Operational and environmental performance measures in a multi-product closed-loop supply chain

Transportation Research Part E 47 (2011) 532–546

Contents lists available at ScienceDirect

Transportation Research Part E journal homepage: www.elsevier.com/locate/tre

Operational and environmental performance measures in a multi-product closed-loop supply chain Turan Paksoy a, Tolga Bektasß b,⇑, Eren Özceylan a a b

Selçuk University, Department of Industrial Engineering, Campus, 42031 Konya, Turkey School of Management, University of Southampton, Highfield, Southampton SO17 1BJ, UK

a r t i c l e

i n f o

Article history: Received 4 April 2010 Received in revised form 30 September 2010 Accepted 4 December 2010

Keywords: Closed-loop chains Greenhouse gas emissions Green supply chain Mathematical modelling

a b s t r a c t This paper investigates a number of operational and environmental performance measures, in particular those related to transportation operations, within a closed-loop supply chain. A mathematical model in the form of a linear programming formulation is used to model the problem, which captures the trade-offs between various costs, including those of emissions and of transporting commodities within the chain. Computational results are presented for a number of scenarios, using a realistic network instance. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction A supply chain refers to a broad set of activities associated with the transformation and flow of goods and services, including the flow of information, from the sources of materials to end-users (Bowersox and Closs, 1996). A typical supply chain primarily consists of a number of production facilities serving a number of market regions, and the transportation of goods from source to intermediate locations, and ultimately to end-users. Flow of material from source to end-users in a supply chain is via the so-called forward chain. Recent interest in supply chains lies in the recovery of products, which is typically achieved through processes such as repair, remanufacturing and recycling, which, combined with all the associated transportation and distribution operations, are collectively termed reverse chain activities. A supply chain in which forward and reverse supply chain activities are integrated is said to be one of a closed-loop, and research on such chains have given rise to the field of closed-loop supply chains (CLSCs). One of the significant sources of greenhouse gas emissions and air pollution within a supply chain is transportation activity, which has harmful effects on human health and undesirable consequences, such as global warming. Most of the research devoted to the design of supply chains has focused on operational performance metrics, such as cost of production, purchasing and transportation, profit, and tax (Meixell and Gargeya, 2005) and has neglected the environmental aspects. Given recent concerns on the harmful consequences of supply chain activities on the environment, and transportation in particular, it has become necessary to specifically take into account environmental factors when planning and managing a supply chain. The list of environmental performance metrics of a supply chain is long, and includes emissions, energy use and recovery, spill and leak prevention and discharges (Hervani et al., 2005). Green supply chain (GrSC) design extends the traditional definition of a supply chain by explicitly considering the following factors in the design process: (i) waste resulting from any processes within the chain, (ii) energy efficiency, (iii) ⇑ Corresponding author. Tel.: +44 (0)23 8059 8969; fax: +44 (0)23 8059 3844. E-mail address: [email protected] (T. Bektasß). 1366-5545/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tre.2010.12.001

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greenhouse gas emissions, and (iv) legal environmental requirements. Among the listed factors, greenhouse gas emissions, and CO2 in particular, are by far the most prominent with respect to their hazardous consequences on human health. One source of these emissions within a supply chain is the transportation activities that take place between various components in the chain. In this study, we model and analyze a multi-product CLSC with an explicit focus on greenhouse gas emissions of the transportation activities, and the costs thereof, as well as product recovery. The model is in the form of a linear programming formulation and allows for the use of different modes of transport, each of which has its own emission rates and costs. The model also considers normal operational transportation costs, as well as various capacity limits on production and distribution. The contributions of this paper to the literature are twofold: (i) we consider environmental costs within a closed-loop supply chain network, mainly reflecting those of CO2 emissions due to transporting material in forward and reverse logistics networks, and (ii) using the proposed model and a sample problem instance, we present results of computational experiments that shed light on the interactions of various performance indicators, primarily measured by cost, but also capturing the environmental aspects. The rest of the paper is organized as follows: Section 2 presents a brief review of the literature on GrSC and CLSC. In Section 3, we describe a mathematical model for the CLSC. Results of computational experiments, using a sample instance are given in Section 4. Main conclusions are offered in Section 5.

2. A brief review of the literature This section presents a brief overview of the existing literature on GrSCs and CLSCs. There is a growing body of literature on supply chain network design concerned with environmental issues, collectively named as GrSC. A comprehensive survey of the field is provided by Srivastava (2007). We now provide a brief review of the literature that is relevant to the focus of the present paper. Beamon (2008) describes the challenges and opportunities facing the supply chain of the future and describes sustainability and effects on supply chain design, management and integration. Traditional supply chains and GrSCs are compared and contrasted, focusing on several important opportunities in GrSC management, including those in manufacturing, bio-waste, construction and packaging (Beamon, 1999). Sarkis (2003) provides a strategic decision framework for green supply chain management, in which he investigates the use of an analytical network process for making decisions within the GrSC. Sheu et al. (2005) present a multi-objective linear programming model for optimizing the operations of a green supply chain, composed of forward and reverse flows, including decisions pertaining to shipment and inventory. The model is based on a typical five-layer chain and considers a single product only, although it is applicable to any industry. The authors present computational results of the model based on the data set of a notebook computer manufacturing chain. Ferretti et al. (2007) present a case-study based on an aluminum supply chain and an associated model to design a single buyer, single vendor green supply chain that minimizes the cost and pollution of production and distribution. The model is tested on a real supply chain in Lombardy, Italy, yielding an improved chain design with reduced pollution levels. Network chain members of a CLSC can be classified into two groups (Zhu et al., 2008): (1) Forward logistics chain members, including raw material suppliers, manufacturers, retailers and demand markets; and (2) Reverse logistics chain members, including demand markets, recovery centres and manufacturers. Manufacturers and demand nodes (i.e. customers) could be seen as ‘junction’ points where the forward and the reverse chains are combined to form the CLSC network. A recent overview of the field from a business perspective is provided by Guide and Wassenhove (2009). A number of papers appear in the literature focusing on the design of the reverse logistics network, or that of the closedloop supply chain. We provide some pointers to the relevant literature. A closed-loop logistics model for remanufacturing has been studied by Jayaraman et al. (1999) in which decisions relevant to shipment and remanufacturing of a set of products, as well as establishment of facilities to store the remanufactured products are taken into consideration. The model is in the form of a 0–1 integer programming formulation and minimizes a total cost function of shipment, remanufacturing and inventory. Fleischmann et al. (2001) consider a reverse logistics network design problem in which they analyze the impact of product return flows on logistics networks. Guide et al. (2003) take a contingency approach in running CLSCs with product recovery. The problem of consolidating returned products in a CLSC has been studied by Min et al. (2006) who propose a mixed-integer, nonlinear programming model and a genetic algorithm for its solution. Kannan et al. (2010) developed a multi echelon, multi period, multi-product CLSC network model for product returns, in which decisions are made regarding material procurement, production, distribution, recycling and disposal. Yang et al. (2009) developed a model of a general CLSC network, which includes raw material suppliers, manufacturers, retailers, customers and recovery centres. For an excellent review of methodological and case-study based papers in reverse and closed-loop logistics network design, the reader is referred to Aras et al. (2010). One of the first studies, to our knowledge, to consider environmental issues within CLSCs is by Krikke et al. (2001, 2003) who examine a supply chain design problem for refrigerators. The authors offer a comprehensive mathematical model that not only minimizes costs associated with distribution, processing and facility set-up, but also takes into account the environmental costs of energy and waste. Our study is similar to that of Krikke et al. in spirit, although we also consider multiple products in the supply chain network, restricting our attention to material flows rather than to decisions pertaining to

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processing and facility set-up. In the next section, we present a formal definition of the problem studied in this paper, together with an associated mathematical model.

3. Problem definition and modelling In this paper, we consider a closed-loop supply chain in which the chain members are broadly classified into two groups: (i) forward chain entities, and (ii) reverse chain entities. The former is used to produce and deliver products to end-users, whereas the latter is used for recycling or waste-disposal of the same products. The network is structured as a typical 5-layer forward supply chain (Sheu et al., 2005), namely: (i) raw material supply, (ii) plants, (iii) warehouses, (iv) distribution centres, (v) customers (end-users). Let S denote the index set of suppliers, Q denote the index set of plants, V denote the index of warehouse, K denote the index set of distribution centres (DCs), L denote the index set of customers. Similarly, a 5-layer structure is considered for the reverse chain, including: (i) collection centres, (ii) repairing centres, (iii) dismantlers, (iv) decomposition centres, (v) final disposal locations of waste material. Let M denote the index set of collection centres, U denote the index set of repairing centres, P denote the index set of dismantlers, O denote the index set of disposal and D denote the index set of decomposition centres. Consider a supply chain network which consists of forward part Gf = (Nf, Af), where Nf = {S [ Q [ V [ K [ L} is the set of forward nodes and Af = {(i, j) | (i 2 S, j 2 Q) [ (i 2 Q, j 2 V) [ (i 2 Q, j 2 K) [ (i 2 V, j 2 K) [ (i 2 V, j 2 L) [ (i 2 K, j 2 L)} is the set of ‘‘forward’’ arcs, and a reverse part Gr = (Nr, Ar), where Nr = M [ U [ P [ O [ D is the set of reverse nodes and Ar = {(i, j) | (i 2 L, j 2 M) [ (i 2 M, j 2 U) [ (i 2 M, j 2 P) [ (i 2 P, j 2 O) [ (i 2 P, j 2 D)} is the set of reverse arcs. The overall network on which the problem is modelled is denoted by G = (N, A) where N = Nf [ Nr and A = Af [ Ar [ {(i, j) | (i 2 U, j 2 K) [ (i 2 U, j 2 V) [ (i 2 D, j 2 S) [ (i 2 D, j 2 Q)} where the third set is the set of arcs linking the reverse chain back to members of the forward chain. The network includes a number of suppliers (i.e. set S) that provide a number of different types of raw materials to plants in which they are transformed into the same number of different products. We denote by R the index set of products. Each product has an associated recycling rate, specifying the rate at which the product can be recycled. For instance, a rate of 100% indicates that the used product can be fully recovered or transformed into a new one, whereas a rate of 50% denotes that the product can only be partially recovered. An example of partial recovery is a PC, which is shown to have a recycle rate of 46% in Korea (Choi et al., 2006). Further assumptions about the problem are stated below: (a) The demand for each product is for a single-period, is deterministic and must be fully satisfied (i.e. no shortages are allowed). The demands and the transported materials are divisible amounts, which is applicable in the case of supply chains of gas or liquid products. (b) The flow is only allowed to be transferred between two sequential echelons (except from a warehouse to a customer). (c) The capacities plants, warehouses and centres (e.g. distribution) are limited and are fixed. (d) Transportation, purchasing, penalty and opportunity costs are deterministic and known a priori. (e) The estimated CO2 emission rates for all transportation activities are available (e.g. per gram per ton-mile, as given in Forkenbrock, 2001). The first three are standard assumptions for supply chain design, and are also considered in other studies (e.g. Sheu et al., 2005; Neto et al., 2008; Wang and Hsu, 2010). One of the most important issues in designing closed-loop supply chains is reverse rates. Wang and Hsu (2010) pointed out that, in the recovery systems, a common assumption is that the recovery amount is calculated as a certain percentage of customer demand. r The demand for product r 2 R by customer i 2 L is denoted by di . Each node i 2 N of the supply chain has a certain capacity r associated with it for each type of product r 2 R it ‘‘processes’’, denoted hi . This ‘‘processing’’, for example, pertains to production operations at each plant, storage at each warehouse or distribution centre, or dismantling operations at each dismantler, etc. In the forward chain, the transportation operations from one layer to another can be realized via a number of options. These options may consist of different modes of transport (e.g. rail, road, shipping, air) hence yielding a multimodal (but not necessarily an intermodal) transportation network, or a number of alternatives within a single mode of transport. This could, for example, be different models of trucks available in road transport. We denote by T the index set of available transportation options. Each transportation option t 2 T between two nodes i 2 N and j 2 N of the supply chain incurs a certain operation cost denoted by ctij (usually a monetary unit per unit of product shipped). The transportation capacity available t at each node denoted i 2 N is denoted by bi , usually defined by the maximum amount of product that can be shipped from node i 2 N using mode t 2 T of transport and dictated by the available fleet ready to perform the transportation operations at that node. Each mode t 2 T transportation option also has an estimated cost of CO2 emissions whilst traversing an arc (i, j) 2 A and this is denoted by etij . We acknowledge that the calculation of the amount of greenhouse gas, in particular CO2, emitted by a certain mode of transport is complex and is typically a nonlinear function of a number of factors, including load, speed and coefficient of drag (see Bektasß and Laporte, 2009, for estimation of emissions in the context of routing vehicles, and Fagerholt et al., 2010, for calculating emissions in the context of shipping). To take into account such calculations within a strategic/tactical level network design problem, such as the one we consider in our study, would be impractical. It is more

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reasonable to use widely available estimates of emissions (such as ton per kilometer) at this level of planning. Once the network is designed, one can then use the aforementioned approaches to plan the detailed operational level activities using more accurate estimators for gas emissions. Contrary to forward operations, the reverse component of the closed-loop supply chain is for either (i) repairing/refurbishing, (ii) recycling or (iii) disposal of a used product. For any of these operations to take place, used products need to be collected, through various means, from end-users, and transported to collection centres. If a product is of such a nature that it can be either repaired or refurbished for a new sell, then it is transported to DCs, from where it is once again delivered to end-users. To this end, we define the parameter armin (armax, respectively) which denotes the expected minimum (or maximum, respectively) collection rate from customers to collection centres for product of type r 2 R. Since there are bound to be losses in the reverse logistics network due to recycling operations or the condition of the used products, we allow for such losses in our problem definition through the use of recovery rates. At each collection centre, a used product is either deemed to be recoverable through repair or refurbishment, or it will be dismantled. In the former case, br specifies the repair/recycle rate of product of type r 2 R in each collection centre and is delivered to repairing centres, 1  br specifies the repair/recycle rate of product of type r 2 R in each collection centre and is delivered to dismantlers, vr defines the rate at which a product of type r 2 R is repaired at each repairing centre and transported to DCs, and 1  vr defines the rate at which a product of type r 2 R is repaired at each repairing centre and transported to warehouses. In the case of the latter, some components of the dismantled product cannot be re-used and need to be disposed of. The parameter dr indicates, for each product of type r 2 R, the expected fraction of the product that is to be sent to decomposition centres. The rest, with a fraction of 1  dr, are disposed of. As for the reusable parts of a product of type r 2 R, some are sent directly to suppliers and the corresponding rate for this operation is defined as er, whereas the rest, the fraction of which is 1  er, are sent to production plants. For each product r 2 R, we denote by pr the unit profit made from choosing to produce that product. As an example, consider two different products one of which is cheap but not recyclable, and the other recyclable but more expensive. If the second product is chosen, it might be re-used due to its recyclable nature. We denote by uri the unit purchasing cost of raw material for product r 2 R from supplier i 2 S. The problem posed in this paper lies in deciding on the flow of materials between each stage of the multi-product closedloop supply chain, so that customer demands are met, with an objective of minimizing the total cost of transportation, purchasing and CO2 emissions, and maximizing the amount of products recycled. The latter two are incorporated into a single objective function to represent various regulations relating to environmental and social responsibilities. These two components minimize greenhouse gas emissions and, at the same time, encourage the companies to use recyclable products through the profit (opportunity cost) defined above. Fig. 1 shows the CLSC network that is considered in this paper. We now present a mathematical model of the problem. There are two sets of decision variables for the problem at hand. X rtij denotes the amount of transported material of type r 2 R via mode of transport of type t 2 T on arc (i, j) 2 Af. Similarly, Y rtij denotes the amount of transported product r 2 R via mode of transport of type t 2 T on arc (i, j) 2 Ar. Although these variables represent similar things, we choose to differentiate between the notation of forward and reverse flows for the sake of simplifying the remainder of the exposition. Given these definitions, the objective function is represented below:

Raw material suppliers (S)

εr 1 − εr

Decomposition Centres (D)

Plants (Q)

Final disposals (O)

δr χr

Distribution centres (K)

1 − χr

Warehouses (V)

αmin , αmax

Repairing centres (U)

βr

1 − δr Dismantlers (P)

1 − βr Collection centres (M)

Consumers (L) Fig. 1. A closed-loop supply chain (CLSC) network.

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X XX X XX XXXX ðctij þ etij ÞX rtij þ ðctij þ etij ÞY rtij þ uri X rtij

Minimize

ði;jÞ2Af r2R



X

XX

pr

r2R

i2U

ði;jÞ2Ar r2R

t2T

Y rij þ

XX

j2K

i2U

Y rij þ

j2V

XX i2D

t2T

Y rij þ

j2S

i2S

XX i2D

r2R

j2Q

t2T

! Y rij :

ð1Þ

j2Q

The objective function (1) has four components. The first two represent the cost of transportation and emissions on each arc of the network for each mode of transportation in the forward and reverse chains, respectively. The third component represents the cost of purchasing over all product types. The final component represents the profits obtained by introducing recycled materials back into the (forward) supply chain, and is used as an incentive for the companies to choose and use recyclable products. We now present the constraints of the model, starting with those relating to capacity:

X X j:ði;jÞ2A

X X j:ði;jÞ2A

r

8 i 2 Nf ; r 2 R

ð2Þ

r

8 i 2 N r ; r 2 R:

ð3Þ

X rtij 6 hi

t2T

f

r

Y rtij 6 hi

t2T

The capacity constraints are mainly of two types: one for process/production capacities in the forward network, represented by (2), and the other for the same in the reverse network, represented by (3).

X X

X rtij 6 bi

t

8 i 2 Nf ; t 2 T:

ð4Þ

Y rtij 6 qti

8 i 2 N r ; t 2 T:

ð5Þ

j:ði;jÞ2Af r2R

X X j:ði;jÞ2Ar r2R

Constraints (4) and (5) pertain to the transportation capacities for each arc of the supply chain network.

XX i2S

t2T

t2T

i2Q

t2T

i2V

X rtij þ

8 j 2 Q; r 2 R

ð6Þ

X rtji

8 j 2 V; r 2 R

ð7Þ

X rtji

8 j 2 K; r 2 R

ð8Þ

t2T

XX t2T

i2L

XX

t2T

XX

t2T

i2V

XX

X rtji þ

X rtji

t2T

i2L

XX

X rtij ¼

X rtji þ

t2T

i2K

XX

XX i2K

XX

X rtij ¼

X rtji þ

t2T

i2V

XX i2Q

XX

X rtij ¼

r

X rtij P dj

8 j 2 L; r 2 R:

ð9Þ

t2T

i2K

Constraints (6)–(8) ensure the conservation of flow for each type of product in the forward network. Constraints (9) are used to guarantee that customer demands for each product are met in full.

armin

XX

þ

t2T

j2V

XX

X rtji

Y rtij 6 armax

i2L

t2T

ð1  br Þ

i2U

XX i2L

vr

XX

Y rtij P

XX

t2T

i2M t2T

ð1  vr Þ

XX i2M

t2T

X rtji þ

t2T

Y rtji

XX j2K

Y rtij

8 i 2 L; r 2 R

ð10Þ

8 i 2 L; r 2 R

ð11Þ

! X rtji

t2T

8 j 2 M; r 2 R

XX i2P

XX i2K

XX

ð12Þ

t2T

Y rtij P

Y rtij P

6

j2M t2T

XX j2V

XX

! X rtji

t2T

j2K

j2M t2T

br

XX

Y rtji

8 j 2 M; r 2 R

ð13Þ

t2T

Y rtji

8 j 2 U; r 2 R

ð14Þ

t2T

Y rtij P

XX i2V

t2T

Y rtji

8 j 2 U; r 2 R

ð15Þ

T. Paksoy et al. / Transportation Research Part E 47 (2011) 532–546

dr

XX

Y rtij P

XX

i2M t2T

ð1  dr Þ

i2O

XX

XX i2P

Y rtij P

Y rtij P

ð1  er Þ

XX i2S

XX i2P

XX i2D

t2T

ð16Þ

Y rtji

8 j 2 P; r 2 R

ð17Þ

t2T

Y rtji

8 j 2 D; r 2 R

ð18Þ

t2T

Y rtij P

t2T

8 j 2 P; r 2 R

t2T

i2M t2T

er

Y rtji

537

XX i2Q

Y rtji

8 j 2 D; r 2 R

ð19Þ

t2T

X rtij ; Y rtij P 0 8 ði; jÞ 2 A; t 2 T; r 2 R:

ð20Þ

Constraints (10) and (11) describe the customer recovery relationship between the minimum and maximum recovery rate. Constraints (12) and (13) provide the recycled product equilibrium from collection centre to repairing centre and dismantlers, respectively. Constraints (14) and (15) show the repaired product equilibrium from repairing centre to DCs and warehouse, respectively. Constraints (16) and (17) guarantee the recycled product equilibrium from dismantlers to disposal and decomposition centres, respectively. Constraints (18) and (19) guarantee the recyclable product equilibrium from decomposition centre to suppliers and plants, respectively. Finally, Constraints (20) impose the non-negativity restrictions on the decision variables. 4. Computational experiments In this section, we present the results obtained with the proposed model, using a realistic closed-loop supply chain network problem. Instances are produced, based on randomly generated parameters, to illustrate the properties of the problem and to derive insights. We should point out that our interest does not lie in studying the computational properties of the model, or investigating the complexities of solving the problem, but rather in shedding light on the effect of the changes in various parameters of the problem on a number of performance measures, defined below. We then present some managerial insights. 4.1. Description of data The network which constitutes the basis of the sample problem consists of three suppliers, three plants, one warehouse, two DCs and five customers in the forward logistics network. Suppliers provide three kinds of raw materials which have recycle rates of 100%, 50% and 0% (i.e. non-recyclable), respectively, which, in turn, are converted into three products. It is assumed that the higher the recycle rate of a product, the higher the cost is of purchasing raw materials and carrying out the relevant production operations. The reverse logistics network in the sample problem contains two collection centres, two dismantlers, one repairing, one disposal and two decomposition centres. The collection centres are responsible for collecting the used products from customers. As for transportation, we assume that road-based transportation is used to carry out the shipping operations, for which there are three types of trucks available which are 0–3, 4–7 and 8–11 years old, respectively. We assume that the older the trucks, the cheaper their rental fees, but, at the same time, the greater their CO2 emissions, due to decreasing engine efficiency. Unit transportation cost is given as 8.42 cents per ton-mile and unit emission costs are estimated at 0.86 cents per ton-mile for a general freight truck in Forkenbrock (1999, 2001). To allow for the different types of trucks used in this instance, we set unit transportation costs at 9.20, 8.42 and 7.60 cents for truck types 1, 2 and 3, respectively. These unit transportation costs are calculated based on operating costs that are correlated with the amount of service provided, and include costs of fuel, salaries, wages, operating supplies, insurance and depreciation (Forkenbrock, 1999). In a similar fashion, we set unit emission costs at 0.77, 0.86 and 0.95 cents for truck types 1, 2 and 3, respectively. Other parameters are set as follows: armin = 0.3, armax = 0.6 (we will look at the effect of changing these two parameters in the remainder of the analysis), br = 0.4, vr = 0.7, er = 0.7, d2 = 0.5, d3 = 1.0. The sample network is depicted in Fig. 2 and all other data used for the example problem are given in Appendix A. There are three types of products in the sample network for which the raw materials are acquired by the plants from the suppliers. End-products are sent to customers either from the warehouses or the DCs. The linear programming formulation (1)–(20) of the sample network contains 663 variables and 4854 constraints. All computational experiments are conducted on a notebook with Intel Core2 Duo 1.66 GHz and 2 GB RAM and the required computation time to solve the model to optimality using LINDO 6.1 is no more than one CPU second for any of the instances solved. The primary performance measures of concern are given in columns one and two of Table 1. Due to the random generation of the parameters of the varying instances, we will present normalized values, rather than absolute values, of measures

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Warehouse Suppliers Zone

Plants Zone

Customers Zone

DCs Zone

Repairing Center Decomposition Centers Zone

Collection Centers Zone Forward Material Flow Reverse Material Flow

Dismantlers Zone Disposal Fig. 2. The network of the sample problem used in the experiments.

Table 1 Results for the initial configuration (base scenario) of the problem instance.

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9

Performance criteria

Value (% of the total cost)

Total objective function value Total transportation costs Total transportation cost in the forward network Total transportation cost in the reverse network Total environmental costs Total environmental costs in the forward network Total environmental costs in the reverse network Total purchasing costs Total profit gained recycling

$503,110.00 85.24 84.28 0.96 9.27 9.14 0.13 6.66 1.16

PC2–PC9 as a percentage of the overall value of the objective function PC1. The last column of Table 12 presents these values for the optimal solution of the basic configuration of the sample problem. The results given in Table 1 show that the total operational transportation costs account for a 85.24% share of the overall cost. This is further split into costs of the forward network transportation operations making for 84.28% of the overall cost, and the costs of the reverse transportation network staying at 0.96% of the overall cost. Total environmental costs only make up 9.27% of the overall cost, the majority of which is due to the forward network, with a contribution of 9.14%. This result is not surprising, given the relative importance of the estimated unit cost values used in the tests. 4.2. Scenario analyses for managerial insights We now generate a number of instances to carry out scenario analyses in which problem parameters are changed to see the effect on the performance measures PC2–PC9 stated above.

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4.2.1. Effect of changing demands The first scenario to consider is the effect of increasing demands of the products on the performance measures PC2–PC8. We look at two of the products representing the two extremes in terms of recycling rates: the product with a 100% recycle rate and the product with a 0% recycle rate. Demands, as presented in Table A8 in the Appendix A, are increased by the percentages shown in Table 2 for each scenario. We present the results of Scenario 1 in Tables 3 and 4. The former presents normalized values for each performance measure with respect to the base scenario of Table 1. In the latter, we present, for each scenario, the relative importance of each performance measure as a fraction of the total cost value. The results presented in Tables 3 and 4 have the following implications: First, for the fully reusable product, the increase in demand results in an increase both in forward and reverse transportation costs. Similar changes are observed with the environmental costs. Although these results are rather intuitive and are as expected, this is not the case as far as the results given in Table 4 are concerned. Here, the relative importance of PC2 is seen to decrease with increasing values of demand, whereas the relative importance of PC5 is increasing under the same circumstances, with respect to the overall cost value. This suggests that, for reusable products, increasing demand through activities such as marketing will lessen the importance of operational costs and place more emphasis on the environmental costs of the forward transportation activities. As for nonreusable products, similar results hold as far as Table 3 is concerned, but opposite results are obtained when one looks at the relative importance of the performance measures in Table 4. One possible reason for this is the fact that, due to the product being non-recyclable, no reverse operations are required, as demand is met only through the forward network. Increasing demand seems to affect the purchasing cost of the recyclable product twice as much as that of the non-recyclable product. However, with increased demand, the relative importance of the former increases, whereas that of the latter decreases.

4.2.2. Effect of different product types and return rates A closed-supply chain might be applicable for distribution and collection of a variety of products, ranging from books and greeting cards to printers, automotive parts and computers. Differentiation between these products in the context of reverse flows can be made with regards to their return percentages. Rogers and Tibben-Lembke (1998) present sample statistics on the return percentages of a number of different products, and indicate that return rates vary from as little as 4% to as high as

Table 2 Scenarios for increasing demands. Scenario Set 1

Customer demand 100% Recyclable product

Customer demand 50% Recyclable product

Customer demand 0% Recyclable product

1 2 3 4 5 6

+2% +4% +6% +0% +0% +0%

+0% +0% +0% +0% +0% +0%

+0% +0% +0% +2% +4% +6%

Table 3 Results for Scenario 1 normalized against the base scenario. Scenario Set 1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

1 2 3 4 5 6

0.9998 1.0078 1.0158 1.0003 1.0087 1.0172

0.9999 1.0079 1.0161 1.0003 1.0088 1.0173

0.9896 0.9976 0.9951 1.0000 0.9976 1.0056

0.9968 1.0049 1.0305 0.9978 1.0048 1.0129

0.9989 1.0070 1.0330 1.0000 1.0070 1.0151

0.8462 0.8530 0.8599 0.8461 0.8530 0.8599

1.0015 1.0127 1.0224 0.9955 0.9990 1.0025

0.9914 0.9908 0.9900 0.9913 0.9994 0.9987

Table 4 Relative importance of performance measures as a fraction of total cost under Scenario 1. Scenario Set 1

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

1 2 3 4 5 6

507243.20 511376.20 515509.40 507226.60 511343.00 515460.20

85.22 85.21 85.20 85.27 85.29 85.32

84.27 84.26 84.26 84.31 84.34 84.37

0.95 0.95 0.94 0.96 0.95 0.95

9.24 9.24 9.40 9.25 9.24 9.24

9.13 9.13 9.29 9.14 9.13 9.13

0.11 0.11 0.11 0.11 0.11 0.11

6.67 6.69 6.70 6.63 6.60 6.57

1.15 1.14 1.13 1.15 1.15 1.14

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T. Paksoy et al. / Transportation Research Part E 47 (2011) 532–546

50%. Similar statistics are given by Srivastava (2008) for various product categories, including televisions, passenger cars, cellular handsets and computers, for which estimated return rates range between 20.6% and 30.7%. A particular case study presented by Davey et al. (2005) quotes a return percentage of 5.7% of units shipped for inkjet printers, whereas another case study by de Koster et al. (2005), looking at large white goods, indicates this might be as high as 85%. To consider this product variety in the return percentage in our analyses, we have performed additional tests with varying armin and armax values, corresponding to minimum and maximum collection rates. Table 5 presents statistics where these values are set relatively high, as in the case of white goods. For this analysis, transportation capacities of the problem instance have been increased to reflect the increments in the collection rates. Table 6 looks at the case where the collection rates are small, as in the case of printers, above, or the auto industry (Rogers and Tibben-Lembke, 1998). Transportation costs of products with a small return percentage in the reverse network are small, as the results in Table 6 show, constituting about 0.18% of the overall cost. On the other hand, the corresponding environmental cost is rather insignificant, with values around 0.02% of the overall cost. As for items with a higher return percentage, the reverse transportation costs increase, as expected. They constitute around 1.54% of the overall costs, and this is a 62% increase as compared to the base scenario. This table also shows a similar increase of 62% over the base scenario on the environmental cost of the reverse logistics network. 4.2.3. Effect of changing profits The third scenario to be investigated in this section pertains to the unit profits of the recyclable products. By problem definition, it is assumed that product profits increase with the recyclability rate of the product, with the assumption that the reusable components will reduce the costs of raw material outsourcing. This is why the Scenario Set 2 given in Table 7 does not include any analysis for the non-reusable product in the data set. As can be seen from this table, the goal of Scenario Set 2 is to study the effect of an increase in the unit profits, from 15% to 60%, in increments of 15%. The results of this experiment are given in Tables 8 and 9 in a manner similar to Tables 3 and 4. Tables 8 and 9 imply that as the profits increase, there is not very much change in the operational and environmental costs of transportation. The total profit PC9 shows an increase between 11% and 46% depending on the nature of the product considered, resulting in a decrease in the total cost value denoted by PC1. Of notable importance, both the values and the

Table 5 Relative importance of performance measures as a fraction of total cost for armin = 0.8, armax = 0.9. Scenario Set 1

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

1 2 3 4 5 6

510184.80 514350.00 518515.20 510161.20 514302.70 518444.20

85.33 85.32 85.30 85.37 85.40 85.42

83.79 83.78 83.77 83.83 83.86 83.89

1.54 1.54 1.53 1.54 1.54 1.53

9.25 9.24 9.24 9.26 9.26 9.25

9.08 9.07 9.07 9.08 9.08 9.08

0.17 0.17 0.17 0.18 0.18 0.17

6.64 6.65 6.66 6.59 6.56 6.53

1.23 1.22 1.21 1.23 1.22 1.21

Table 6 Relative importance of performance measures as a fraction of total cost for armin = 0.04, armax = 0.08. Scenario Set 1

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

1 2 3 4 5 6

507514.20 511641.30 515768.40 507494.20 511601.40 515708.50

84.41 84.39 84.40 84.44 84.47 84.50

84.23 84.22 84.22 84.27 84.30 84.33

0.18 0.17 0.18 0.17 0.17 0.17

9.15 9.14 9.13 9.15 9.15 9.14

9.13 9.12 9.11 9.13 9.13 9.12

0.02 0.02 0.02 0.02 0.02 0.02

6.67 6.69 6.70 6.63 6.59 6.57

0.23 0.23 0.23 0.23 0.22 0.22

Table 7 Scenarios for increasing profits. Scenario Set 2

Unit profit of 100% recyclable product

Unit profit of 50% recyclable product

1 2 3 4 5 6 7 8

+15% +30% +45% +60% +0% +0% +0% +0%

+0% +0% +0% +0% +15% +30% +45% +60%

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T. Paksoy et al. / Transportation Research Part E 47 (2011) 532–546 Table 8 Results for Scenario 2 normalized against the base scenario. Scenario Set 2

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

1 2 3 4 5 6 7 8

1.0015 1.0015 1.0016 1.0016 1.0013 1.0013 1.0012 1.0012

1.0012 1.0012 1.0013 1.0013 1.0013 1.0013 1.0012 1.0012

1.0313 1.0299 1.0286 1.0273 1.0009 1.0006 1.0002 0.9999

1.0000 1.0009 1.0007 1.0005 0.9998 0.9995 0.9992 0.9999

1.0011 1.0020 1.0018 1.0017 1.0020 1.0017 1.0013 1.0021

0.9231 0.9219 0.9207 0.9196 0.8469 0.8466 0.8463 0.8461

1.0015 1.0017 1.0020 1.0022 1.0009 1.0006 1.0017 1.0014

1.1379 1.2398 1.3500 1.4600 1.0268 1.0610 1.0865 1.1119

Table 9 Relative importance of performance measures as a fraction of total cost under Scenario 2. Scenario Set 2

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

1 2 3 4 5 6 7 8

502480.80 501846.20 501211.60 500577.00 502939.10 502768.10 502597.20 502426.20

85.37 85.48 85.59 85.70 85.27 85.30 85.32 85.35

84.38 84.49 84.60 84.71 84.31 84.34 84.36 84.39

0.99 0.99 0.99 0.99 0.96 0.96 0.96 0.96

9.27 9.29 9.30 9.31 9.26 9.26 9.26 9.27

9.15 9.17 9.18 9.19 9.15 9.15 9.15 9.16

0.12 0.12 0.12 0.12 0.11 0.11 0.11 0.11

6.67 6.68 6.69 6.70 6.66 6.66 6.67 6.67

1.32 1.44 1.57 1.70 1.19 1.23 1.26 1.29

relative importance of the forward transportation activities increase with increasing profits. This is mainly due to the forward network being fed by the reverse network with recycled products. 4.2.4. Effect of changing transportation capacities This section presents scenarios looking at changes in transportation capacities on links in the network and their effect on the performance measures. For this purpose, we gradually increase the capacity of a particular truck in increments of 15% and up to 60%, one truck at a time, while the capacities of the remaining trucks are kept constant. The relevant scenarios are presented in Table 10, while the associated results are given in Tables 11 and 12. The results given in Table 11 have a number of implications. First, increasing the transportation capacity of Truck 1 does not seem to have a major impact on the performance measures, except for the slight decrease in PC5 of up to 1%. Similar modifications in capacities of Trucks 2 and 3 yield more significant changes in PC1 and PC5. For the latter, a decrease of up to 4% in operational costs (PC1) may be observed, but this comes at an increase of up to 2% in environmental costs (PC5) when capacities are gradually increased. These results suggest that shifting freight onto operationally less costly (but environmentally more hazardous) means of transportation have double the impact on operational costs as compared to environmental costs. Measures PC8 and PC9 do not change much under the changes in transportation capacity. As for Table 12, the results given therein are similar to those in Tables 3 and 9 in terms of the relative importance of the various performance measures with respect to total cost. 4.2.5. Effect of changing emission rates The last set of scenarios to be investigated is those relevant to changing emission rates of the three different truck types in the sample network instance. To this end, emission rates are increased by units of 100%, up to 400%, for one truck at a time. The relevant scenarios are presented in Table 13 and the results given in Tables 14 and 15. Table 10 Scenarios for increasing transportation capacities. Scenario Set 3

Truck 1 capacity (%)

Truck 2 capacity (%)

Truck 3 capacity (%)

1 2 3 4 5 6 7 8 9 10 11 12

+15 +30 +45 +60 +0 +0 +0 +0 +0 +0 +0 +0

+0 +0 +0 +0 +15 +30 +45 +60 +0 +0 +0 +0

+0 +0 +0 +0 +0 +0 +0 +0 +15 +30 +45 +60

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Table 11 Results for Scenario 2 normalized against the base scenario. Scenario Set 3

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

1 2 3 4 5 6 7 8 9 10 11 12

1.0000 0.9986 0.9977 0.9970 0.9965 0.9918 0.9869 0.9828 0.9905 0.9800 0.9715 0.9639

1.0000 0.9988 0.9977 0.9969 0.9965 0.9917 0.9868 0.9827 0.9905 0.9798 0.9713 0.9636

1.0000 0.9779 0.9975 1.0069 0.9973 1.0037 0.9997 0.9962 0.9928 0.9946 0.9877 0.9918

0.9968 0.9933 0.9899 0.9880 1.0006 1.0009 1.0033 1.0040 1.0057 1.0131 1.0176 1.0229

0.9989 0.9954 0.9920 0.9900 1.0028 1.0031 1.0045 1.0054 1.0080 1.0145 1.0192 1.0247

0.8462 0.8451 0.8440 0.8432 0.8439 0.8405 0.9133 0.9101 0.8401 0.9087 0.9023 0.8968

1.0015 1.0017 1.0019 1.0025 1.0018 1.0023 1.0013 1.0022 1.0018 1.0021 1.0025 1.0022

1.0000 0.9987 0.9975 1.0051 0.9973 1.0019 1.0064 1.0030 1.0014 1.0014 1.0028 1.0051

Table 12 Relative importance of performance measures as a fraction of total cost under Scenario 2. Scenario Set 3

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

1 2 3 4 5 6 7 8 9 10 11 12

502325.60 501685.80 501045.80 500595.60 500990.40 498991.20 496993.00 495272.50 498724.00 494478.80 491038.80 488038.60

85.24 85.23 85.26 85.28 85.17 85.11 85.03 84.97 85.04 84.86 84.71 84.57

84.28 84.29 84.30 84.31 84.21 84.14 84.06 84.00 84.08 83.89 83.74 83.59

0.96 0.94 0.96 0.97 0.96 0.97 0.97 0.97 0.96 0.97 0.97 0.98

9.24 9.22 9.20 9.19 9.30 9.34 9.40 9.44 9.39 9.54 9.65 9.76

9.13 9.11 9.09 9.08 9.19 9.23 9.28 9.32 9.28 9.42 9.53 9.64

0.11 0.11 0.11 0.11 0.11 0.11 0.12 0.12 0.11 0.12 0.12 0.12

6.67 6.68 6.69 6.70 6.69 6.72 6.74 6.77 6.72 6.78 6.83 6.87

1.16 1.16 1.16 1.17 1.16 1.17 1.18 1.18 1.17 1.18 1.19 1.20

Table 13 Scenarios for increasing transportation capacities. Scenario Set 4

Truck 1 emission rate (%)

Truck 2 emission rate (%)

Truck 3 emission rate (%)

1 2 3 4 5 6 7 8 9 10 11 12

+100 +200 +300 +400 +0 +0 +0 +0 +0 +0 +0 +0

+0 +0 +0 +0 +100 +200 +300 +400 +0 +0 +0 +0

+0 +0 +0 +0 +0 +0 +0 +0 +100 +200 +300 +400

Table 14 Results for Scenario 2 normalized against the base scenario. Scenario Set 3

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

1 2 3 4 5 6 7 8 9 10 11 12

0.9858 0.9857 0.9865 0.9888 1.0009 1.0008 1.0008 1.0008 0.9997 1.0179 1.0303 1.0303

0.9858 0.9859 0.9867 0.9889 1.0010 1.0009 1.0010 1.0010 0.9991 1.0175 1.0301 1.0300

0.9896 0.9719 0.9747 0.9763 0.9927 0.9875 0.9815 0.9860 1.0505 1.0540 1.0490 1.0545

1.1370 1.2887 1.4342 1.5560 1.1484 1.3261 1.5055 1.6843 1.2788 1.3565 1.4954 1.6815

1.1400 1.2938 1.4400 1.5633 1.1514 1.3314 1.5121 1.6931 1.2835 1.3620 1.5026 1.6899

0.9231 0.9361 1.0283 1.0414 0.9358 0.9511 1.0470 1.0636 0.9499 0.9730 0.9889 1.0885

0.9865 0.9867 0.9866 0.9851 0.9864 0.9855 0.9857 0.9853 0.9858 0.9860 0.9860 0.9855

0.9828 0.9792 0.9751 0.9786 0.9876 0.9771 0.9748 0.9719 0.9847 0.9723 0.9789 0.9759

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T. Paksoy et al. / Transportation Research Part E 47 (2011) 532–546 Table 15 Relative importance of performance measures as a fraction of total cost under Scenario 2. Scenario Set 3

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

1 2 3 4 5 6 7 8 9 10 11 12

510331.80 517552.90 524750.80 531476.60 517391.20 525851.60 534310.40 542769.20 525149.20 537912.60 546705.90 555499.20

84.03 82.85 81.78 80.93 84.15 82.79 81.48 80.21 82.81 82.32 81.98 80.68

83.08 81.93 80.87 80.03 83.21 81.87 80.58 79.32 81.83 81.36 81.04 79.75

0.95 0.92 0.91 0.90 0.94 0.92 0.90 0.89 0.98 0.96 0.94 0.93

10.54 11.78 12.93 13.85 10.50 11.93 13.33 14.68 11.52 11.93 12.94 14.32

10.42 11.66 12.80 13.72 10.38 11.81 13.20 14.55 11.40 11.81 12.82 14.19

0.12 0.12 0.13 0.13 0.12 0.12 0.13 0.13 0.12 0.12 0.12 0.13

6.57 6.48 6.39 6.30 6.48 6.37 6.27 6.17 6.38 6.23 6.13 6.03

1.14 1.12 1.10 1.09 1.13 1.10 1.08 1.06 1.11 1.07 1.06 1.04

The figures given in Table 14 have the same implications for all types of trucks. In particular, when emission rates are increased to 400%, the total environmental cost increases between 56% and 69%. All other measures of performance remain the same. However, when one looks at the results given in Table 15, the relative importance of environmental costs in the overall cost function is seen to increase at most by about 4.7% relative to the base scenario, which indicates that, even under extreme emission scenarios, the dominance of the operational costs over environmental costs still holds. In fact, a 400% increased emission rate implies only about 4% reduction of the relative importance of PC2 in the total cost figure.

5. Conclusions This paper has considered a closed-loop supply chain network problem, in which the trade-offs between operational and environmental performance measures of shipping products were investigated. Using a realistic network instance as a base case, we have generated and explored a number of scenarios, where the effects on the performance measures of various exogenous parameters to the problem, such as demand, capacity, and emission rates, were investigated. Due to its prominent place in the global agenda, this paper focused on CO2 emissions, and the cost thereof, as the primary measures in assessing a supply chain’s environmental performance. One of the main findings of this paper is that, using realistic estimates, costs of environmental impacts are still not as apparent as operational measures, as far as their relative importance in a total cost function are concerned. Operational costs of handling products, both in forward and reverse networks, seem to be dominant in all scenarios generated, including those where emissions rates are exceptionally high. Another interesting result is relevant to the promotion of reusable products, the use of which seems to lessen the operational costs of the chain, but places a burden on the environmental costs. There is growing literature on design, planning and operation of closed-loop supply chains, and issues such as energy usage, greenhouse gas emissions and climate change have started to emerge within the context (Ferguson and Souza, 2010). This paper contributes to the literature by: (i) considering environmental costs within a closed-loop supply chain network, mainly reflecting those of CO2 emissions due to transporting material in forward and reverse logistics networks, and (ii) using the proposed model and a sample problem instance, we present results of computational experiments that shed light on the interactions of various performance indicators, primarily measured by cost, but nevertheless capturing environmental aspects. The nature of emissions and the complexities surrounding the balancing of cost, reduction and impact make this topic highly controversial. However, ‘‘the integration of environmental policies into the transport sector needs to address several issues, even if climate change is seen as the overriding concern’’ (European Environment Agency, 2009). These uncertainties and complications make policy writing exceptionally hard. However, it is to be hoped that the results presented in this paper will be of help in this respect, both to private organizations and governments around the world. Acknowledgements The authors express their gratitude to the two anonymous reviewers for their valuable comments on the paper. In carrying out this research, the first and the third authors have been supported by the Selçuk University Scientific Research Project Fund (BAP), and the second author has been partially supported by a Pump-Priming grant from the School of Management at the University of Southampton. These funds are hereby gratefully acknowledged.

Appendix A See Tables A1–A9.

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T. Paksoy et al. / Transportation Research Part E 47 (2011) 532–546 Table A1 Distances (miles) in the forward network.

Supplier 1 Supplier 2 Supplier 3 Warehouse Distribution Centre 1 Distribution Centre 2

Plant 1

Plant 2

Plant 3

250 220 190 220 120 170

190 410 290 390 100 190

380 250 310 260 135 200

Table A2 Distances (miles) in the forward network.

Customer 1 Customer 2 Customer 3 Customer 4 Customer 5 Warehouse

Warehouse

Distribution Centre 1

Distribution Centre 2

240 210 190 310 280 –

170 180 210 330 150 140

220 290 200 250 280 110

Table A3 Material handling capacities in the forward network (in tons). Material type

100% Recyclable 50% Recyclable Non-recyclable

Suppliers

Plants

Warehouse

1

2

3

1

2

3

1000 1100 1200

900 1000 1100

1100 1200 1300

950 1050 1150

1150 1150 1050

1250 1150 1250

DC

2200 1900 1800

1

2

1200 1000 1100

1100 900 1100

Table A4 Transportation capacities of each type of truck (in tons). Truck types

From suppliers

From plants

From warehouse

From distribution centres

1 2 3

3200 3400 3100

3500 3700 3400

2200 2100 2300

1800 1700 1900

Table A5 Distances (miles) in the reverse network.

Customer 1 Customer 2 Customer 3 Customer 4 Customer 5 Repairing Centre Dismantler 1 Dismantler 2

Collection Centre 1

Collection Centre 2

6 9 7 11 12 9 9.5 11

8 8.5 10 5 13 8 12 10

Table A6 Distances (miles) in the reverse network. Repairing Centre Warehouse DC 1 DC 2 Disposal Decomposition Centre 1 Decomposition Centre2

Dismantler 1

Dismantler 2

11 16 15

12 13 19

9 8 7

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T. Paksoy et al. / Transportation Research Part E 47 (2011) 532–546 Table A7 Distances (miles) in the reverse network.

Supplier 1 Supplier 2 Supplier 3 Plant 1 Plant 2 Plant 3

Decomposition Centre 1

Decomposition Centre 2

9 7 10 11 12 9

6 9.5 8 7.5 11.5 8

Table A8 Material handling capacities in the reverse network and customer demands (in tons). Material type

100% Recyclable 50% Recyclable Non-recyclable

Collection Centres 1

2

6000 5000 4500

5400 5500 4500

Repairing Centre

4200 4100 4050

Dismantlers

Decomposition Centres

Customers

1

2

1

2

1

2

3

4

5

4000 4500 4600

4500 4800 4000

4600 4800 4700

4600 4800 4700

6000 6700 5900

4000 5700 5900

7000 6700 6900

5000 5700 4900

6000 7700 5900

Table A9 Net profits of suppliers, plants, DCs and the warehouse ($/unit) and raw material purchasing costs ($/unit). Material type

Warehouse

DCs

1

Suppliers 2

3

1

Plants 2

3

1

1

2

Profits ($/unit) 100% Recyclable 50% Recyclable Non-recyclable

3.5 2.7 –

3.2 2.5 –

3.6 2.9 –

4 2.9 –

4.1 3.1 –

4.2 2.7 –

3.7 2.5 1.9

3.9 2.8 1.8

4.1 2.9 1.9

Purchasing costs ($/unit) 100% Recyclable 50% Recyclable Non-recyclable

6 3.2 2.3

6.2 3.5 2

5.8 3 2.1

– – –

– – –

– – –

– – –

– – –

– – –

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