A Grey Mathematical Programming Model to Salvage or Re-Commercialize Commodities in Reverse Logistics Management with Consideration of Cross Dock

Document Type : Research Paper

Authors

Department of Industrial Engineering, Faculty of Engineering, Shahed University

Abstract

Competition among organizations to gain more market share and higher profit encourages managers to use new and cost-effective strategies. Returned and surplus goods are always a significant part of stores and company's inventory that deciding whether or not to re-commercialize these goods can have tangible effects on their profits and losses. In this paper, a mixed integer linear programming model for maximizing profit with the cross-docking system in unsold products redistribution process is proposed. This model also takes into account the new considerations of deciding whether or not to re-commercialize products in the reverse logistics operation scheduling. Given the uncertainties in factors such as sales revenue, costs and time, the parameters of the problem are considered in terms of gray numbers and an approach to solve the gray mathematical programming model is used to deal with uncertainties. Moreover, the reverse logistics process in one of the chain stores in Tehran is considered as a case study. Implementing the proposed approach and validating the results by a sensitivity analysis on important parameters indicated that the proposed method has a high performance in decision-making process of the studied company.

Keywords

References

[1] De Brito, M. P., Dekker, R. (2004). A framework for reverse logistics. In Reverse logistics (pp. 3-27). Springer, Berlin, Heidelberg.
[2] Kheirkhah, A., Rezaei, S. (2016). Using cross-docking operations in a reverse logistics network design: a new approach. Production Engineering, 10(2): 175-184.
[3] Bernon, M., Rossi, S., Cullen, J. (2011). Retail reverse logistics: a call and grounding framework for research. International Journal of Physical Distribution & Logistics Management, 41(5): 484-510.
[4] Pishvaee, M. S., Jolai, F., Razmi, J. (2009). A stochastic optimization model for integrated forward/reverse logistics network design. Journal of Manufacturing Systems, 28(4): 107-114.
 [5] Jayaraman, V., Luo, Y. (2007). Creating competitive advantages through new value creation: a reverse logistics perspective. Academy of management perspectives, 21(2): 56-73.
[6] Rogers, D. S., Tibben-Lembke, R. S. (1999). Going backwards: reverse logistics trends and practices (Vol. 2). Pittsburgh, PA: Reverse Logistics Executive Council.
[7] Lambert, S., Riopel, D., Abdul-Kader, W. (2011). A reverse logistics decisions conceptual framework. Computers & Industrial Engineering, 61(3): 561-58.
[8] Guide Jr, V. D. R., Souza, G. C., Van Wassenhove, L. N., & Blackburn, J. D. (2006). Time value of commercial product returns. Management Science, 52(8): 1200-1214.
[9] Boysen, N., Fliedner, M. (2010). Cross dock scheduling: Classification, literature review and research agenda. Omega, 38(6): 413-422.
[10] Ladier, A. L., Alpan, G. (2016). Cross-docking operations: Current research versus industry practice. Omega, 62(1): 145-162.
[11] Stalk, G., Evans, P., Shulman, L. E. (1992). Competing on capabilities: The new rules of corporate strategy. Harvard business review, 70(2): 57-69.
[12] Forger, G. (1995). UPS starts world's premiere cross-docking operation. Modern material handling, 36(8): 36-38.
[13] Witt, C. E. (1998). Crossdocking: Concepts demand choice. Material Handling Engineering, 53(7): 44-49.
[14] Yu, W., Egbelu, P. J. (2008). Scheduling of inbound and outbound trucks in cross docking systems with temporary storage. European Journal of Operational Research, 184(1): 377-396.
[15] Zuluaga, J. P. S., Thiell, M., Perales, R. C. (2017). Reverse cross-docking. Omega, 66(1): 48-57.
[16] Krikke, H., Bloemhof-Ruwaard, J., Van Wassenhove, L. N. (2003). Concurrent product and closed-loop supply chain design with an application to refrigerators. International journal of production research, 41(16): 3689-3719.
[17] Chandoul, A., Cung, V. D., Mangione, F. (2007). Reusable containers within reverse logistic context.
[18] Agrawal, S., Singh, R. K., Murtaza, Q. (2015). A literature review and perspectives in reverse logistics. Resources, Conservation and Recycling, 97: 76-92.
[19] Maknoon, M.Y., Kone, O., Baptiste, P. (2014). A sequential priority-based heuristic for scheduling material handling in a satellite cross-dock. Computers & Industrial Engineering, 72(1): 43-49.
[20] Fanti, M.P., Stecco, G., Ukovich, W. (2016). Scheduling Internal Operations in Post-Distribution Cross Docking Systems. IEEE Transactions on Automation Science and Engineering, 296-312.
[21] Rezaei, S.,  Kheirkhah, A. (2017). Applying forward and reverse cross-docking in a multi-product integrated supply chain network. Production Engineering, 11(4-5): 495-509.
[22] Kaboudani, Y., Ghodsypour, S. H., Kia, H., &Shahmardan, A. (2018). Vehicle routing and scheduling in cross docks with forward and reverse logistics. Operational Research, 1-34.
[23] Vahdani, B. (2019). Assignment and scheduling trucks in cross-docking system with energy consumption consideration and trucks queuing. Journal of Cleaner Production, 213(1): 21-41.
[24] Mahmoudi, A., Liu, S., Javed, S. A., Abbasi, M. (2019). A novel method for solving linear programming with grey parameters. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-12.
[25] Mousavi, S. M., Antuchevičienė, J., Zavadskas, E. K., Vahdani, B., Hashemi, H. (2019). A new decision model for cross-docking center location in logistics networks under interval-valued intuitionistic fuzzy uncertainty. Transport, 34(1): 30-40.
[26] Rahbari, A., Nasiri, M. M., Werner, F., Musavi, M., & Jolai, F. (2019). The vehicle routing and scheduling problem with cross-docking for perishable products under uncertainty: Two robust bi-objective models. Applied Mathematical Modelling, 70: 605-625.
[27] Heidari, F., Zegordi, S. H., Tavakkoli-Moghaddam, R. (2018). Modeling truck scheduling problem at a cross-dock facility through a bi-objective bi-level optimization approach. Journal of Intelligent Manufacturing, 29(5): 1155-1170.
[30] Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G. V., Eversdyk, D. (2004). A simulation based optimization approach to supply chain management under demand uncertainty. Computers & chemical engineering, 28(10): 2087-2106.
[31] Mele, F. D., Guillén, G., Espuna, A., & Puigjaner, L. (2007). An agent-based approach for supply chain retrofitting under uncertainty. Computers & chemical engineering, 31(5-6): 722-735.
[32] Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., Omid, M. (2018). Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers & Operations Research, 89: 337-347.
[33] Daugherty, P. J., Myers, M. B., Richey, R. G. (2002). Information support for reverse logistics: the influence of relationship commitment. Journal of business logistics, 23(1): 85-106.
[34] Kim, J., Do Chung, B., Kang, Y., Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of Cleaner Production.
[35] Li, G. D., Yamaguchi, D., Nagai, M. (2007). A grey-based decision-making approach to the supplier selection problem. Mathematical and computer modelling, 46(3-4): 573-581.
[36] Lin, Y. H., Lee, P. C., Ting, H. I. (2008). Dynamic multi-attribute decision making model with grey number evaluations. Expert Systems with Applications, 35(4): 1638-1644.
[37] Deng, J. L. (1982). Control problems of grey systems. Sys. & Contr. Lett., 1(5): 288-294.
[38] Pan, L.K., Wang, C.C., Wei, S.L., Sher, H.F., 2007. Optimizing multiple quality characteristics via Taguchi method-based grey analysis. J. Mater. Process. Technol. 182 (1): 107e116.
[39] Tseng, M. L. (2009). A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert systems with applications, 36(4): 7738-7748.
[40] Rajesh, R., Ravi, V. (2015). Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production, 86: 343-359.
 [41] Deng, J.L. (1989). Introduction to gray system theory, Journal of Grey System., 1(1): 1–24.
[42] Dang, S. L. Y., Forrest, J. (2009, October). On positioned solution of linear programming with grey parameters. In 2009 IEEE International Conference on Systems, Man and Cybernetics (pp. 751-756). IEEE.
[43] Chen, T.Y. (2011). Optimistic and pessimistic decision making with dissonance reduction using interval-valued fuzzy sets. Information Sciences, 181(3): 479-5.
]28[ فاروقی، هیوا، اشرفی فشی، محمد. (1396). "طراحی شبکه زنجیره‌ی تأمین چند سطحی با در نظر گرفتن راهبردهای پایای چندگانه در سطح مراکز توزیع"، نشریه پژوهش‌های مهندسی صنایع در سیستم های تولید، 5(10)، 53-67.
]29[ اردوان، علی، عالم تبریز، اکبر، ربیعه، مسعود، زندیه، مصطفی. (1397). "انتخاب تأمین‌کنندگان پایدار با رویکرد تئوری خاکستری: مورد مطالعه صنعت فولاد". نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید. 6(13)، 165-177.
Journal of Industrial Engineering Research in Production Systems
Volume 7, Issue 14
September 2019
Pages 163-177

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