Multi-stage investment planning and customer selection in a two-echelon multi-period supply chain design

Document Type : Article


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


In the supply chain of Fast-moving consumer goods, logistics costs are a main part of the expenses. In low levels of these chains, we usually face with a Vehicle Routing Problem. In practice, due to the high cost of service in many cases, some customers are not chosen for serving. Restrictions associated with the investment in many cases makes impossible serve to some potential clients. In this problem, design a supply chain network, including a location-allocation problem in the warehouse, Multiple Depot Vehicle Routing Problem at the level of distribution and customer selection at the retail level in several periods of time is considered. In this issue, in addition to certain methods that can be used in small sizes, Meta-heuristic algorithms to solve large-scale model have been used. With the aim of improving performance, if not improve in a few diversifications, algorithms are temporarily enhanced. In GA this change leads to improve 1.2 percent and 1.6 percent about the mean of solutions and the best solution and in SA algorithm, this change is led to improving by 2.1% and 2.4% in average solutions and the best solutions. Finally, this approach about a real problem in Sepahan Oil Company is employed.


Main Subjects

1. Setak, M., Azizi, V., Karimi, H., and Jalili, S. Pickup and delivery supply chain network with semi soft time windows: metaheuristic approach", International Journal of Management Science and Engineering Management, 12(2), pp. 89-95 (2016). 2. Tavakkoli-Moghaddam, R., Gazanfari, M., Alinaghian, M., Salamatbakhsh, A., and Norouzi, N. A new mathematical model for a competitive vehicle routing problem with time windows solved by simulated annealing", Journal of Manufacturing Systems, 30(2), pp. 83-92 (2011). 3. Riquelme-Rodr__guez, J.-P., Gamache, M., and Langevin, A. Location arc routing problem with inventory constraints", Computers & Operations Research, 76, pp. 84-94 (2016). 4. Karaoglan, I., Altiparmak, F., Kara, I., and Dengiz, B. The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach", Omega, 40(4), pp. 465-477 (2012). 5. Mehrjerdi, Y.Z. and Nadizadeh, A. Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands", European Journal of Operational Research, 22(1), pp. 75-84 (2013). 6. Schmid, V., Doerner, K.F., and Laporte, G. Rich routing problems arising in supply chain management", European Journal of Operational Research, 224(3), pp. 435-448 (2013). 7. Wang, K.-J. and Lee, C.-H. A revised ant algorithm for solving location-allocation problem with risky demand in a multi-echelon supply chain network", Applied Soft Computing, 32, pp. 311-321 (2015). 8. Dondo, R., M_endez, C.A., and Cerd_a, J. The multiechelon vehicle routing problem with cross docking in supply chain management", Computers & Chemical Engineering, 35(12), pp. 3002-3024 (2011). 9. Osman, S. and Mojahid, F. Capacitated transport vehicle routing for joint distribution in supply chain networks", International Journal of Supply Chain Management, 5(1), pp. 25-32 (2016). 10. Govindan, K., Jafarian, A., Khodaverdi, R., and Devika, K. Two-echelon multiple-vehicle locationrouting problem with time windows for optimization of sustainable supply chain network of perishable food", International Journal of Production Economics, 152, pp. 9-28 (2014). 11. Shari_, S.S.R., Omar, M., and Moin, N.H. Location routing inventory problem with transshipment points using p-center", In Industrial Engineering, Management Science and Application (ICIMSA), 2016 International Conference on, IEEE (2016). 12. Schmid, V., Doerner, K.F., and Laporte, G. Rich routing problems arising in supply chain management", European Journal of Operational Research, 224(3), pp. 435-448 (2013). 13. Chan, F.T., Jha, A., and Tiwari, M.K. Bi-objective optimization of three echelon supply chain involving truck selection and loading using NSGA-II with heuristics algorithm", Applied Soft Computing, 38, pp. 978- 987 (2016). 14. Ahmadi Javid, A. and Azad, N. Incorporating location, routing and inventory decisions in supply chain network design", Transportation Research Part E., 46(5), pp. 582-597 (2010). 15. Wang, Z., Soleimani, H., Kannan, D., and Xu, L. Advanced cross-entropy in closed-loop supply chain planning", Journal of Cleaner Production, 135, pp. 201-213 (2016). 16. Hassanzadeh, A., Rasti-Barzoki, M., and Khosroshahi, H. Two new meta-heuristics for a bi-objective supply chain scheduling problem in ow-shop environment", Applied Soft Computing, 49, pp. 335-351 (2016). 17. Pasandideh, S.H.R., Niaki, S.T.A., and Asadi, K. Biobjective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA", Information Sciences, 292, pp. 57-74 (2015). 18. Lopes, R.B., Ferreira, C., and Santos, B.S. A simple and e_ective evolutionary algorithm for the capacitated location-routing problem", Computers & Operations Research, 70, pp. 155-162 (2016). B. Abbasi et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3032{3050 3049 19. Min, H., Jayaraman, V., and Srivastava, R. Combined location-routing problems: A synthesis and future research directions", European Journal of Operational Research, 7(1), pp. 1-15 (1998). 20. Mokhtarinejad, M., Ahmadi, A., Karimi, B., and Rahmati, S.H.A. A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment", Applied Soft Computing, 34, pp. 274-285 (2015). 21. Perl, J. and Daskin, M.S. A warehouse locationrouting problem", Transportation Research Part B: Methodological, 19(5), pp. 381-396 (1985). 22. Prins, C., Prodhon, C., Ruiz, A., Soriano, P., and Woler Calvo, R. Solving the capacitated location-routing problem by a cooperative Lagrangean relaxation-granular tabu search heuristic", Transportation Science, 41(4), pp. 470-483 (2007). 23. Vincent, F.Y., Lin, S.W., Lee, W., and Ting, C.J. A simulated annealing heuristic for the capacitated location routing problem", Computers & Industrial Engineering, 58(2), pp. 288-299 (2010). 24. Escobar, J.W., Linfati, R., and Toth, P. A two-phase hybrid heuristic algorithm for the capacitated locationrouting problem", Computers & Operations Research, 40(1), pp. 70-79 (2013). 25. Syarif, A., Yun, Y., and Gen, M. Study on multistage logistic chain network: a spanning tree-based genetic algorithm approach", Computers & Industrial Engineering, 43(1), pp. 299-314 (2002). 26. Hajiaghaei-Keshteli, M. The allocation of customers to potential distribution centers in supply chain networks: GA and AIA approaches", Applied Soft Computing, 11(2), pp. 2069-2078 (2011). 27. Michalewicz, Z. and Fogel, D.B., Summary, in How to Solve It: Modern Heuristics, Springer, pp. 483-494 (2004). 28. Bueno, P.M., Jino, M., andWong, W.E. Diversity oriented test data generation using metaheuristic search techniques", Information Sciences, 259, pp. 490-509 (2014). 29. Diabat, A. Hybrid algorithm for a vendor managed inventory system in a two-echelon supply chain", European Journal of Operational Research, 238(1), pp. 114-121 (2014). 30. Costa, A., Celano, G., Fichera, S., and Trovato, E. A new e_cient encoding/decoding procedure for the design of a supply chain network with genetic algorithms", Computers & Industrial Engineering, 59(4), pp. 986-999 (2010). 31. Zegordi, S.H., Abadi, I.K., and Nia, M.B. A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain", Computers & Industrial Engineering, 58(3), pp. 373- 381 (2010). 32. Kannan, G., Sasikumar, P., and Devika, K. A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling", Applied Mathematical Modelling, 34(3), pp. 655-670 (2010). 33. Yeh, W.-C. and Chuang, M.-C. Using multi-objective genetic algorithm for partner selection in green supply chain problems", Expert Systems with Applications, 38(4), pp. 4244-4253 (2011). 34. Jamshidi, R., Ghomi, S.F., and Karimi, B. Flexible supply chain optimization with controllable lead time and shipping option", Applied Soft Computing, 30(5), pp. 26-35 (2015). 35. Assarzadegan, P. and Rasti-Barzoki, M. Minimizing sum of the due date assignment costs, maximum tardiness and distribution costs in a supply chain scheduling problem", Applied Soft Computing, 47, pp. 343-356 (2016). 36. Masoud, S.A. and Mason, S.J. Integrated cost optimization in a two-stage, automotive supply chain", Computers & Operations Research, 67, pp. 1-11 (2016). 37. Fahimnia, B., Davarzani, H., and Eshragh, A. Planning of complex supply chains: A performance comparison of three meta-heuristic algorithms", Computers & Operations Research, 89, pp. 241-252 (2018). 38. Dai, Z. and Zheng, X. Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model", Computers & Industrial Engineering, 88, pp. 444-457 (2015). 39. Pasandideh, S.H.R., Niaki, S.T.A., and Nia, A.R. A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model", Expert Systems with Applications, 38(3), pp. 2708-2716 (2011). 40. Soleimani, H., Seyyed-Esfahani, M., and Shirazi, M.A. Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm", The International Journal of Advanced Manufacturing Technology, 68(1-4), pp. 917- 931 (2013). 41.  Ozk_r, V. and Ba_sl_gil, H. Multi-objective optimization of closed-loop supply chains in uncertain environment", Journal of Cleaner Production, 41, pp. 114-125 (2013). 42. Haridass, K., Valenzuela, J., Yucekaya, A.D., and Mc- Donald, T. Scheduling a log transport system using simulated annealing", Information Sciences, 264, pp. 302-316 (2014). 43. Subramanian, P., Ramkumar, N., Narendran, T.T., and Ganesh, K. PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem", Applied Soft Computing, 13(2), pp. 1121- 1135 (2013). 44. Tognetti, A., Grosse-Ruyken, P.T., and Wagner, S.M. Green supply chain network optimization and the 3050 B. Abbasi et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 3032{3050 trade-o_ between environmental and economic objectives", International Journal of Production Economics, 170, pp. 385-392 (2015). 45. Oberkampf, W.L., Trucano, T.G., and Hirsch, C. Veri_cation, validation, and predictive capability in computational engineering and physics", Applied Mechanics Reviews, 5(7), pp. 345-384 (2004).