (a,b)jasb as follows:

~cða;bÞ ¼

X

~ j ; if asb w

j2J þ



~ ~ where, J þ ¼ fj
d ða;jÞ  dðb;jÞ g is the set that contains the index of all criteria in favor of the assertion ”a is at least as good b.” The fuzzy discordance index can be calculated as follows:

~ d ða;bÞ

  8 ~ ~ > maxj2J  d > ðb;jÞ .dða;jÞ > <  ; ~ ~ ¼ d d ; d max j2J ða;jÞ ðb;jÞ > > > : 0;

if

j2J 

otherwise



~ ~ where, J  ¼ fj
d ða;jÞ < dðb;jÞ g is the set that contains the index of all criteria against the assertion “a is at least as good b”. Step 4. Compute the fuzzy pure concordance index and the fuzzy pure discordance index of each alternative based on [34] as follows:

~a ¼ J

m X

~cða;bÞ .

b¼1

m X

~cðb;aÞ ; ca ¼ 1; 2; …; m; asb

b¼1

P where refers to the fuzzy numbers summation operation (see ~ a ¼ ðj ; j ; j Þ. Definition 3), and J a1 a2 a3

~a ¼ U

m X b¼1

~ d ða;bÞ .

m X

~ d ðb;aÞ ; ca ¼ 1; 2; …; m; asb

b¼1

~ a ¼ ðu ; u ; u Þ. where, U a1 a2 a3 ~ a and U ~ a indexes to interval [0e1], following Step 5. Normalize J Definition 6. ~ , based on Step 6. Calculate the fuzzy positive ideal solution A



f f ~ Ja and Ua , as follows: A ¼ f~vc ; ~vd g, where ~vc ¼ ðv c1 ; v c2 ; v c3 Þ, ~v d ¼ ðv d1 ; v d2 ; v d3 Þ, v c1 ¼ v c2 ¼ v c3 ¼ maxa fja3 g, and v d1 ¼ v d2 ¼ v d3 ¼ mina fua1 g. ~  , based on Step7. Calculate the fuzzy negative ideal solution A       f f ~ ~ ~ Ja and Ua , as follows: A ¼ f~vc ; vd g, where vc ¼ ðv c1 ; vc2 ; vc3 Þ,   ¼ v ¼ min fj g,  ; v ; v Þ, ~ v ¼ v ¼ ðv and v a a1 d c1 c2 c3 d1 d2 d3 v ¼ v ¼ v ¼ maxa fua3 g. d1 d2 d3 Step 8. Calculate the distance of each alternative ðS a Þ in relation to the positive ideal solution as follows:

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~ a ; ~v Þ þ dðU ~ a ; ~v Þ, where dðJ ~ a ; ~v Þ and dðU ~ a ; ~v Þ are S a ¼ dðJ c c d d computed as in Definition 5. Step 9. Calculate the distance of each alternative ðS a Þ in relation to the negative ideal solution as follows: ~ a ; ~v Þ þ dðU ~ a ; ~v Þ, where dðJ ~ a ; ~v Þ and dðU ~ a ; ~v Þ are S ¼ d ð J a c c d d computed as in Definition 5. Step 10. Calculate the closeness coefficient (CCa) of each alternative as follows:

CCa ¼

S a

S a þ S a

a1 X

xi 2f0; 1g i ¼ 1; …; a  1 where xi is the binary decision variable to each project, indicating if the project is selected (xi ¼ 1) or not (xi ¼ 0), asi is the augmented score of project i, ci is the cost of project i, ca is the investment budget, and za is the highest score that can be attained by projects that are worse than a and have cumulative cost lower than ca. Step 3: If (za < asa1) then set asa ) asa1þ1. Otherwise, asa ) zaþ1. If a s m go to Step 2, otherwise STOP.

Step 11. Rank the alternatives in the descending order of their respective CCa . The highest CCa value indicates the best performance in relation to the evaluation criteria.

3.4. Phase 3 - definition of the RIPs The main objective of this phase is to evaluate the restructuring alternatives, taking into account both the scores obtained in the previous phase and the synergies among them; the objective is to define a set of possible restructuring investment projects. Each RIP will be formed by a set of restructuring alternatives, respecting technical, profitability, and budgetary constraints. Although not infrequently found in the literature, the selection approach and portfolio optimization based on maximizing the overall performance of a set of projects obtained by MADM techniques has characteristics that may lead to inconsistent judgments, as discussed in Section 1. In these models, the prioritizing of projects is only dependent on the cardinal scores resulting from the application of MADM methods at the expense of project relationships [20]. The resulting portfolios end up showing preference bias for low-cost projects, since in the classical knapsack problem, given capacity constraints, two objects of lower value and lower cost are preferable to a higher value and higher cost object. This is an undesirable situation in our problem context. To overcome these shortcomings without adding too much complexity to the problem, the method described in Ref. [20] was used. The method consists of a new approach for the selection of projects in order to maximize the compatibility of previously ranked projects with technical, temporal, and economical constraints, eliminating selection bias of lower cost projects. Unlike other approaches that simply aim to maximize the overall performance of a combination of projects, these authors propose the use of augmented scores (as). This score is computed in a way that given a project a at position z, there is no combination of projects (in a lower position than z and inferior costs to project a) whose augmented scores are greater than asa, the augmented score of project a. The method can be succinctly described as follows: Step 1: Ranking of a ¼ 1,…,m projects according to their multiattribute score (CCa) in increasing order. For the worst project, assign as1 ) 1. In our case, the number of alternatives m is equal to the number of projects. Step 2: For the a-th project (a ¼ 2,…,m), solve the following knapsack problem:

maximize za ¼

a1 X i¼1

s:t:

asi xi

ci xi  ca

i¼1

Based on the augmented score of each project i (asi), it is possible to optimize the set of restructuring projects as a 0e1 linear programming model. The constraints and project's synergy are transformed into linear inequalities within the mathematical formulation. Examples of synergy between projects are as follows: “projects i and j are mutually exclusive” or “if project i is selected then project j must be also selected, characterizing a dependency relationship”. We use binary relation symbols me and dep to determine whether two projects characterize mutually exclusive and dependent pair of projects, respectively. If me(i,j) ¼ 1, projects i and j constitute a mutually exclusive pair of projects, me(i,j) ¼ 0 otherwise. Analogously, if dep(i,j) ¼ 1, projects i and j constitute a dependent pair of projects, dep(i,j) ¼ 0 otherwise. Introducing xi as the binary decision variable that denotes the selection (xi ¼ 1) or not (xi ¼ 0) of the i-th project, the basic formulation of the portfolio optimization model is as follows:

maximize

m X

asi xi

(1)

i¼1

s:t: m X

ci xi  B

(2)

i¼1

ðPi  Pmin Þxi  0

i ¼ 1…m

(3)

xi þ xj  1


ci; j
meði; jÞ ¼ 1

(4)

xi  xj  0


ci; j
depði; jÞ ¼ 1

(5)

m X

air xi  ð  Þtr

cr

(6)

i¼1

xi 2f0; 1g

ci

(7)

where B is the total available budget, Pi is the profitability index of the i-th project, Pmin is the minimum required profitability index of each project, air is the technological coefficient of project i concerning a specific requirement r2R, and tr is the target value for a specific technical, economical, or aesthetic requirement r2R defined by the DMs. Constraints (2) and (3) guarantee that essential economical requirements are obtained by all projects. Constraints (4) and (5) are synergy rules that represent incompatibility and compatibility restrictions, respectively, among different projects. These constraints can be defined by technical, geographical, and productivity reasons.

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203

Constraints (6) assert that the targets for specific additional requirements, such as costs, power performance, or peak performance, are fulfilled. The solution of this optimization model generates the so-called RIPs, each one consisting of a set of ranked alternatives in Phase 2. The RIPs are then further evaluated in the next phase.

with the Fuzzy-TOPSIS's rankings. The portfolio optimization model was validated, comparing its results with the ones obtained by solver LINDO for the same ILP formulations. LINDO was chosen due to its simple interface and straightforward model expression style. As the same results were obtained, our implementation was considered valid.

3.5. Phase 4 - prioritization of RIPs

4. Case study

The objective of this phase is to prioritize the RIPs selected in Phase 3, taking into account the individual evaluation of each restructuring alternative performed in Phase 1. While Phase 3 selects a viable set of RIP, their prioritization, considering different scenarios (in a sensitivity analysis process), is an important task towards a robust decision-making process. The algorithm designed in this phase considers that the global evaluation of an RIP is related to the individual performance of each constituent restructuring alternative. Thus, a new aggregated fuzzy decision matrix ~ ¼ ½~r  R mj an , where a is the number of RIPs obtained in Phase 3, is computed according to the following formulation:

This section presents the application of the developed decisionmaking method to the restructuring of RPR.

~r mj ¼

m 1X ~ 5x d i S i¼1 ij

m ¼ 1; …; a; j ¼ 1; …; n

(8)

where ~r ij ¼ ðaij ; bij ; cij Þ is the aggregated TFN performance rating of P the RIPi in relation to each evaluation criteria j, S ¼ m i¼1 xi is the number of restructuring alternatives that comprise RIPi, xi is the binary decision variable computed in Phase 3 (xi ¼ 1 when the restructuring alternative i was selected, and xi ¼ 0 otherwise), and ~ is the fuzzy rating of alternative i in relation to criterion j, acd ij cording to Phase 1 e Step1. Finally, FETOPSIS is reused to prioritize the RIPs, using as input ~ ¼ ½~r  parameter the aggregated fuzzy decision matrix R mj an , and ~ ~ the vector of weights W ¼ ½wj n (obtained in Phase 1). 3.6. Implementation issues The integrated decision method was implemented to run in a PC environment under Windows. The current implementation uses EXCEL both as the user interface and to carry out statistical analysis of the results obtained by FETOPSIS and the portfolio optimization model. EXCEL's high quality graphical facilities are also used to present the results of both models, facilitating the use of the software system by the DMs, with large experience in using this spreadsheet software. FETOPSIS was implemented using JAVA, while the portfolio optimization model was solved using IBM ILOG CPLEX v12.5. Facilities were provide to call FETOPSIS and the CPLEX solver from EXCEL. In order to verify the internal system structure and to guarantee accuracy, a subsystem Verification & Validation [35] was carried out for the two models in the developed computational system. It consisted of testing, verifying, and validating the models' implementations one at a time as they are developed. FETOPSIS was validated using numerical simulation, comparing it with ELECTRE II, a well-known extension of ELECTRE that is used to rank alternatives, and Fuzzy-TOPSIS. In the simulation experiments, we considered different sample sizes and parameters, in terms of the number of criteria, alternatives, and different ways of rating alternatives and weight distribution. The results of the simulation showed that the rankings offered by FETOPSIS presents some advantages in comparison with the ones offered by ELECTRE II and Fuzzy-TOPSIS. FETOPSIS significantly reduced or eliminated both ranking indifferences and reversals in a comparison with ELECTRE II, and increased the discrimination of alternatives when compared

4.1. Phase 1 e problem structuring As previously mentioned, the members of the RPR Board constituted the decision-making group, responsible for defining the objectives, selection criteria, and preferences for which the decision will be guided. Besides the analysts and the board, a group of experts was appointed by the CEO to provide technical information to assess the restructuring alternatives. Due to the impossibility of directly measuring the opinions of diverse public interests, DMs were asked to consider in their analysis other perspectives that have already been externalized (through the media or direct interactions), especially in relation to social, environmental, and political issues. 4.1.1. Candidate alternatives The identification of a good set of alternatives is an important part of the decision-making process, and therefore it is up to the analyst to provide the means by which the DMs can expand and qualify the alternatives to be analyzed, creating a minimum set of alternatives that are viable, complete, and diversified. As an initial analysis point, we considered the results of the strategy workshops conducted previously at the beginning of this study. The potential areas of focus identified for RPR were as follows: (i) oil refining; (ii) distribution of oil products; (iii) logistics; (iv) treatment of waste and effluents; (v) oil derived products recycling, including the development of special technologies for waste collection and processing; and (vi) bioprocessing, with emphasis on bio-diesel. Fig. 2 presents the strategy generation table constructed by the decision-making group. The first column consists of the six potential business areas. The other columns represent the business drivers extracted from the SWOT matrix, namely the required level of investment products offered, the raw materials used, the market, the degrees of mastery of the technologies involved, and the relationship with controllers. The alternatives were defined by

Fig. 2. Alternative matrix generation.

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selecting an option in each column of the table. During the analysis process, combinations of columns that resulted in alternatives deemed unfeasible were discarded. The group identified the following lines of business as potential alternatives to industrial restructuring of RPR: (i) Petroleum refining only in distillation units (REF_1); (ii) Petroleum refining in all refinery units (REF_2); (iii) Unit test for research center (UTC); (iv) Logistic services (LOG); (v) Treatment of waste and effluent (TREF); (vi) Special solvents (SOLV); (vii) Solvent distribution (DSOL); (viii) Lubrificant formulation (LUB); (ix) Biofuels (BIO); and (x) Plastic recycling (REC).

environmental efficiency of industrial processes. Finally, the criterion of broader environmental risk spectrum was directed to focus on the potential for accidents with environmental impact. We also checked the sufficiency of the criteria selected for the evaluation of the alternatives. We identified the need for inclusion of representative criterion of investment attractiveness in terms of economic efficiency. The evaluation of the economic attractiveness of any business project basically depends on the volume of investment required, the expected benefits, and the useful life of the project. The profitability index was chosen by the DMs. Table 1 presents a detailed description of the final set of criteria.

4.1.2. Selection of evaluation criteria The set of criteria used to measure the degree of achievement of the goal “ensuring sustainable business continuity of RPR” was defined from a hierarchical structure model based on three drivers: (i) create shareholder value (economic dimension); (ii) contribute to regional development (social dimension); and (iii) act in an environmentally responsible way (environmental dimension). To identify the criteria that detail each of the three sub-goals, we used the GRI [36], an internationally recognized standard for assessing corporate sustainability. Initially, seventy five indicators applicable to the context of industrial enterprises were identified. Considering the decision-making stage and the availability of information, the decision-making group selected fourteen criteria for assessing the corporate sustainability of the restructuring alternatives. For the dimension“add value for controllers” five criteria were selected: synergy with controllers, expected growth, fundraising potential, access to raw materials, and business and technology risk. For the dimension “contribute to regional development” four criteria were selected: generation of employment and income, return on taxes, local market products and services, and research and innovation. Finally, to the dimension “act with environmental responsibility” five criteria were selected, namely air emissions and wastewater, environmental impact risks, consumption of natural resources, energy efficiency, and use of clean technologies and sustainable products. In order to define the independence between the criteria, statistical tests of Spearman correlation for each pair of criteria were employed. To preserve the representativeness of each dimension (economic, social, and environmental), only significant correlations identified internally to each of the dimensions of analysis were analyzed. In the economic dimension, the criterion of access to raw material was highly correlated with the criterion of expected growth. While the guarantee of access to raw materials can be regarded as a major competitive advantage, it was decided to eliminate the criterion of access to raw materials, incorporating it into the evaluation parameters of growth expectation criteria. Similarly, the criteria of fundraising and support for enterprise risk also showed high correlation. The former criteria was suppressed, since difficulties in raising funds to finance the investments imply an increase of business risks. In the social dimension, the criterion of direct generation of employment and income showed high correlation with the criteria generation of taxes and local market products and services. Whereas, from the perspective of regional development, direct employment in the evaluated business lines represent marginal impact, it was decided to set a new criterion, income generation, that would incorporate the previous criteria. In the environmental dimension, the presence of correlation between the various criteria required a broader redefinition of the criteria. The criteria for air emissions, wastewater, clean technologies, and sustainable products were aggregated into a criterion of sustainability in relation to the raw material used and the generated products. The consumer criteria of environmental resources and energy efficiency were aggregated into a criterion on the

4.1.3. Scoring of alternatives The evaluation process of alternatives was conducted by a group of experts in such a way as to obtain a representative group consensus. During this process, we enabled the free interaction between experts and analysts, in an attempt to minimize inconsistencies. Evaluation parameters were often reviewed to ensure the best discrimination of the alternatives as possible. Procedures were also adopted to reduce the incidence of common biases in qualitative assessment processes, such as ambiguity, central tendency, and overstatement. To avoid deviations due to poor understanding of the meaning of the criteria, we provided to the experts a criterion descriptor, composed of three basic pieces of information: (i) description of the criteria; (ii) justification for use of the criterion within the problem context; and (iii) parameters to be observed in the evaluation of alternatives. Fig. 3 shows an example of the Research and Development criterion descriptor. The linguistics terms used to assess the alternatives are presented in the first column of Table 2. Table 3 shows the evaluation of the alternatives for each evaluation criterion. In order to avoid bias resulting from the central tendency, we asked the DMs to classify at least one alternative as “Very High,” and another as “Vey Low,” distributing the other alternatives between these two ends. 4.1.4. Criteria weighting The decision-making process requires an explicit reflection of the DMs on their preferences and values. Thus, obtaining adequate and consistent criteria weighting is an essential condition to ensure a better process. Although FETOPSIS does not include any specific mechanism for supporting this task, several techniques may be applied in defining the relative importance of criteria, including analytical procedures, simulations or empirical approaches. A direct assignment of weights was employed in our case, using the experience and insight of the DMs to determine a consistent preference structure. The DMs received a form in which the ten criteria were sorted alphabetically. From the information of each criterion descriptor, decision makers initially placed the criteria in order of importance in relation to their contribution to “assurance of sustainable business continuity of RPR.” Based on this ruling, DMs classified the criteria in the five-point scale shown in the third column of Table 2. Table 4 presents the importance given by the three decision~ jk ). Preferences issued by makers to each one of the criteria (w DMs reflect the different perspectives involved in this type of problem. DM 1 distributed the importance of criteria fairly equally among the three considered dimensions. The remaining DMs had a clear bias in relation to one dimension. While DM 2 minimized the importance of criteria related with social dimension (C21 ¼ Average, C22 ¼ Very Low, C23 ¼ Low), DM 3 did the same in relation with the environmental dimension (C31 ¼ Very Low, C32 ¼ Low, and C33 ¼ Low). It should be noted that both judgments did not mean negligence with a specific dimension of performance, but differences in perspectives on sustainability. Different preference structures indicate that there were different perceptions of

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205

Table 1 Criteria and sub-criteria definitions. Criteria

Sub-criteria

1. Economic (C1)

Expectation of growth (C11) Exposure to enterprise risks (C12) Alignment with controllers (C13) Profitability (C14) Income generation (C21) Tax generation (C22) Research and innovation (C23)

Definition

Growth potential of the business from investments Technological and enterprise risks that affect the alternative implementation Business Process Alignment with two or more controllers Relation between operating result expected and total investment required Impact on generation of direct and indirect employment Generation of tax revenue for state and local governments from new strategies implemented Possibility of interaction with universities and research centers with purpose of knowledge transferring and innovation Efficient industrial processes (C31) Use of natural resources, waste treatment, and control the emission of carbon dioxide Environmentally sustainable products Use of renewable raw-material, biodegradable or recycled, even as the production of reusable products or (C32) recyclables. Exposure to environmental risks (C33) If the industrial process requires the use, storage, and disposal of hazardous substances

2. Social (C2)

3. Environmental (C3)

Fig. 3. “Research and Innovation” criteria descriptor.

Table 2 Linguistic variables for importance level of criteria and alternatives ratings. Weights

Fuzzy numbers

Ratings

Fuzzy numbers

Very low Low Average High Very high

(1, (1, (3, (5, (7,

Unimportant Moderately important Important Very important Extremely important

(1, (1, (3, (5, (7,

1, 3, 5, 7, 9,

3) 5) 7) 9) 9)

1, 3, 5, 7, 9,

3) 5) 7) 9) 9)

what attributes of an alternative have greater impact on the sustainability of RPR. One of the main challenges of the group decisionmaking process is to obtain a structure of preferences that fairly represents the set of perceptions of all the involved DMs. FETOPSIS presents a mechanism to aggregate preference structures from several DMs (see Step 2 of the proposed method in Section 3.3). 4.2. Phase 2 e restructuring alternatives evaluation Once all required parameters for Phase 2 were defined, FETOPSIS was employed to obtain the following variables: the aggregated fuzzy weights of the criteria (Table 4); the normalized fuzzy

decision matrix; the concordance and discordance matrices; the fuzzy pure concordance and discordance indexes; the fuzzy positive ideal solution; the fuzzy negative ideal solution; and the closeness coefficient (Table 5). These values were computed following the method described in Section 3.3 and all intermediate data can be downloaded from http://goo.gl/TnQpx4. The higher the value of the proximity coefficient CCa of an alternative a, the closer this alternative is to the ideal positive solution, and the farther it is from the ideal negative solution. Therefore, the bigger the proximity coefficient, the better the alternative. 4.3. Phase 3 e definition of the RIPs The purpose of this step is to identify, from the assessment of restructuring alternatives, viable projects of industrial restructuring, composed by a set of restructuring alternatives, implemented in an integrated way. The identification of different RIPs should consider, in addition to the individual contribution of each alternative, strategic guidelines, possible synergies (positive or negative) between the alternatives, as well as the technical and budgetary constraints set by the decision-making group. The board

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A.P. Caetani et al. / Energy 111 (2016) 197e210

Table 3 Fuzzy decision matrix.

REF_1 REF_2 UTC LOG TREF SOLV DSOL LUB BIO REC

C11

C12

C13

C14

C21

C22

C23

C31

C32

C33

Very Low Low Low Very High Very High Average Average High Average Low

Low Average Low Very Low Very Low Low Low Average High Very High

High High Average Very High Very High High Very Low Average High High

Very Low Low Very Low High Very High High Very High Average Low Low

Low Average Low Very Low Very Low Average Average High High Very High

Low Average Very Low Low Low Average Average High Very High Low

Average Average High High Very High Average Very Low Average High Very High

Low Very Low Low High Very High Low High Average High High

Average Low Very Low High High Average High Average Very High High

High Very High High Low Low High Very Low Average High High

Note: REF_1 refers to petroleum refining only in distillation units; REF_2 refers to petroleum refining in all refinery units; UTC refers to unit test for research center; LOG refers to logistic services; TREF refers to treatment of waste and effluent; SOLV refers to special solvents; DSOL refers to solvent distribution; LUB refers to lubrificant formulation; BIO refers to biofuels; and REC refers to Plastic recycling. Table 4 Importance level of the criteria.

C11 C12 C13 C14 C21 C22 C23 C31 C32 C33

Decision-maker 1

Decision-maker 2

Decision-maker 3

Aggregated weights

Very important Moderately important Extremely important Extremely important Very important Important Unimportant Moderately important Very important Extremely important

Important Important Extremely important Very Important Important Unimportant Moderately important Very important Very important Extremely important

Very important Unimportant Moderately important Very important Extremely important Very important Important Unimportant Moderately important Moderately important

(3.00, (1.00, (1.00, (1.00, (3.00, (1.00, (1.00, (1.00, (1.00, (1.00,

Table 5 Final ranking of the restructuring alternatives. Alternatives

~ d i

~

d i

CCi

Refining only in distillation units (REF_1) Refining in all refinery units (REF_2) Unit test for research center (UTC) Logistics services (LOG) Treatment of waste and effluent (TREF) Special solvents (SOLV) Solvent distribution (DSOL) Lubrificant formulation (LUB) Biofuels (BIO) Plastic recycling (REC)

1.2849 1.3750 1.3510 0.8266 0.7382 1.0222 0.8418 1.1279 1.0758 1.1948

0.9364 0.8403 0.8926 1.4052 1.5168 1.1773 1.3315 1.0901 1.1118 0.9529

0.4215 0.3793 0.3978 0.6296 0.6726 0.5352 0.6126 0.4914 0.5082 0.4436

has already defined that RIPs should be implemented in the same industrial site currently occupied by the RPR, leveraging complementary relations and the concurrent use of industrial assets. The alternatives were thus evaluated for potential interdependencies in relation to income benefits, resource utilization, and technical aspects. For simplification purposes, interdependencies were evaluated only between pairs of restructuring alternatives, as the binary interaction model proposed by Ref. [37]. For each pair of alternatives, DMs answered the following questions: (i) Are the business lines incompatible with each other?; (ii) Is one of these business lines a prerequisite for implementation of the other?; and (iii) Does the implementation of these business lines involve shared resources (e.g. civil works, logistics infrastructure, supplies, or utilities)? Positive answers to the first two questions, indicate the existence of technical dependencies between business lines, considered as restrictions on the composition of the restructuring strategy. Positive answers to the last question indicate the existence of synergies, for the sharing of resources enhancing the reduction in investment required for their implementation. Moreover, given

6.33, 3.00, 7.00, 7.66, 7.00, 4.33. 3.00, 3.66, 5.66, 7.00,

9.00) 7.00) 9.00) 9.00) 9.00) 9.00) 7.00) 9.00) 9.00) 9.00)

the nature and value of the resources involved, some alternatives cannot be implemented concomitantly. Alternatives REF_1 and REF_2 share the same industrial assets and therefore are mutually exclusive. Similarly, biofuel (BIO) and test unit (UTC) are incompatible with the refining activity in all RPR processing units (REF_2), since these units depend on the same equipment or parts for their effective implementation. Other alternatives, by contrast, depend on others. The test unit (UTC) alternative must be associated with the refining activity in the same way as the solvents production unit. The distribution of solvents (DSOL) alternative is only feasible if associated with solvent production activity (SOLV). Furthermore, the DMs added eight additional alternatives, obtained by combining the initial set of restructuring alternatives, based on the

Table 6 Parameters of the portfolio optimization model. Alternative

CCi

asi

ci

Pi

REF_1 REF_1C REF_2 UTC LOG LOG_D LOG_E LOG_DE TREF TREF_D SOLV DSOL LUB LUB_A BIO BIO_B REC REC_B

0.4245 0.4245 0.3793 0.3978 0.6296 0.6296 0.6296 0.6296 0.6726 0.6726 0.5352 0.6126 0.4914 0.4914 0.5082 0.5082 0.4436 0.4436

3 6 1 2 148 149 150 153 224 225 50 51 16 19 48 49 12 15

25.00 20.00 40.00 0.10 5.00 4.25 8.00 7.25 4.50 3.75 2.00 0.50 2.00 2.00 50.00 46.50 60.00 56.50

1.20 1.42 1.70 1.10 10.96 11.94 5.58 6.26 12.00 12.07 6.00 13.00 3.10 3.70 1.72 1.76 1.44 1.50

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criteria weights in Table 4), FETOPSIS generated the ranking presented in Table 9.

following analysis (see Table 6): 1. LUB_A: originated from the association of LUB and REF_1 or REF_2, producing an increase of 30% in profitability. 2. BIO_B and REC_B: originated from the association of BIO and REC, reducing the total investment cost by $3.5 M. 3. REF_1C: originated from the association between the alternatives BIO and REF_1, reducing the total investment costs of the new alternatives by around $5 M. 4. LOG_D and TREF_D: originated from the simultaneous implementation of LOG and TREF, reducing the total investment costs of these two new alternatives by $0.75 M. 5. LOG_E: originated from the association of LOG and REF_1 or REF_2, increasing the total investment costs by $3 M. 6. LOG_DE: originated from the association of TREF, reducing the total investment costs by $ 0.75 M, and REF, simultaneously increasing this cost by $ 3.0 M, totaling an increase in the total investment costs of $ 2.75 M. With the definition of all alternatives, along with their limitations, synergies, average increased scores (asi ), and average profitability (see Table 6), different portfolio optimization model (1)e(7) instances were generated, each one representing nine different scenarios. values of ci, and Pi in Table 6 are weighted averaged values, obtained from a risk analysis performed by the refinery's technical staff using Monte Carlo simulation. Due to confidentiality issues, this risk analysis cannot be presented in this paper. These scenarios considered different combinations of budget values and minimum profitability indexes. Budget values were defined within the set B(in$M) ¼ {40,70,120}, while the minimum profitability index in the set Pmin ¼ {1,1.5,2}. Six different RIPs were obtained for the twelve analyzed scenarios as presented in Table 7. RIPs obtained with low profitability were composed by a higher number of restructuring alternatives. As profitability increases, the portfolio optimization model was more selective, focusing in few, but very profitable alternatives. Budget was not so restrictive as profitability. RIPs 1, 4, 5, and 6 were only indicated for low profitability scenarios. RIP3 was the only one indicated for Pmin ¼ 4, regardless of the considered budget, while RIP2 was obtained for 4 scenarios, one with Pmin ¼ 1.5, and for all scenarios with Pmin ¼ 2. The values of ci and Pi were varied in ± 10% range in a sensitivity analysis. The results show in Table 7 were not altered. 4.4. Phase 4 e prioritization of RIPs The last phase of the decision-making process consisted in ranking the RIPs obtained in the previous phase. The new aggre~ ¼ ½~r  gated fuzzy decision matrix (R mj an ) was generated, employing Equation (8), based on the performance ratings of the restructuring alternatives constituent of each RIP (see Table 3). Table 8 presents the computed values. ~ (Table 8) and in the fuzzy Based on the aggregated matrix R

4.5. Sensitivity analysis The sensitivity analysis was performed to examine the robustness of the RIPs, considering preference changes of the DMs on the their prioritization. The analysis consisted of successively changing the weight of some criterion and observing how each of these changes affect the priority of each RIP. We used a similar procedure described in Ref. [38], approved by the decision-making group. Fifty-two different scenario cases were created. In the first 25 cases the DMs' preferences for the two highest weighted criteria “Profitability” (C14), and “Exposure to environmental risks” (C33) were varied from Very Low to Very High (VL, L, M, H, VH) by maintaining the remaining criteria preference as shown in Table 4. In the next 25 cases, the DMs' preferences for criteria “Alignment with controllers” (C13), and “Income generation” (C21) were also varied from Very Low to Very High by maintaining the other criteria preference as shown in Table 4. The preference between criteria C33 and “Exposure to enterprise risks” (C12) were interchanged (case 51st), and, in the last case, the preference between criteria ”Research and innovation” (C23) and C14 were also interchanged. Table 10 summarizes the behavior of the alternatives, in terms of the obtained rankings, considering the analyzed scenario cases. Clearly, RIP1 and RIP2 are the best alternatives, demonstrating the robustness of the results obtained with the proposed model. Alternative RIP1 presented the most robust behavior, always being the first or second best ranked alternative in the 52 cases. 4.6. Results analysis This section analyzes and evaluates the results obtained by Tables 9 and 10 towards defining a better restructuring plan for RPR. In this final analysis, the board members emphasized the interaction and synergy with the three controller companies. This was an essential condition for them to approve any restructuring initiative. RIPs 4, 5, and 6 were discarded by the DMs, for the excessive presence of diverse business lines in their compositions. There was a consensus in the decision-making group that it would be impossible to implement simultaneously the diverse business lines that constitute these RIPs in the short to medium term, either due to over-investment, or due to the complexity in project management and implementation. Although with scores lower than RIP1 and RIP2, RIP3 was considered a good restructuring project alternative for the company, given the following attributes: (i) it requires a reasonable investment of US $23 M; (ii) it maintains current refining activity; (iii) it offers the alternatives of higher profitability and individual assessment (LOG e TREF); and (iv) it adds the activity of formulating lubricants, which has a low initial investment, and good synergy with controllers (lubrificants are raw materials of two controllers). Moreover, it involves the actual core

Table 7 RIPs configuration. RIP

B

Pmin

Restructuring alternatives

RIP1 RIP2

40 40 40, 70, 120 40, 70, 100 70 70, 120 120

1 1.5 2 4 1 1.5 1

UTC, REF_1, SOLV, DSOL, LOG_DE, and TREF_D LOG_D, TREF_D, and LUB

RIP3 RIP4 RIP5 RIP6

207

LOG_D, and TREF_D UTC, REF_1, LUB_A, SOLV, DSOL, LOG_DE, and TREF_D REF_2, LUB, SOLV, DSOL, LOG_DE, and TREF_D UTC, REF_1C, LUB_A, BIO, SOLV, DSOL, LOG_DE, and TREF_D

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Table 8 ~ ¼ ½~rm  Fuzzy decision aggregated matrix (R j an ). RIP C11 RIP1 (6.33, 9.00) RIP2 (7.00, 9.00) RIP3 (3.67, 6.67) RIP4 (3.86, 7.00) RIP5 (4.33, 7.67) RIP6 (3.75, 7.00)

C12 8.33, 9.00, 5.33, 5.57, 6.33, 5.50,

(1.67, 4.33) (1.00, 3.00) (1.00, 4.33) (1.29, 4.71) (1.67, 5.00) (1.75, 5.25)

C13 2.33,

(5.67, 8.33) (7.00, 9.00) (4.67, 7.67) (4.43, 7.57) (4.67, 7.67) (4.50, 7.75)

1.00, 2.33, 2.71, 3.00, 3.25,

C21 7.67, 9.00, 6.33, 6.14, 6.33, 6.25,

(2.33, 5.00) (1.00, 3.00) (1.67, 5.00) (2.14, 5.57) (2.67, 6.00) (2.50, 6.00)

C22 3.00, 1.00, 3.00, 3.57, 4.00, 4.00,

(2.33, 6.33) (1.00, 5.00) (1.67, 5.33) (2.14, 5.86) (2.67, 6.67) (2.75, 6.25)

C23 4.33, 3.00, 3.33, 3.86, 4.67, 4.50,

Table 9 Final ranking of the RIPs. RIP

~ d i

~

d i

CCi

RIP1 RIP2 RIP3 RIP4 RIP5 RIP6

1.2739 1.3134 0.9572 0.9465 1.127 0.9847

0.88631 0.8068 1.2492 1.3680 1.1077 1.2314

0.5897 0.6194 0.4338 0.4089 0.5044 0.4443

Table 10 Sensitivity analysis results. RIP

RIP1 RIP2 RIP3 RIP4 RIP5 RIP6

Ranking position 1

2

3

4

5

6

50.0% 50.0% 0.0% 0.0% 0.0% 0.0%

50.0% 26.9% 0.0% 0.0% 17.3% 5.8%

0.0% 11.5% 0.0% 0.0% 80.8% 7.7%

0.0% 11.5% 13.5% 0.0% 0.0% 73.1%

0.0% 0.0% 38.5% 48.1% 0.0% 13.5%

0.0% 0.0% 48.1% 51.9% 0.0% 0.0%

business of RPR, and presents low business risks, despite the prospects of continuity of an unfavorable macroeconomic context. However, this RIP presents high marks in environmental risk exposure, and therefore was also considered unfeasible. RIP2, formed by restructuring alternatives LOG, LUB, and TREF, initially ranked as the best alternative, presents excellent growth perspectives and great potential for integration and synergy with the activities carried out by the RPR's controller companies. The current tankage capacity of RPR, the strategic location near a large port, and broad access to modal waterway, rail, and road, greatly reduce the involved risks. Moreover, an internal study by RPR pointed out that there is a growing demand for services related with TREF, mainly due to increasing environmental regulation in Brazil. In addition, LOG and TREF, when implemented together, require low levels of investment and have a great profitability potential. Although with low risk exposure, this RIP presents only intermediate performances in relation to social and environmental criteria, mainly due to the low impact of the composed business lines in generating jobs and income for the region. However, this alternative implies the progressive deactivation of a consolidated and operating industrial park, which can generate questions and uncertainties in the controller companies, the interested social forces, and some governmental agencies. In this sense, RIP1, the most robust alternative after a sensitivity analysis, emerged to circumvent this problem. This RIP would maintain the current refining process, adding the production and distribution of solvents

(5.00, 8.33) (6.00, 9.00) (4.00, 7.33) (3.86, 7.29) (3.67, 7.00) (4.00, 7.50)

C31 7.00, 8.00, 5.67, 5.57, 5.33, 5.75,

(5.00, 8.33) (6.00, 9.00) (3.33, 7.00) (3.29, 7.00) (3.67, 7.00) (3.50, 7.25)

C32 7.00, 8.00, 5.33, 5.29, 5.33, 5.50,

(4.33, 8.33) (5.00, 9.00) (3.67, 7.33) (3.57, 7.29) (3.67, 7.67) (4.00, 7.50)

C33 6.33, 7.00, 5.33, 5.29, 5.67, 5.75,

(1.67, 5.67) (1.00, 5.00) (3.00, 6.67) (3.00, 6.71) (3.00, 6.33) (3.25, 7.00)

C34 3.67, 3.00, 4.67, 4.71, 4.67, 5.00,

(5.00, 8.33) (6.00, 9.00) (4.33, 7.00) (4.14, 7.00) (4.67,

7.00, 8.00, 5.67, 5.57, 6.67, 8)

(3.75, 5.25, 6.75)

(DSOL) and special solvents (SOLV) businesses. Based on the DMs' perception, these business activities have great economic potential, and they would require low investments to update the current production facilities. The implementation of SOLV is well aligned with the recently inaugurated pentane plant; UTC is an interesting business alternative, mainly considering that it would require extensive innovative research in the medium- and long-term, changing the current production-oriented RPR profile. The major shortcoming of this RIP is the possibility of establishing competitive relationships in the solvent area with two controller companies. Based on (i) the various analyses carried out throughout the developed method application; (ii) in the expectation of achieving simultaneously economic, social and environmental benefits; and (iii) in an appropriate balancing of the chosen RIPS in terms of allocation of resources, profitability and risks, the adoption of RIP2 was faced with some reservations by the decision-making group. The adoption of this RIP would require extensive investments (around US$ 37.6 million), and would imply in the future deactivation of the current industrial park. This fact can be considered as a very radical move by the executives of the controller companies. A more conservative and robust RIP, requiring both less investments and a less radical changes in the current production configuration, was pointed out by the DMs as a better suited option. Therefore, even if the business lines that make up the RIP2 ensure the best set of benefits and the least future risk exposure, the DM group considered that RIP1 is currently a better alternative, following the results of the sensitivity analysis. This RIP offers significant potential economical gains for both RPR and the local community, requiring a much smaller business investment (around US$ 10 million). Combining these attributes, RIP1 was seen by the managing board as the most acceptable restructuring project from the perspective of the controller companies. Overall, the DMs responded very positively to the developed method. Based on their opinion, the main advantage of the method was to offer a tool for objective analysis, avoiding the evaluation of restructuring options being based only on empirical factors or simple operational and economical measures. Analysis and evaluation of the possible restructuring alternatives, employing the models within the method, provided a means to study each alternative with respect to several and conflicting attributes, resulting in a much better decision-making process. One of the most appreciated features of the method is its ability to work with linguistic terms to capture the executives' assessments of the key aspects in the involved decision. The possibility to alter operational scenarios interactively, to evaluate them in an efficient and effective manner, and to include real-world constraints and limitations make the developed method an effective tool for strategic decisions in the energy field.

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5. Conclusions and final remarks The selection of an industrial restructuring strategy for any energy company is a complex task. The uncertainties involved, the existence of potentially conflicting objectives, and the need to reconcile different perspectives and interests require the use of sophisticated support methods for the DMs. This paper presented an integrated decision analysis approach, combining a fuzzy MADM method, FETOPSIS, and a 0e1 portfolio optimization model, offering the following support capabilities for a strategic decisionmaking process: (i) identification of possible alternatives; (ii) elicitation of evaluation criteria; (iii) a mathematical model to rank alternatives; and (iv) a portfolio optimization model to select alternatives, considering technical, economical, and alternative interdependence constraints. The method was applied to support the managing board of a small oil refinery in southern Brazil to evaluate restructuring projects. The refinery is a traditional company in the local community that facing a crisis due to the obsolescence of its production plant. Using the method, the decision-making group has been able to elicit criteria, alternatives, and preferences, and to compare the outcomes of different restructuring projects. The results of this study with the application of FETOPSIS associated with mathematical programming models have demonstrated the efficiency of the approach proposed in order to facilitate the understanding and exploration of the problem situation and thus offer adequate support to the decision-making. As any study in the area of strategic decision-making, this research presents some limitations related to methodological aspects, such as: (i) the use of binary comparison to identify interactions and synergies among alternatives; (ii) the use of a single objective optimization model to identify feasible alternatives; and (iii) the use of arithmetic average to aggregate preferences and assessments of different DMs. Another important issue is the general applicability of the method to other strategic decisions on the energy sector, as well as in other areas. The method is information intensive. The complete data acquisition may call for a systemic and organized approach. Since several companies do not record some of the required data, this process can involve some difficulties. Furthermore, given the natural complexity of the problems such as restructuring and selection of project investments, the method calls for a long period of experimentation by the DMs before it can be effectively applied to a real problem. The required period of time is highly dependent on the experience of the decision-making group in the use of computer tools, and the presented methods and techniques in the method. However, it must be noticed that this time can not be considered as a waste, but as a learning experience that will increase the knowledge of the DMs, that will naturally improve the decision-making process. These issues may direct future extensions to this study. Overall, in proposing a structured way to present the problem and to analyze alternative solutions, the method provided a basis on which DMs could understand and exploit the problem, considering simultaneously distinct and conflicting dimensions. There was a consensus among the decision-making group that the method offered a proper support, facilitating the definition of a suitable restructuring project investment towards the sustainability of the refinery. Although the development of this study was suited to some specific conditions of RPR's restructuring process, we are very sure that several components of the developed integrated method may be used by other energy companies facing analogous crisis situations. The interaction between conflicting evaluation dimensions, the interdependence of alternatives, the use of linguistic terms to elicit preferences, and the ability to cope with group decision-making are widely generalizable, especially in

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the selection of strategic investment projects, such as the development/location of new production plants and new oil-fields development. As far as the RPR is concerned, the application of the integrated decision-making method has generated a detailed report (focusing on results) for the evaluation of the refinery controllers. Unfortunately, since the elaboration of this report, at the end of 2014, the petrochemical market in Brazil has faced several problems, related with the severe deceleration of Brazilian economy, and wellreported corruption problems involving Petrobras, one of the refinery's controller. Simultaneously, the refinery profit margins have significantly increased due to the fall in the crude oil price in the international markets. The combination of these factors has led the refinery controllers to ask the managing board to reanalyze the restructuring plan, considering the new context, where very few financial resources will be made available for new investments in this sector in the next years. A new round of the application of the developed method is being considered by the managing board. Given the described experience, it is expected that the new evaluation process will be much quicker and will be capable of producing coherent results with the new application context. Nevertheless, some recommendations of the restructuring process were used to help to implement less expensive projects in the refinery, resulting in the diversification of the product portfolio, with the inclusion of special diesels, and a more customized line of solvents and lubrificant oils. Due to the novelty of these initiatives, it was not possible to quantify the results in the refinery operations. Acknowledgement The authors would like to thank the anonymous referees and the Editor for their valuable comments which greatly improved the quality of the paper. This research has been supported by Conselho
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