A Novel Robust Fuzzy Programming Approach for Closed-loop Supply Chain Network Design

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Operation and Production Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

2 Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

3 Associate Prof., Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

Abstract

Objective: Attention to environmental issues in supply chain activities has been taken into consideration due to the increase in public awareness and strict laws related to environmental protection. Initially, only the economic aspects of the supply chain were considered in the network configuration, but with increasing concerns about environmental issues, reverse logistics and closed-loop supply chains were developed. Designing a closed-loop supply chain network plays an important role in reducing costs, improving service levels, and responding to environmental issues. Therefore, the purpose of this study is to design a closed-loop supply chain network taking into account hybrid uncertainties and flexibility in constraints.
Methods: In most of the conducted studies about supply chain network design, the types of cognitive and random uncertainties, as well as the flexibility of soft constraints, have not been investigated simultaneously, while the conducted modeling is not able to consider hybrid uncertainty in supply chain parameters in the real world. In this study, to simultaneously consider the hybrid uncertainties and flexibility in constraints, a novel model of robust stochastic, possibilistic, and flexible programming based on Me measurement was developed. In this model, the convex combination of optimistic and pessimistic attitudes of decision-makers was considered in the form of the Me measure, and the modeling was more flexible and realistic.
Results: In the proposed approach, a convex combination of optimistic and pessimistic spectra was considered in the model. The need for subjective and repetitive reviews by decision-makers was eliminated in the model and the level of satisfaction was calculated optimally after solving the problem. On the other hand, due to the robustness of the model, possible deviations, scenario deviations, non-fulfillment of demand and capacity, and deviations of soft constraints were minimized. In the proposed approach based on the Me measure, the problem-solving approach was reduced and there was no need for a two-step solution to find solutions.
Conclusion: A case study was conducted in the supply chain of stone paper production to evaluate the efficiency of the proposed model. The results of sensitivity analysis, robustness analysis, and simulation with the realization model showed that the proposed model was able to provide robust and realistic solutions. The proposal of a realistic and flexible solution for designing problems of the supply chain network by creating a trade-off between the objective function and the risk-taking level of decision-makers and managers through changing the justified space in the Me criterion in the proposed approach was one of the achievements of the present study. As its other achievement, the present study could provide a combination of different viewpoints of decision-makers’ risk-taking through changing the justified space based on different values of the parameter λ in measuring Me and propose flexible and realistic solutions according to the results of numerical simulation in the proposed approach.

Keywords

References

Aieneh Vand, S., & Gholamian, M. (2020). A location-inventory model of blood products (platelet) in the blood supply chain based on the EOQ ordering system. Industrial Management Journal, 12(4), 609-633. (in Persian)
Amiri, M., Hosseini Dehshiri, S. J., & Yousefi Hanoomarvar, A. (2018). Determining the optimal combination of LARG supply chain strategies using SWOT analysis, multi-criteria decision-making techniques and game theory. Industrial Management Journal, 10(2), 221-246. (in Persian)
Atabaki, M. S., Mohammadi, M., & Naderi, B. (2020). New robust optimization models for closed-loop supply chain of durable products: Towards a circular economy. Computers & Industrial Engineering, 146, 106520.
Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227(1), 199-215.
Boronoos, M., Mousazadeh, M., & Torabi, S. A. (2021). A robust mixed flexible-possibilistic programming approach for multi-objective closed-loop green supply chain network design. Environment, Development and Sustainability, 23(3), 3368-3395. doi:10.1007/s10668-020-00723-z
Carlsson, C., & Fullér, R. (2001). On possibilistic mean value and variance of fuzzy numbers. Fuzzy sets and systems, 122(2), 315-326.
Dehghan, E., Nikabadi, M. S., Amiri, M., & Jabbarzadeh, A. (2018). Hybrid robust, stochastic and possibilistic programming for closed-loop supply chain network design. Computers & Industrial Engineering, 123, 220-231.
Devika, K., Jafarian, A., & Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques. European Journal of Operational Research, 235(3), 594-615.
Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2018). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy sets and systems, 341, 69-91.
Fazli-Khalaf, M., Khalilpourazari, S., & Mohammadi, M. (2019). Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of operations research, 283(1), 1079-1109.
Gaur, J., Amini, M., & Rao, A. (2017). Closed-loop supply chain configuration for new and reconditioned products: An integrated optimization model. Omega, 66, 212-223.
Ghahremani Nahr, J., Kian, R., & Sabet, E. (2019). A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert systems with applications, 116, 454-471.
Gilani, H., & Sahebi, H. (2021). Optimal Design and Operation of the green pistachio supply network: A robust possibilistic programming model. Journal of Cleaner Production, 282, 125212.
Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 263(1), 108-141.
Govindan, K., Jafarian, A., & Nourbakhsh, V. (2015). Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic. Computers & Operations Research, 62, 112-130.
Günay, E. E., Kremer, G. E. O., & Zarindast, A. (2020). A multi-objective robust possibilistic programming approach to sustainable public transportation network design. Fuzzy sets and systems, 422, 106-129.
Habib, M. S., Asghar, O., Hussain, A., Imran, M., Mughal, M. P., & Sarkar, B. (2021). A robust possibilistic programming approach toward animal fat-based biodiesel supply chain network design under uncertain environment. Journal of Cleaner Production, 278, 122403.
Hosseini Dehshiri, S. J., Amiri, M., Olfat, L., & Pishvaee, M. S. (2022). Stone Paper Closed-Loop Supply Chain Network Design using Robust Stochastic, Possibilistic and Flexible Chance-constrained Programming. Journal of Industrial Management Perspective, 12(1, Spring 2022), 45-81. (in Persian)
Hosseini Dehshiri, S. J., Amiri, M., Olfat, L., & Pishvaee, M. S. (2022). Multi-objective closed-loop supply chain network design: A novel robust stochastic, possibilistic, and flexible approach. Expert Systems with Applications, 206, 117807. https://doi.org/10.1016/j.eswa.2022.117807
Hosseini-Motlagh, S.-M., Samani, M. R. G., & Cheraghi, S. (2020). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-economic planning sciences, 70, 100725.
Inuiguchi, M., & Ramı́k, J. (2000). Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy sets and systems, 111(1), 3-28.
Khalili, S., Pooya, A., Kazemi, M., & Fakoor Saghih, A. (2022). Designing a Sustainable and Resilient Gasoline Supply Chain Network under Uncertainty (Case study: Gasoline Supply Chain Network of Khorasan Razavi Province). Industrial Management Journal, 14(1), 27-79. (in Persian)
Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: a critical review. European Journal of Operational Research, 203(2), 283-293.
Liu, B., & Liu, Y.-K. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE transactions on Fuzzy Systems, 10(4), 445-450.
Liu, Y., Ma, L., & Liu, Y. (2021). A novel robust fuzzy mean-UPM model for green closed-loop supply chain network design under distribution ambiguity. Applied Mathematical Modelling, 92, 99-135. doi:https://doi.org/10.1016/j.apm.2020.10.042
Mohammed, F., Selim, S. Z., Hassan, A., & Syed, M. N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transportation Research Part D: Transport and Environment, 51, 146-172.
Momeni, M., & Zereshki, N. (2021). Modeling of Closed-Loop Supply Chains by Utilizing Scenario-Based Approaches in Facing Uncertainty in Quality and Quantity of Returns. Industrial Management Journal, 13(1), 105-130. (in Persian)
Mousazadeh, M., Torabi, S. A., & Zahiri, B. (2015). A robust possibilistic programming approach for pharmaceutical supply chain network design. Computers & Chemical Engineering, 82, 115-128.
Mousazadeh, M., Torabi, S. A., Pishvaee, M. S., & Abolhassani, F. (2018). Health service network design: a robust possibilistic approach. International transactions in operational research, 25(1), 337-373.
Mousazadeh, M., Torabi, S. A., Pishvaee, M., & Abolhassani, F. (2018). Accessible, stable, and equitable health service network redesign: A robust mixed possibilistic-flexible approach. Transportation Research Part E: Logistics and Transportation Review, 111, 113-129.
Pishvaee, M. S., & Khalaf, M. F. (2016). Novel robust fuzzy mathematical programming methods. Applied Mathematical Modelling, 40(1), 407-418.
Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649.
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1-20.
Ren, A. (2018). Solving the General Fuzzy Random Bilevel Programming Problem Through $ Me $ Measure-Based Approach. IEEE Access, 6, 25610-25620.
Sadghiani, N. S., Torabi, S., & Sahebjamnia, N. (2015). Retail supply chain network design under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 75, 95-114.
Sibevei, A., Azar, A., & Zandieh, M. (2022). Developing a Two-stage Robust Stochastic Model for Designing a Resilient Blood Supply Chain Considering Earthquake Disturbances and Infectious Diseases. Industrial Management Journal, 13(4), 664-703. (in Persian)
Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. Journal of Cleaner Production, 113, 662-673.
Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems, 159(2), 193-214.
Torabi, S., Namdar, J., Hatefi, S., & Jolai, F. (2016). An enhanced possibilistic programming approach for reliable closed-loop supply chain network design. International Journal of Production Research, 54(5), 1358-1387.
Tsao, Y.-C., & Thanh, V.-V. (2019). A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment. Transportation Research Part E: Logistics and Transportation Review, 124, 13-39.
Velte, C. J., & Steinhilper, R. (2016). Complexity in a circular economy: A need for rethinking complexity management strategies. Paper presented at the Proceedings of the World Congress on Engineering, London, UK.
Wang, J., & Wan, Q. (2022). A multi-period multi-product green supply network design problem with price and greenness dependent demands under uncertainty. Applied Soft Computing, 114, 108078. https://doi.org/10.1016/j.asoc.2021.108078
Xu, J., & Zhou, X. (2013). Approximation based fuzzy multi-objective models with expected objectives and chance constraints: Application to earth-rock work allocation. Information Sciences, 238, 75-95.
Yu, C.-S., & Li, H.-L. (2000). A robust optimization model for stochastic logistic problems. International Journal of Production Economics, 64(1-3), 385-397.
Yu, H., & Solvang, W. D. (2020). A fuzzy-stochastic multi-objective model for sustainable planning of a closed-loop supply chain considering mixed uncertainty and network flexibility. Journal of Cleaner Production, 266, 121702.
Yu, L., & Li, Y. (2019). A flexible-possibilistic stochastic programming method for planning municipal-scale energy system through introducing renewable energies and electric vehicles. Journal of Cleaner Production, 207, 772-787.
Zhang, P., & Zhang, W.-G. (2014). Multiperiod mean absolute deviation fuzzy portfolio selection model with risk control and cardinality constraints. Fuzzy sets and systems, 255, 74-91.
Zhang, W.-G., & Xiao, W.-L. (2009). On weighted lower and upper possibilistic means and variances of fuzzy numbers and its application in decision. Knowledge and information systems, 18(3), 311-330.
Industrial Management Journal
Volume 14, Issue 3
2022
Pages 421-457

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