A Multi-Objective Model for Green Closed-Loop Supply Chain Design by Handling Uncertainties inEffective Parameters

Kalantari Khalil Abad, Amin Reza; Pasandideh, Seyed Hamid Reza

doi:10.22070/jqepo.2020.5398.1153

A Multi-Objective Model for Green Closed-Loop Supply Chain Design by Handling Uncertainties inEffective Parameters

Document Type : Research Paper

Authors

Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

10.22070/jqepo.2020.5398.1153

Abstract

The process of designing and redesigning supply chain networks is subject to multiple uncertainties. Given the growing environmental pollution and global warming caused by societies' industrialization, this process can be completed when environmental considerations are also taken into account in the decisions. In this study, an integrated four-level closed-loop supply chain network, including factories, warehouses, customers, and disassembly centers (DCs) is designed to fulfill environmental objectives in addition to economic ones. The reverse flow, including recycling and reprocessing the waste products, is considered to increase production efficiency. Also, the different transportation modes between facilities, proportional to their cost and greenhouse gas emissions, are taken into account in the decisions. A random cost function and chance constraints are presented firstly to handle the uncertainties in different parameters. After defining the random constraints using the chance-constraint programming approach, a deterministic three-objective model is presented. The developed model is solved using the GAMS software and the goal attainment (GA) method. Also, the effect of the priority of the goal, uncertain parameters, and confidence level of chance constraints on objective function values has been carefully evaluated using different numerical examples.

Keywords

References

Al-Juboori, M., & Datta, B. (2019). Optimum design of hydraulic water retaining structures incorporating uncertainty in estimating heterogeneous hydraulic conductivity utilizing stochastic ensemble surrogate models within a multi-objective multi-realisation optimisation model. Journal of Computational Design and Engineering, 6(3), 296–315.

https://doi.org/10.1016/j.jcde.2018.12.003

Alshamsi, A., & Diabat, A. (2015). A reverse logistics network design. Journal of Manufacturing Systems, 37(3), 589-598. https://doi.org/10.1016/j.jmsy.2015.02.006

Altiparmak, F., Gen, M., Lin, L., & Paksoy, T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks, Computer & Industrial Engineering-Special Issue on Computational Intelligence and Information Technology: Applications to Industrial Engineering33rd, ICC&IE – Computational Intelligence & Information, 51(1), 196 – 215. https://doi.org/10.1016/j.cie.2006.07.011

Azaron, A., Brown, K.N., Tarim, S.A., & Modarres M. (2008). A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, 116(1), 129 – 138.

https://doi.org/10.1016/j.ijpe.2008.08.002

Babazadeh, R., Razmi, J., & Ghodsi, R. (2013). Facility location in responsive and flexible supply chain network design (SCND) considering outsourcing. International Journal of Operational Research, 17(3), 295–310.

https://doi.org/10.1504/IJOR.2013.054437

Bera S., Jana D.K., Basu K., Maiti M. (2020). Novel Multi-objective Green Supply Chain Model with CO2 Emission Cost in Fuzzy Environment via Soft Computing Technique. In: Castillo O., Jana D., Giri D., Ahmed A. (eds) Recent Advances in Intelligent Information Systems and Applied Mathematics. ICITAM 2019. Studies in Computational Intelligence, vol 863. Springer, Cham. https://doi.org/10.1007/978-3-030-34152-7_36

Cardona-Valdes, Y., Alvarez, A., Ozdemir D. (2011). A bi-objective supply Alvarez chain design problem with uncertainty, Transportation Research Part C, 19: 821 – 832. https://doi.org/10.1016/j.trc.2010.04.003

Chopra, S., & Meindl, P., (2007). Supply chain management. Strategy, planning & operation. In: Boersch C., Elschen R. (eds) Das summa summarum des management. Gabler. 265 -275. https://doi.org/10.1007/978-3-8349-9320-5_22

Devikaa, K., Jafarian, A., & Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach, European Journal of Operational Research, 235(3): 594 – 615. https://doi.org/10.1016/j.ejor.2013.12.032

Fakhrzad, M. B., & Goodarzian, F. (2019). A fuzzy multi-objective programming approach to develop a green closed-loop supply chain network design problem under uncertainty: Modifications of imperialist competitive algorithm. (2019). RAIRO-Operation Research, 53, 963–990. https://doi.org/10.1051/ro/2019018

Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). A stochastic multi-objective model for a closed-loop supply chain with environmental considerations. Applied Soft Computing, 69, 232-249. https://doi.org/10.1016/j.asoc.2018.04.055 (

Giarola, S., Zamboni, A., Bezzo, F., (2011). Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries. Computer & Chemical Engineering, 35(9), 1782–1797, Energy Systems Engineering. https://doi.org/10.1016/j.compchemeng.2011.01.020

Haddadsisakht, A., & Ryan, S. M. (2018). Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax. International Journal of Production Economics, 195, 118–131.

https://doi.org/10.1016/j.ijpe.2017.09.009

Heidari-Fathian, H., & Pasandideh, S. H. R. (2018). Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation. Computer & Industrial Engineering, 122, 95-105.

https://doi.org/10.1016/j.cie.2018.05.051

Imran, M., Kang, C. W., & Ramzan M. B. (2018). Medicine supply chain model for an integrated healthcare system with uncertain product complaints. Journal of Manufacturing Systems, 46, 13–28. https://doi.org/10.1016/j.jmsy.2017.10.006

Kamali, A., Ghomi, S. M., & Jolai, F. (2011). A multi objective quantity discount and joint optimization model for coordination of a single-buyer multi-vendor supply chain. Computers & Mathematics with Applications, 62(8), 3251–3269.

https://doi.org/10.1016/j.camwa.2011.08.040.

Khatami, M., Mahootchi, M., & Farahani, R. Z. (2015). Benders' decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties. Transportation Research, Part E: Logistic and Transportation Review, 79, 1–21. https://doi.org/10.1016/j.tre.2015.03.003

Mardan, E.,Govindan, K., Mina, H., & Gholami-Zanjani,S., M. (2019). An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem, Journal of Cleaner Production. 235, 1499-1514. https://doi.org/10.1016/j.jclepro.2019.06.187

Mohammed, A., and Wang, Q. (2017). The fuzzy multi-objective distribution planner for a green meat supply chain, International Journal of Production Economics, 184, 47 – 58. https://doi.org/10.1016/j.ijpe.2016.11.016

Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421–432.

https://doi.org/10.1016/j.ijpe.2016.08.028

zceylan, E., Demirel, N., ¸Cetinkaya, C., & Demirel, E. (2016). A closed-loop supply chain network design for automotive industry in Turkey. Computer and Industrial Engineering, 113, 727-745. https://doi.org/10.1016/j.cie.2016.12.022

Ozkir, V., & Basligil, H. (2013). Multi objective optimization of closed loop supply chains in uncertain environment. Journal of Cleaner Production, 41, 114–125. https://doi.org/10.1016/j.jclepro.2012.10.013

Pasandideh, S. H. R., Niaki, S. T. A., and Asadi, K. (2014). Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA, Information Science, 292, 57 - 74.

https://doi.org/10.1016/j.ins.2014.08.068

Pati, R., Jans, R., & Tyagi, R. K. (2013). Green logistics network design: a critical review. Production & Operations Management, 1–10. http://www.pomsmeetings.org/ConfProceedings/043/FullPapers/FullPaper_files/043-1123.pdf.

Pishvaee, M. S., Razmi, J., & Torabi S. A. (2014). An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain. Transportation Research Part E: Logistics and Transportation Review, 67, 14-38. https://doi.org/10.1016/j.tre.2014.04.001

Qu, X., Liu, G., Duan, S., & Yang J. (2016). Multi-objective robust optimization method for the modified epoxy resin sheet molding compounds of the impeller. Journal of Computational Design and Engineering, 3(3), 179–190.

https://doi.org/10.1016/j.jcde.2016.01.002

Rezaee, A., Dehghanian, F., Fahimnia, B., & Beamon, B. (2017). Green supply chain network design with stochastic demand and carbon price. Annals of Operations Research, 250(2), 463–485. https://doi.org/10.1007/s10479-015-1936-z )نه

Shaw, K., Irfan, M., Shankar, R., & Yadav, S. S. (2016). Low carbon chance constrained supply chain network design problem: A benders decomposition based approach. Computers & Industrial Engineering, 98, 483–497.

https://doi.org/10.1016/j.cie.2016.06.011

Song, D.-P., Dong, j-x., Xu, J. (2014). Integrated inventory management and supplier base reduction in a supply chain with multiple uncertainties, European Journal of Operation Research, 232 (3), 522 – 536. https://doi.org/10.1016/j.ejor.2013.07.044

Trochu, J., Chaabane, A., & Ouhimmou, M. (2020). A carbon-constrained stochastic model for eco-efficient reverse logistics network design under environmental regulations in the CRD industry. Journal of Cleaner Production, 245, 118818. https://doi.org/10.1016/j.jclepro.2019.118818

Tsao, Y-C., Thanh, V-V., Lu, J-C., & Yu, V. (2018). Designing sustainable supply chain networks under uncertain environments: Fuzzy multi-objective programming. Journal of Cleaner Production, 174, 1550-1565.

https://doi.org/10.1016/j.jclepro.2017.10.272

Varsei, M., & Polyakovskiy S. (2017). Sustainable supply chain network design: A case of the wine industry in Australia. Omega-International Journal of Management Science, 66, 236–47. https://doi.org/10.1016/j.omega.2015.11.009

Yavari, M., & Geraeli, M. (2019). Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. Journal of Cleaner Production, 226, 282-305. https://doi.org/10.1016/j.jclepro.2019.03.279

Zailani, S., Jeyaraman, K., Vengadasan, G., & Premkumar, R. (2012). Sustainable supply chain management (SSCM) in Malaysia: A survey. International Journal of Production Economics, 140 (1), 330-340. https://doi.org/10.1016/j.ijpe.2012.02.008

Zeballos, L. J., M_endez, C. A., Barbosa-Povoa, A. P., & Novais, A. Q. (2014). Multi-period design and planning of closed-loop supply chains with uncertain supply and demand. Computers & Chemical Engineering, 66, 151–164.

https://doi.org/10.1016/j.compchemeng.2014.02.027