Abstract
Supplier selection is one of the most important components of supply chain management. The main objective of this exercise is to select best suppliers based on different criteria. Those criteria are often closely related with sustainability and are set by the decision-maker. Hence, the objective of this paper is to integrate sustainability and decision-making. The sustainability aspect of the model includes economic, environmental and social dimensions and strategic level of decision-making criteria. The methodology used in this paper is mathematical modeling and the problem is formulated as mixed integer linear programming. The integration of sustainability and supplier selection is presented on an illustrative example of a bio-fuel supply chain. The best multi-objective solution is obtained using the weighting sum method. The study indicates that the integration of several sustainability aspects and supplier selection can be useful for decision making process. The main contribution of this work is the integration of supplier selection with the sustainability of supply chains.
Keywords: Supply chain, Decision-making, Integer linear programming, Sustainability, Supplier selection, Optimization.
Received: 25 January 2019 / Revised: 15 March 2019 / Accepted: 22 April 2019/ Published: 8 July 2019
Contribution/ Originality
The main contribution of this paper is in the field of sustainable supply chain development. The methodology used was mathematical modeling (optimization). The objective was to integrate supplier selection and sustainability of supply chains.
1. INTRODUCTION
Supply chains (SCs) are considered one of the main part in today’s economy and business organizations (Pasandideh et al., 2015) and their goal is to deliver products from front end (supply) to back end (demand) (Tang and Musa, 2011). Additionally, SCs are characterized by forward (open loop SCs) and reverse movement (closed loop SCs) of material (Easwaran and Üster, 2010; Govindan et al., 2015; Pedram et al., 2017). SCs are integrated networks of raw materials suppliers, manufacturers, warehouses, distribution centers, and consumers (Melo et al., 2009) with various aspects of inter-network (within SCs) competition and relations seen from business managements perspective (Lambert and Cooper, 2000; Mentzer et al., 2001). The common interest of all SCs players is most often reflected in terms of economic gains (Carter and Liane Easton, 2011).
The most common measure of a SC performance is its sustainability (Schaltegger and Burritt, 2014) which is approached at three levels, namely: economic, environmental and social (Beske and Seuring, 2014; Joyce and Paquin, 2016; Rajeev et al., 2017). Although, SCs and sustainability have been defined in many ways over the years, the most often used definition of sustainability describes it as: ”development thata meets the needs of the present without compromising the ability of future generations to meet their own needs” (United Nations General Assembly, 1988). While sustainable SC management is defined by Seuring and Müller (2008) as: “the management of material, information and capital flows as well as cooperation among companies along the SC while taking goals from all three dimensions of sustainable development, i.e., economic, environmental and social, into account which are derived from customer and stakeholder requirements”. From a decision-making standpoint, balancing out all three dimensions of sustainability is a real challenge (Seuring, 2013). Additionally, Pagell and Shevchenko (2014) argue that our current level of understanding the subject at hand is not sufficient to create truly sustainable SCs. They listed several major issues and solutions to them.
Supplier selection is a challenging task in SC management (Liao and Kao, 2011) . Hence, supplier selection is important and may have a considerable impact on sustainable development and sustainability of SCs (Banaeian et al., 2018) . According to Ayhan (2013) supplier selection, which consists of several different and conflicting objectives, can be defined as an objective of finding the best supplier with best price at the right quality, at the right time and quantity.
The aim of this analysis is to address the issues of supplier selection and sustainable development of SCs as an integrated problem, following the research question: how can SC sustainability be integrated with supplier selection?
In this work, a mathematical programming model is proposed, which integrates economic, environmental, and social performance of a SC with supplier selection. The model is tested on an illustrative example of a bio-fuel SC.
The remainder of the paper is structured as follows: in the next Section we provide the background of the study, i.e., literature review focusing on SC sustainability and supplier selection. Section 3 defines problems and characteristics of the model, while in Section 4, a detailed model is presented. Results are discussed in Section 5, and conclusions and future remarks are given in the last Section.
2. LITERATURE REVIEW
There are numerous review papers dealing with the issues studied in this work, some of the most prominent reviews dealing with supplier selection include: De Boer et al. (2001); Ho et al. (2010) and Govindan et al. (2015). Amongst papers dealing with sustainable SCs we may mention: Seuring and Müller (2008); Seuring (2013) and Mujkić et al. (2018). However, for this work we have selected and classified papers based on the criteria listed below.
The papers reviewed in this study are classified based on two categories: (I) papers focused on sustainable development as an objective which deals with one or more aspects of sustainability (economic, environmental or social), and (II) papers presenting supplier selection as a decision-making process. Additionally, mathematical modeling was the methodology used in all papers.
2.1. Supply Chains and Sustainability
The objective of a SC is to organize the flow of raw materials from extraction to the final product and the delivery to the consumer. However, sustainability has extended it to cover the consideration of product design, manufacturing, by-products, and product life cycle from cradle to grave (Linton et al., 2007). Table 1 presents papers classified based on the mathematical model and explored aspects of sustainability.
You and Grossmann (2008) proposed a MINLP optimization model for a SC network design with three-echelons and stochastic inventory management. In the model, the authors consider only the economic dimension of sustainability, more specifically, minimizing total costs (facilities, distribution centers, and transportation) and inventory. The model was tested on several examples. Alfonso et al. (2009) developed a model to optimize biomass facility location as to the costs and CO2 reduction, i.e. with respect to economic and environmental criteria. Elhedhli and Merrick (2012) studied a SC network design, which takes into account fixed and variable location and production costs, together with CO2 emissions. Liu and Papageorgiou (2013) proposed a mixed integer linear programming (MILP) model in which total cost, flow time and lost demand are set as objective functions. Similarly, Mota et al. (2015) proposed a multi-objective MILP model to design a SC network structure. The model addressed all the aspects of sustainability with the focus on newly created jobs as the social aspect. The model was tested on a Portugal battery factory. A multi-objective linear programming model was proposed by Banasik et al. (2017b) to minimize the flow of materials in an agri-food mushroom closed loop SC. Varsei and Polyakovskiy (2017a) simultaneously considered all dimensions of sustainability and developed a multi-objective model to find the best network configuration, while minimizing costs, greenhouse gas emission and maximizing the social impact.
Table-1. Review of previous literature on supplier selection.
| Article | Methodology |
Sustainability dimension |
||
Eco |
Env |
Soc |
||
| You and Grossmann (2008) | MINLP |
● |
||
| Alfonso et al. (2009) | LP |
● |
● |
|
| Elhedhli and Merrick (2012) | LP |
● |
● |
|
| Liu and Papageorgiou (2013) | MILP |
● |
||
| Mota et al. (2015) | MILP |
● |
● |
● |
| Banasik et al. (2017a) | MILP |
● |
● |
|
| Varsei and Polyakovskiy (2017b) | MILP |
● |
● |
● |
| Chávez et al. (2018) | MILP |
● |
● |
● |
| Alavidoost et al. (2018) | MINLP |
● |
||
| How (2019b) | MILP |
● |
● |
● |
Source: Author’s Research.
A bio-fuel SC with coffee as raw material was proposed by Chávez et al. (2018) where net present value (economy), gray water footprint and CO2 emission (environment), jobs created and food security (society) are considered as objective functions. A three echelon SC model to determine optimal service level and minimize total costs was proposed by Alavidoost et al. (2018). How (2019a) presented a MILP model of palm biofuel with simultaneous consideration of economic, environmental and social criteria.
2.2. Supplier Selection and Sustainability
Supplier selection is an important decision-making process in SC management and it presents an effective step towards more sustainable development of SCs (Kumar et al., 2014). Sustainability aspects should be included in the supplier selection criteria from economic, environmental and social perspective (Bai and Sarkis, 2010). Integration of sustainability and supplier selection leads to increased complexity of decision-making process. However, integration may produce an overall better performance of a SC (Luthra et al., 2017).
Ng (2008) developed a generic linear model for the selection of the best supplier based on different criteria. The supply chain vs. only buyer’s benefit was addressed by Kheljani et al. (2009) in terms of several different annual costs. Rezaei and Davoodi (2011) considered a MINLP model with three objective functions (total cost, quality and service level) as decision variables. Sawik (2014) proposed a mathematical model with single and dual sourcing as a decision variable. In the model objective functions are set as the minimization of total costs or maximization of the service level. An integration of sustainable development of a SC and supplier selection was addressed by Azadnia et al. (2015). In the model several conflicting objective functions were formulated for total cost minimization and maximization of economic, environmental and social performance. Hashim et al. (2017) introduced multi-objective fuzzy model to address several issues including economic, environmental and social aspects. In Table 2 presents some of the works done in the fields of sustainable development of supply chains.
Table-2. Review of previous studies on supply chain sustainability.
| Article | Methodology |
Sustainability dimension |
||
Eco |
Env |
Soc |
||
| Ng (2008) | LP |
● |
||
| Kheljani et al. (2009) | MINLP |
● |
||
| Rezaei and Davoodi (2011) | MINLP |
● |
||
| Sawik (2014) | MILP |
● |
||
| Azadnia et al. (2015) | FUZZY |
● |
● |
● |
| Hashim et al. (2017) | FUZZY |
● |
● |
|
| Cheraghalipour and Farsad (2018) | MILP |
● |
● |
● |
| Nourmohamadi et al. (2018) | MILP |
● |
● |
● |
| Rad and Nahavandi (2018) | MILP |
● |
● |
|
| Tosarkani and Amin (2018) | MILP |
● |
● |
|
Source: Author’s Research.
A bi-objective model was proposed by Cheraghalipour and Farsad (2018) the aim of the model is to minimize total costs and maximize total economic, environmental and social scores. Similarly, Nourmohamadi et al. (2018) developed a MILP model with the intention to meet four objectives: to deal with total costs, economic, environmental, and social issues. A MILP model was proposed by Rad and Nahavandi (2018) to minimize costs and emission, to maximize consumer satisfaction rate, and to carry out supplier selection based on several different criteria. Tosarkani and Amin (2018) considered a MILP to maximize profit, environmental score and on-time delivery within a closed-loop SCs and minimize defect rates in reverse logistics.
3. PROBLEM DEFINITION
The aim of this work is to develop a MILP optimization model for biofuels SC by considering and integrating economic, environmental and social aspects of sustainability with supplier selection as decision-making criteria. The model is formulated as a multi-objective problem with cost minimization (transportation and purchasing costs), minimization of environmental impact (CO2 emissions) and maximization of social benefits (created jobs). Furthermore, the model considers sustainable supplier selection, considering economic, environmental and social scores. The scores are given to the following echelons: raw material extraction, manufacturer, and retailer. The criteria used here are related to economic, environmental and social performance of the SC. With that in mind, the objective of this work is to minimize the total cost, while maximizing environmental and social aspects of a biofuel SC, and considering sustainability aspects of SCs and supplier selection. Based on that the following is given:
- Flows of raw material, and final products purchased.
- Flows of biofuel produced and capacities of each echelon.
- Transportation and purchasing cost of raw material and biofuels.
- CO2 emissions related to transportation and production of biofuels.
The problem and the SC defined above is a multi-echelon structure, which includes: raw material, manufacturing, retailers and consumers as shown on Figure 1.
3.1. Supplier Selection Criteria
Most often, supplier selection criteria are based on quantitative performance indicators for quality, delivery, speed and price (Rezaei et al., 2016). However, growing awareness of SC impact on environment and society, is pressing on organizations to reconsider their business models and include other criteria into sustainability and supplier selection process (Luthra et al., 2017). This paper covers all three dimensions of sustainability (economic, environmental and social) (Qorri et al., 2018). Furthermore, the indicators used in this work have been selected and agreed by the authors as indicators the most commonly used in literature (Mata et al., 2013; Kumar Kar and K. Pani, 2014; Zimmer et al., 2016) and a standard framework (Global Reporting Initiative GRI, 2014). The criteria and sub-criteria used in the model are presented in Table 3.
Figure-1. Simplified representation of the supply chain (adopted from Babazadeh et al. (2017)).
Table-3. Sustainability criteria and sub-criteria used in the model.
| Criteria | Economic |
Environmental |
Social |
Sub-criteria |
|||
Cost |
Transportation cost Purchasing cost |
- |
- |
Environmental impact |
- |
CO2 Emission |
- |
Employment |
- |
- |
Jobs created |
Source: Author’s Research.
3.2. Model Formulation
The indices, parameters and variables used in the model formulation are given and described in Appendix A. While, the models constrains, equations and objective functions are listed below:
Objective functions:
3.2.1. Economic Objective Function
Economic aspects of supply chain are presented in the Equation 1. The cost objective function of the MILP model consists of transportation and purchasing cost:
3.3. Weighted Sum Method
The proposed mathematical model has three conflicting objectives, which must be met using a Pareto optimal set. A solution to the problem is defined as the best possible solution of the conflicting objective functions, which means that the solution cannot be improved by any other method (Mavrotas, 2009). In this work the Pareto optimal solution for economic (total cost minimization) environmental (minimization of CO2 emission), and social objective functions (maximization of jobs created) are proposed.
The weight sum of all the objective function is given in the Equation 8. Sum of weights is expressed as:
4. COMPUTATIONAL RESULTS AND DISCUSSIONS
In this part we discuss the model as illustrated by an example. As mentioned above, the model is formulated as a MILP problem. The linearity of the model comes from the input (production) and output (delivery) capacity of each echelon in the SC network structure and the distance between the two mentioned echelons. The mixed integer part of the model comes from the supplier selection based on different criteria as presented in Table 3. The model is solved in two scenarios and several sub-scenarios. Scenarios are divided based on the weight of each sustainability dimension; results of the multi-objective optimization of the problem are presented in Table 4 and Table 4. Results for multi-objective scenario.
4.1. Data Description of the Problem
This paper presents a multi-echelon SC network design composed of: suppliers, manufacturers, retailers and customers, as shown in Figure 1. We assume that material is transported between the echelons using road transport (trucking) and the cost of transport is expressed in €/km. According to Hooper and Murray (2018) operational cost of trucking in 2017 was 1.691 $/mile or 0,9198 €/km.
4.2. Multi-Objective Optimization
An illustrative example is presented to analyze the mathematical model. The SC is presented as an open-loop network structure with four types of echelons, namely; raw material production, biofuel production, retailer, and consumer. Each echelon in the network structure has a unique role to play in the SC.
Table-4. Results for multi-objective scenario.
Scenario |
Weights |
Objective function values |
||||
1 |
Eco |
Env |
Soc |
Total cost |
CO2 emission |
Jobs created |
2 |
1 |
0 |
0 |
10208200 |
102595 |
8678 |
3 |
0.6 |
0.4 |
0 |
10208200 |
102595 |
8678 |
4 |
0.6 |
0.2 |
0.2 |
10208200 |
102595 |
8678 |
5 |
0.6 |
0 |
0.4 |
10208200 |
102595 |
8678 |
6 |
0,3333 |
0.3333 |
0.3333 |
10208200 |
102595 |
8678 |
7 |
0.2 |
0.8 |
0 |
10607200 |
98403 |
8421 |
8 |
0.2 |
0 |
0.8 |
10607200 |
98403 |
8421 |
9 |
0.2 |
0.4 |
0.4 |
10607200 |
98403 |
8421 |
10 |
0.2 |
0.2 |
0.6 |
10809700 |
100658 |
8175 |
11 |
0.2 |
0 |
0.8 |
11961200 |
112182 |
7295 |
12 |
0 |
1 |
0 |
10607200 |
98403 |
8421 |
13 |
0 |
0 |
1 |
11961200 |
112182 |
7295 |
Source: Author’s Research.
Table-5. Optimal network structure scenario of the supply chain.
Scenario |
Raw material |
Factories |
Retailer |
1 |
1,2 |
1,2,3 |
1,2,4 |
6 |
1,2 |
1,2,3 |
1,3,4 |
12 |
1,2 |
1,2,3 |
1,2,3,4 |
13 |
1,2 |
1,2,3 |
1,2,3,4 |
Source: Author’s Research.
A more detailed analysis of the results from Table 4 and Table 5. Results for multi-objective scenario is presented below:
The first case (scenario 1) of the multi-objective optimization problem comprises two raw material production plants, two manufacturing factories, and three retailer units. The total cost is € 10,208,200, CO2 emission is 102,595 and 8,678 jobs created. The second case (scenario 6) of the multi-objective optimization problem includes two raw material production plants, two manufacturing factories, and three retailer units. The total cost is €10,208,200, CO2 emission is 102,595 and the number of jobs created is 8,678. The third scenario of the multi-objective optimization problem was developed for two raw material production plants, two manufacturing factories, and four retailer units. The total cost is €10,607,200, CO2 emission is 98,403 and the number of jobs created is 8,421. The fourth scenario of the multi-objective optimization problem includes two raw material production plants, two manufacturing factories, and four retailer units. The total cost for this scenario is €11,961,200, CO2 emission is 112,182 and the number of jobs created is 7,295.
5. CONCLUSIONS FOR FUTURE RESEARCH DIRECTIONS
The importance of sustainability and supplier selection has gained significant attention from academia and practitioners in the recent years. Having said that, the objective of this paper was to show how supplier selection may improve the overall supply chain sustainability The model used in our study was formulated as a multi-echelon, multi-supplier and multi-objective MILP model. In the model the weighting sum was used in the decision-making process to set the weight of sustainability criteria and sub-criteria. The model considers all three aspects of sustainability: economic, environmental and social, together with supplier selection. The economic objective function in the model includes the costs of transportation and purchasing, the environmental objective function is to minimize CO2 emissions, and the social objective function is to maximize the number of jobs created.
Drawbacks identified while conducting this study indicate several possible future research directions that can be recommended to go more in-depth into the issue. First, the model was tested on an illustrative example, while a real-life case would help better demonstrate the combined impact of sustainability and supplier selection. Second, the model takes account of only six sub-criteria, however, with higher number of sub-criteria the overall impact of sustainability and supplier selection could lead to deeper implications. Third, the number of objective functions can be increased to better describe the biofuels SC.
| Funding: This study received no specific financial support. |
| Competing Interests: The authors declare that they have no competing interests. |
| Acknowledgement: The authors are grateful to the Finish Cultural Foundation for financial support during the research study carried out for this paper. |
REFERENCES
Alavidoost, M., M. Tarimoradi and M.F. Zarandi, 2018. Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks. Journal of Intelligent Manufacturing, 29(4): 809-826.Available at: https://doi.org/10.1007/s10845-015-1130-9.
Alfonso, D., C. Perpiñá, A. Pérez-Navarro, E. Peñalvo, C. Vargas and R. Cárdenas, 2009. Methodology for optimization of distributed biomass resources evaluation, management and final energy use. Biomass and Bioenergy, 33(8): 1070-1079.Available at: https://doi.org/10.1016/j.biombioe.2009.04.002.
Ayhan, M.B., 2013. A fuzzy AHP approach for supplier selection problem: A case study in a Gear motor company. arXiv preprint arXiv:1311.2886.
Azadnia, A.H., M.Z.M. Saman and K.Y. Wong, 2015. Sustainable supplier selection and order lot-sizing: An integrated multi-objective decision-making process. International Journal of Production Research, 53(2): 383-408.Available at: https://doi.org/10.1080/00207543.2014.935827.
Babazadeh, R., J. Razmi, M. Rabbani and M.S. Pishvaee, 2017. An integrated data envelopment analysis–mathematical programming approach to strategic biodiesel supply chain network design problem. Journal of Cleaner Production, 147: 694-707.Available at: https://doi.org/10.1016/j.jclepro.2015.09.038.
Bai, C. and J. Sarkis, 2010. Integrating sustainability into supplier selection with grey system and rough set methodologies. International Journal of Production Economics, 124(1): 252-264.Available at: https://doi.org/10.1016/j.ijpe.2009.11.023.
Banaeian, N., H. Mobli, B. Fahimnia, I.E. Nielsen and M. Omid, 2018. Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers & Operations Research, 89: 337-347.Available at: https://doi.org/10.1016/j.cor.2016.02.015.
Banasik, A., A. Kanellopoulos, G.D.H. Claassen, J.M. Bloemhof-Ruwaard and J.G.A.J. van der Vorst, 2017a. Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, 183: 409-420.Available at: https://doi.org/10.1016/j.ijpe.2016.08.012.
Banasik, A., A. Kanellopoulos, G.D.H. Claassen, J.M. Bloemhof-Ruwaard and J.G.A.J. van der Vorst, 2017b. Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, 183: 409-420.Available at: https://doi.org/10.1016/j.ijpe.2016.08.012.
Beske, P. and S. Seuring, 2014. Putting sustainability into supply chain management. Supply Chain Management: An International Journal, 19(3): 322-331.
Carter, C.R. and P. Liane Easton, 2011. Sustainable supply chain management: Evolution and future directions. International Journal of Physical Distribution & Logistics Management, 41(1): 46-62.Available at: https://doi.org/10.1108/09600031111101420.
Chávez, M.M.M., W. Sarache and Y. Costa, 2018. Towards a comprehensive model of a biofuel supply chain optimization from coffee crop residues. Transportation Research Part E: Logistics and Transportation Review, 116: 136-162.Available at: https://doi.org/10.1016/j.tre.2018.06.001.
Cheraghalipour, A. and S. Farsad, 2018. A bi-objective sustainable supplier selection and order allocation considering quantity discounts under disruption risks: A case study in plastic industry. Computers & Industrial Engineering, 118: 237-250.Available at: https://doi.org/10.1016/j.cie.2018.02.041.
De Boer, L., E. Labro and P. Morlacchi, 2001. A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management, 7(2): 75-89.Available at: https://doi.org/10.1016/s0969-7012(00)00028-9.
Easwaran, G. and H. Üster, 2010. A closed-loop supply chain network design problem with integrated forward and reverse channel decisions. Lie Transactions, 42(11): 779-792.
Elhedhli, S. and R. Merrick, 2012. Green supply chain network design to reduce carbon emissions. Transportation Research Part D: Transport and Environment, 17(5): 370-379.Available at: https://doi.org/10.1016/j.trd.2012.02.002.
Global Reporting Initiative GRI, 2014. Sustainability reporting guidelines. Boston, USA: Global Reporting Initiative.
Govindan, K., S. Rajendran, J. Sarkis and P. Murugesan, 2015. Multi criteria decision making approaches for green supplier evaluation and selection: A literature review. Journal of Cleaner Production, 98: 66-83.Available at: https://doi.org/10.1016/j.jclepro.2013.06.046.
Govindan, K., H. Soleimani and D. Kannan, 2015. Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3): 603-626.Available at: https://doi.org/10.1016/j.ejor.2014.07.012.
Hashim, M., M. Nazam, L. Yao, S.A. Baig, M. Abrar and M. Zia-ur-Rehman, 2017. Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment. Journal of Industrial Engineering and Management, 10(2): 188-212.Available at: https://doi.org/10.3926/jiem.2078.
Ho, W., X. Xu and P.K. Dey, 2010. Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1): 16-24.Available at: https://doi.org/10.1016/j.ejor.2009.05.009.
Hooper, A. and D. Murray, 2018. An analysis of the operational costs of trucking: 2018 update.
How, B.S., 2019a. An overview of palm biomass supply chain modelling. In: Green technologies for the oil palm industry. Springer. pp: 131-172.
How, B.S., 2019b. An overview of palm biomass supply chain modelling. In Green Technologies for the Oil Palm Industry. Springer. pp: 131-172.
Joyce, A. and R.L. Paquin, 2016. The triple layered business model canvas: A tool to design more sustainable business models. Journal of Cleaner Production, 135: 1474-1486.Available at: https://doi.org/10.1016/j.jclepro.2016.06.067.
Kheljani, J.G., S. Ghodsypour and C. O’Brien, 2009. Optimizing whole supply chain benefit versus buyer's benefit through supplier selection. International Journal of Production Economics, 121(2): 482-493.Available at: https://doi.org/10.1016/j.ijpe.2007.04.009.
Kumar, A., V. Jain and S. Kumar, 2014. A comprehensive environment friendly approach for supplier selection. Omega, 42(1): 109-123.Available at: https://doi.org/10.1016/j.omega.2013.04.003.
Kumar Kar, A. and A. K. Pani, 2014. Exploring the importance of different supplier selection criteria. Management Research Review, 37(1): 89-105.Available at: https://doi.org/10.1108/mrr-10-2012-0230.
Lambert, D.M. and M.C. Cooper, 2000. Issues in supply chain management. Industrial Marketing Management, 29(1): 65-83.
Liao, C.-N. and H.-P. Kao, 2011. An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Systems with Applications, 38(9): 10803-10811.Available at: https://doi.org/10.1016/j.eswa.2011.02.031.
Linton, J.D., R. Klassen and V. Jayaraman, 2007. Sustainable supply chains: An introduction. Journal of Operations Management, 25(6): 1075-1082.
Liu, S. and L.G. Papageorgiou, 2013. Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry. Omega, 41(2): 369-382.Available at: 10.1016/j.omega.2012.03.007.
Luthra, S., K. Govindan, D. Kannan, S.K. Mangla and C.P. Garg, 2017. An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140: 1686-1698.Available at: https://doi.org/10.1016/j.jclepro.2016.09.078.
Mata, T.M., N.S. Caetano, C.A. Costa, S.K. Sikdar and A.A. Martins, 2013. Sustainability analysis of biofuels through the supply chain using indicators. Sustainable Energy Technologies and Assessments, 3: 53-60.Available at: https://doi.org/10.1016/j.seta.2013.06.001.
Mavrotas, G., 2009. Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213(2): 455-465.Available at: https://doi.org/10.1016/j.amc.2009.03.037.
Melo, M.T., S. Nickel and F. Saldanha-Da-Gama, 2009. Facility location and supply chain management–a review. European Journal of Operational Research, 196(2): 401-412.Available at: https://doi.org/10.1016/j.ejor.2008.05.007.
Mentzer, J.T., W. DeWitt, J.S. Keebler, S. Min, N.W. Nix, C.D. Smith and Z.G. Zacharia, 2001. Defining supply chain management. Journal of Business Logistics, 22(2): 1-25.
Mota, B., M.I. Gomes, A. Carvalho and A.P. Barbosa-Povoa, 2015. Towards supply chain sustainability: Economic, environmental and social design and planning. Journal of Cleaner Production, 105: 14-27.Available at: https://doi.org/10.1016/j.jclepro.2014.07.052.
Mujkić, Z., A. Qorri and A. Kraslawski, 2018. Sustainability and optimization of supply chains. Operations and Supply Chain Management: An International Journal, 11(4): 186 - 199.
Ng, W.L., 2008. An efficient and simple model for multiple criteria supplier selection problem. European Journal of Operational Research, 186(3): 1059-1067.Available at: https://doi.org/10.1016/j.ejor.2007.01.018.
Nourmohamadi, S.P., M.M. Paydar and M. Hajiaghaei-Keshteli, 2018. Sustainable supplier selection and order allocation through quantity discounts. International Journal of Management Science and Engineering Management, 13(1): 20-32.
Pagell, M. and A. Shevchenko, 2014. Why research in sustainable supply chain management should have no future. Journal of Supply Chain Management, 50(1): 44-55.Available at: https://doi.org/10.1111/jscm.12037.
Pasandideh, S.H.R., S.T.A. Niaki and K. Asadi, 2015. Optimizing a bi-objective multi-product multi-period three echelon supply chain network with warehouse reliability. Expert Systems with Applications, 42(5): 2615-2623.Available at: https://doi.org/10.1016/j.eswa.2014.11.018.
Pedram, A., N.B. Yusoff, O.E. Udoncy, A.B. Mahat, P. Pedram and A. Babalola, 2017. Integrated forward and reverse supply chain: A tire case study. Waste Management, 60: 460-470.Available at: https://doi.org/10.1016/j.wasman.2016.06.029.
Qorri, A., Z. Mujkić and A. Kraslawski, 2018. A conceptual framework for measuring sustainability performance of supply chains. Journal of Cleaner Production, 189: 570-584.Available at: https://doi.org/10.1016/j.jclepro.2018.04.073.
Rad, R.S. and N. Nahavandi, 2018. A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. Journal of Cleaner Production, 196: 1549-1565.Available at: https://doi.org/10.1016/j.jclepro.2018.06.034.
Rajeev, A., R.K. Pati, S.S. Padhi and K. Govindan, 2017. Evolution of sustainability in supply chain management: A literature review. Journal of Cleaner Production, 162: 299-314.Available at: https://doi.org/10.1016/j.jclepro.2017.05.026.
Rezaei, J. and M. Davoodi, 2011. Multi-objective models for lot-sizing with supplier selection. International Journal of Production Economics, 130(1): 77-86.
Rezaei, J., T. Nispeling, J. Sarkis and L. Tavasszy, 2016. A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135: 577-588.Available at: https://doi.org/10.1016/j.jclepro.2016.06.125.
Sawik, T., 2014. Joint supplier selection and scheduling of customer orders under disruption risks: Single vs. Dual sourcing. Omega, 43: 83-95.Available at: https://doi.org/10.1016/j.omega.2013.06.007.
Schaltegger, S. and R. Burritt, 2014. Measuring and managing sustainability performance of supply chains: Review and sustainability supply chain management framework. Supply Chain Management: An International Journal, 19(3): 232-241.Available at: https://doi.org/10.1108/scm-02-2014-0083.
Seuring, S., 2013. A review of modeling approaches for sustainable supply chain management. Decision Support Systems, 54(4): 1513-1520.Available at: https://doi.org/10.1016/j.dss.2012.05.053.
Seuring, S. and M. Müller, 2008. From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15): 1699-1710.Available at: https://doi.org/10.1016/j.jclepro.2008.04.020.
Tang, O. and S.N. Musa, 2011. Identifying risk issues and research advancements in supply chain risk management. International Journal of Production Economics, 133(1): 25-34.Available at: https://doi.org/10.1016/j.ijpe.2010.06.013.
Tosarkani, B.M. and S.H. Amin, 2018. A multi-objective model to configure an electronic reverse logistics network and third party selection. Journal of Cleaner Production, 198: 662-682.Available at: https://doi.org/10.1016/j.jclepro.2018.07.056.
United Nations General Assembly, 1988. The brundtland report:‘Our common future. Medicine and War, 4(1): 17-25.
Varsei, M. and S. Polyakovskiy, 2017a. Sustainable supply chain network design: A case of the wine industry in Australia. Omega, 66: 236-247.Available at: https://doi.org/10.1016/j.omega.2015.11.009.
Varsei, M. and S. Polyakovskiy, 2017b. Sustainable supply chain network design: A case of the wine industry in Australia. Omega, 66: 236-247.Available at: https://doi.org/10.1016/j.omega.2015.11.009.
You, F. and I.E. Grossmann, 2008. Mixed-integer nonlinear programming models and algorithms for large-scale supply chain design with stochastic inventory management. Industrial & Engineering Chemistry Research, 47(20): 7802-7817.Available at: https://doi.org/10.1021/ie800257x.
Zimmer, K., M. Fröhling and F. Schultmann, 2016. Sustainable supplier management–a review of models supporting sustainable supplier selection, monitoring and development. International Journal of Production Research, 54(5): 1412-1442.Available at: https://doi.org/10.1080/00207543.2015.1079340.
Appendix A
Indices Description
Parameters
Variables:
Appendix B
The above given model was solved using General Algebraic Modeling System (GAMS) version 24.2.3, and BARON was used as a solver, the problems were solved on an Intel i7 CPU (3.50 GHz) computer with 8 GB of RAM where the results of single-objective optimization are presented. The model is solved for three scenarios, namely: economic, environmental and social aspects of SCs. Additionally, Table 4 presents the results of multi-objective optimization.
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