A sustainable closed-loop location-routing-inventory problem for perishable products

Document Type : Article

Authors

School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

Perishable products may expire if their holding time exceeds their shelf-life. In this study, along with designing a forward flow to distribute perishable products; remained perished products at retailers can be gathered for recycling during distributing fresh products. To mitigate the waste, recycled products are offered to a secondary market. A mathematical model for this Closed-Loop Location-Routing-Inventory Problem (CL-LRIP) is developed by considering multi-compartment trucks, simultaneous pickup and delivery, technology selection, and risk of urban traffic. Based on three sustainability pillars, three objective functions are considered. This way, the interests of the network's three main stakeholders are embedded. The proposed model is solved by the Torabi-Hassini method. Two evolutionary algorithms, including Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a new hybrid one, are also developed to solve large-sized cases of the NP-complete problem. Statistical tests show the superiority of the hybrid algorithm in the computational time (CT) metric, which is about 0.4 NSGA-II’s CT. The results indicate the importance of closing the network loop for perishable products. Finally, the sensitivity analysis determined that 83.33 % decrease in recycled product’s sale price causes 9.08% increase in costs, 2.77% decrease in environmental side-effects, and 5.16% decrease in social objectives, which are significant.

Keywords

References

References:
1. Gustavsson, J. and Cederberg, C., Global Food Losses and Food Waste, Food and Agriculture Organization of the United Nations, Rome (2011).
2. Govindan, K., Jafarian, A., Khodaverdi, R., and Devika, K. "Two-echelon multiple-vehicle location - routing problem with time windows for optimization of sustainable supply chain network of perishable food", Intern J. Prod. Econ., 152, pp. 9-28 (2014).https://doi.org/10.1016/j.ijpe.2013.12.028.
3. Parfitt, J., Barthel, M., and Macnaughton, S. "Food waste within food supply chains: quantification and potential for change to 2050", Philos. Trans. R. Soc. B Biol. Sci., 365(1554), pp. 3065-3081 (2010).https://doi.org/10.1098/rstb.2010.0126.
4. Sazvar, Z., Mirzapour Al-e-hashem, S.M.J., Govindan, K., et al. "A novel mathematical model for a multiperiod, multi-product optimal ordering problem considering expiry dates in a FEFO system", Transp. Res. Part E Logist. Transp. Rev., 93, pp. 232-261 (2016).https://doi.org/10.1016/j.tre.2016.04.011.
5. Devika, K., Jafarian, A., and Nourbakhsh, V. "Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques", Eur. J. Oper. Res., 235(3), pp. 594-615 (2014).https://doi.org/10.1016/j.ejor.2013.12.032.
6. Seuring, S. and Muller, M. "From a literature review to a conceptual framework for sustainable supply chain management", J. Clean. Prod., 16(15), pp. 1699-1710 (2008). https://doi.org/10.1016/j.jclepro.2008.04.020.
7. Eskandarpour, M., Dejax, P., Miemczyk, J., et al. "Sustainable supply chain network design: An optimization-oriented review", Omega, 54, pp. 11-32 (2015). https://doi.org/10.1016/j.omega.2015.01.006.
8. Govindan, K., Soleimani, H., and Kannan, D. "Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future", Intern J. Prod. Econ., 240(3), pp. 603-626 (2015).https://doi.org/10.1016/j.ejor.2014.07.012.
9. Kim, T., Glock, C.H., and Kwon, Y. "A closed-loop supply chain for deteriorating products under stochastic container return times", Omega, 43, pp. 30-40 (2014). https://doi.org/10.1016/j.omega.2013.06.002.
10. Moon, I., Salhi, S., and Feng, X. "The locationrouting problem with multi-compartment and multitrip: formulation and heuristic approaches", Transp. A Transp. Sci., 16(3), pp. 501-528 (2020).https://doi.org/10.1080/23249935.2020.1720036.
11. Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., et al. "Sustainable design of a closed-loop locationrouting- inventory supply chain network under mixed uncertainty", Transp. Res. Part E Logist. Transp. Rev., 89, pp. 182-214 (2016). https://doi.org/10.1016/j.tre.2016.02.011.
12. European Commission, Circular Economy - Clear Targets and Tools for Better Waste Management, European Commission (2015).
13. Ramezani, M., Bashiri, M., and Tavakkoli-Moghaddam, R. "A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level", Appl. Math. Model., 37(1-2), pp. 328-344 (2013). https://doi.org/10.1016/j.apm.2012.02.032.
14. Banasik, A., Kanellopolos, A., and Claassen, G.D.H. "closing loops in agricultural supply chains using multi objective optimization: A case study of an industrial mushroom supply chain", Int. J. Prod. Econ., 183, pp. 409-420 (2017).
15. Pishvaee, M.S., Rabbani, M., and Torabi, S.A. "A robust optimization approach to closed-loop supply chain network design under uncertainty", Appl. Math. Model., 35(2), pp. 637-649 (2011). https://doi.org/10.1016/j.apm.2010.07.013.
16. Rahimi, M., Baboli, A., and Rekik, Y. "Sustainable inventory routing problem for perishable products by considering reverse logistic", IFAC-Pap., 49(12), pp. 949-954 (2016). https://doi.org/10.1016/j.ifacol.2016.07.898.
17. Khalili-Damghani, K., Abtahi, A.-R., and Ghasemi, A. "A new bi-objective location-routing problem for distribution of perishable products: Evolutionary computation approach", J. Math. Model. Algorithms Oper. Res., 14(3), pp. 287-312 (2015).
https://doi.org/10.1007/s10852-015-9274-3.
18. Rabbani, M., Navazi, F., Farrokhi-Asl, H., et al. "A sustainable transportation-location-routing problem with soft time windows for distribution systems", Uncertain Supply Chain Manag., 6(3), pp. 229-254 (2018).https://doi.org/10.5267/j.uscm.2017.12.002.
19. Sazvar, Z., Rahmani, M., and Govindan, K. "A sustainable supply chain for organic, conventional agro-food products: The role of demand substitution, climate change and public health", J. Clean. Prod., 194, pp. 564-583 (2018). https://doi.org/10.1016/j.jclepro.2018.04.118.
20. Sazvar, Z. and Sepehri, M. "An integrated replenishment-recruitment policy in a sustainable retailing system for deteriorating products", Socioecon. Plann., Sci., 69(February 2019), 100686 (2020). https://doi.org/10.1016/j.seps.2019.02.001.
21. Bakker, M., Riezebos, J., and Teunter, R.H. "Review of inventory systems with deterioration since 2001", Eur. J. Oper. Res., 221(2), pp. 275-284 (2012). https://doi.org/10.1016/j.ejor.2012.03.004.
22. Sepehri, M. and Sazvar, Z. "Multi-objective sustainable supply chain with deteriorating products and transportation options under uncertain demand and backorder", Scientia Iranica, 23(6), pp. 2977-2994 (2016). https://doi.org/10.24200/sci.2016.4006.
23. Caracciolo, F., Amani, P., Cavallo, C., et al. "The environmental benefits of changing logistics structures for fresh vegetables", Int. J. Sustain. Transp., 12(4), pp. 233-40 (2018). https://doi.org/10.1080/15568318.2017.1337834.
24. Chaudhary, V., Kulshrestha, R., and Routroy, S. "State-of-the-art literature review on inventory models for perishable products", J. Adv. Manag. Res., 15(3), pp. 306-346 (2018). https://doi.org/10.1108/JAMR-09-2017-0091.
25. Teng, J-T., Cardenas-Barron, L.E., Chang, H-J., et al. "Inventory lot-size policies for deteriorating items with expiration dates and advance payments", Appl. Math. Model., 40(19-20), pp. 8605-8616 (2016). https://doi.org/10.1016/j.apm.2016.05.022.
26. Ketzenberg, M., Gaukler, G., and Salin, V. "Expiration dates and order quantities for perishables", Eur. J. Oper. Res., 266(2), pp. 569-584 (2018). https://doi.org/10.1016/j.ejor.2017.10.005.
27. Singh, S., Khurana, D., and Tayal, S. "An economic order quantity model for deteriorating products having stock dependent demand with trade credit period and preservation technology", Uncertain Supply Chain Manag., 4(1), pp. 29-42 (2016).
28. Weiss, H.J. "Economic order quantity models with nonlinear holding costs", Eur. J. Oper. Res., 9(1), pp. 56-60 (1982). https://doi.org/10.1016/0377-2217(82)90010-8.
29. San-Jose, L.A., Sicilia, J., Cardenas-Barron, L.E., et al. "Optimal price and quantity under power demand pattern and non-linear holding cost", Comput. Ind. Eng., 129, pp. 426-434 (2019). https://doi.org/10.1016/j.cie.2019.01.054.
30. Sazvar, Z., Mirzapour Al-E-Hashem, S.M.J., Baboli, A., et al. "A bi-objective stochastic programming model for a centralized green supply chain with deteriorating products", Int. J. Prod. Econ., 150, pp. 140-154 (2014). https://doi.org/10.1016/j.ijpe.2013.12.023.
31. Rahimi, M., Baboli, A., and Rekik, Y. "Multiobjective inventory routing problem: A stochastic model to consider profit, service level and green criteria", Transp. Res. Part E Logist. Transp. Rev., 101, pp. 59-83 (2017). https://doi.org/10.1016/j.tre.2017.03.001.
32. Ahmadi Javid, A. and Azad, N. "Incorporating location, routing and inventory decisions in supply chain network design", Transp. Res. Part E Logist. Transp. Rev., 46(5), pp. 582-597 (2010). https://doi.org/10.1016/j.tre.2009.06.005.
33. Prodhon, C. and Prins, C. "A survey of recent research on location-routing problems", Eur. J. Oper. Res., 238(1), pp. 1-17 (2014). https://doi.org/10.1016/j.ejor.2014.01.005.
34. Nekooghadirli, N., Tavakkoli-moghaddam, R., Ghezavati, V.R., et al. "Solving a new bi-objective locationrouting-inventory problem in a distribution network by meta-heuristics", Comput. Ind. Eng., 76(1), pp. 204- 221 (2014). https://doi.org/10.1016/j.cie.2014.08.004.
35. Ghorbani, A. and Akbari Jokar, M.R. "A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem", Comput. Ind. Eng., 101, pp. 116-127(2016). https://doi.org/10.1016/j.cie.2016.08.027.
36. Tavakkoli-Moghaddam, R. and Raziei, Z. "A new bi-objective location-routing-inventory problem with fuzzy demands", IFAC-Pap., 49(12), pp. 1116-1121 (2016). https://doi.org/10.1016/j.ifacol.2016.07.646.
37. Zheng, X., Yin, M., and Zhang, Y. "Integrated optimization of location, inventory and routing in supply chain network design", Transp. Res. Part B Methodol., 121, pp. 1-20 (2019). https://doi.org/10.1016/j.trb.2019.01.003.
38. Karakostas, P., Sifaleras, A., and Georgiadis, M.C. "A general variable neighborhood search-based solution approach for the location-inventory-routing problem with distribution outsourcing", Comput. Chem. Eng., 126, pp. 263-279 (2019). https://doi.org/10.1016/j.compchemeng.2019.04.015.
39. Hiassat, A., Diabat, A., and Rahwan, I. "A genetic algorithm approach for location-inventory-routing problem with perishable products", J. Manuf. Syst., 42, pp. 93-103 (2017). https://doi.org/10.1016/j.jmsy.2016.10.004.
40. Rafie-Majd, Z., Pasandideh, S.H.R., and Naderi, B. "Modelling and solving the integrated inventorylocation- routing problem in a multi-period and multiperishable product supply chain with uncertainty: Lagrangian relaxation algorithm", Comput. Chem. Eng., 109, pp. 9-22 (2018). https://doi.org/10.1016/j.compchemeng.2017.10.013.
41. Navazi, F., Tavakkoli-Moghaddam, R., Sazvar, Z., et al. "Sustainable design for a bi-level transportationlocation-vehicle routing scheduling problem in a perishable product supply chain", in: Borangiu T, Trentesaux D, Thomas A, Cavalieri S (Eds.), Service Orientation in Holonic and Multi-Agent Manufacturing, Springer International Publishing, Cham, pp. 308-321 (2018).
42. Soysal, M. "Closed-loop inventory routing problem for returnable transport items", Transp. Res. Part Transp. Environ., 48, pp. 31-45 (2016).https://doi.org/10.1016/j.trd.2016.07.001.
43. Xiao, Y., Zhao, Q., Kaku, I., et al. "Development of a fuel consumption optimization model for the capacitated vehicle routing problem", Comput. Oper. Res., 39(7), pp. 1419-1431 (2012). https://doi.org/10.1016/j.cor.2011.08.013.
44. Mirzapour Al-e-hashem, S.M.J., Baboli, A., and Sazvar, Z. "A stochastic aggregate production planning model in a green supply chain: Considering  flexible lead times, nonlinear purchase and shortage cost functions", Eur. J. Oper. Res., 230(1), pp. 26-41 (2013). https://doi.org/10.1016/j.ejor.2013.03.033.
45. Pishvaee, M.S., Razmi, J., and Torabi, S.A. "Robust possibilistic programming for socially responsible supply chain network design: A new approach", Fuzzy Sets Syst., 206, pp. 1-20 (2012). https://doi.org/10.1016/j.fss.2012.04.010.
46. Torabi, S.A. and Hassini, E. "An interactive possibilistic programming approach for multiple objective supply chain master planning", Fuzzy Sets Syst., 159, pp. 193-214 (2008). https://doi.org/10.1016/j.fss.2007.08.010.
47. Deb, K., Pratap, A., Agarwal, S., et al. "A fast and elitist multiobjective genetic algorithm", IEEE Trans. Evol. Comput., 6(2), pp. 182-197 (2002).
48. Reyes-sierra, M. and Coello, C.A.C. "Multi-objective article swarm optimizers: A survey of the state-ofthe- art", Int. J. Comput. Intell. Res., 2(3), pp. 287- 308 (2006).
49. Rabbani, M., Navazi, F., Eskandari, N., et al. "A green transportation location-inventory-routing problem by dynamic regional pricing", J. Ind. Eng. Manag. Stud., 7(1), pp. 35-58 (2020).
50. Forouzanfar, F., Tavakkoli-Moghaddam, R., Bashiri, M., et al. "New mathematical modeling for a locationrouting- inventory problem in a multi-period closedloop supply chain in a car industry", J. Ind. Eng. Int., 14(3), pp. 537-553 (2018).
51. Arab, R., Ghaderi, S.F., and Tavakkoli-Moghaddam, R. "Bi-objective inventory routing problem with backhauls under transportation risks: two metaheuristics", Transp. Lett., 12(2), pp. 113-129 (2020). https://doi.org/ 19427867.2018.1533624.
52. Vahdani, B., Shekari, D.V.N., and Mousavi, S.M. "Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair", Neural Comput. Appl., 30(3), pp. 835-854 (2016). https://doi.org/10.1007/s00521-016-2696-7.
Scientia Iranica
Volume 30, Issue 2
Transactions on Industrial Engineering (E)
March and April 2023
Pages 757-783

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