The growing trend of natural resources consumption has caused irreparable losses to the environment. The scientists believe that if environmental degradation continues at its current pace, the prospect of human life will be shrouded in mystery. One of the most effective ways to deal with the environmental adverse effects is by implementing green supply chains. In this study a multilevel mathematical model including supply, production, distribution and customer levels has been presented for routing–location–inventory
in green supply chain. Vehicle routing between distribution centres and customers has been considered in the model. Establishment place of distribution centres among potential places is determined by the model. The distributors use continuous review policy (r, Q) to control the inventory. The proposed model object is to find an optimal supply chain with minimum costs. To validate the proposed model and measure its compliance with real world problems, GAMS IDE/Cplex has been used. In order to measure the efficiency of the proposed model in large scale problems, a genetic algorithm has been used. The results confirm the efficiency of the proposed model as a practical tool for decision makers to solve location-inventory-routing problems in green supply chain. The proposed GA could reduce the solving time by 85% while reaching on the average 97% of optimal solution compared with exact method.
Prodhon C. Le problème de localisation-routage [PhD Thesis]. France: University of Technology of Troyes; 2006.
Gebennini E, Gamberini R, Manzini R. An integrated production–distribution model for the dynamic location and allocation problem with safety stock optimization. International Journal of Production Economics. 2009;122(1):286-304. doi: 10.1016/j.ijpe.2009.06.027
Prins C, Prodhon C, Ruiz A. Soriano P. Wolfler-Calvo R. Solving the capacitated location-routing problem by a cooperative Lagrangean relaxation granular tabu search heuristic. Transportation Science. 2007;41(4):470-83. doi: 10.1287/trsc.1060.0187
Belenguer JM, Benavent E, Prins C, Prodhon C, Wofler-Calvo R. A branch-and-cut method for the capacitated location-routing problem. Computers & Operations Research. 2011;38(6):931-41. doi: 10.1016/j.cor.2010.09.019
Prodhon C. A hybrid evolutionary algorithm for the periodic location-routing problem. European Journal of Operational Research. 2011;210(2):204-12. doi: 10.1016/j.ejor.2010.09.021
Nagy G, Salhi S. Location-routing issues models and methods: A review. European Journal of Operational Research. 2007;117:649-72. doi: 10.1016/j.ejor.2006.04.004
Prodhon C, Prins C. A survey of recent research on location-routing problems. European Journal of Operational Research. 2014;238:1-17. doi: 10.1016/j.ejor.2014.01.005
Bertazzi L, Paletta G, Speranza M. Deterministic order- up-to level policies in an inventory routing problem. Transportation Science. 2002;36(1):119-32. doi: 10.1287/trsc.36.1.119.573
Cha BC, Moon IK, Park JH. The joint replenishment and delivery scheduling of the one-warehouse n-retailer system. Transportation Research Part E Logist. Transp. Rev. 2008;44(5):720-30.
Solyali O, Cordeu JF, Laporte G. Robust inventory routing under demand uncertainty. Transportation Science. 2010;46(3):327-40. doi: 10.2307/23263546
Oppen J, Lokketangen A, Desrosiers J. Solving a rich vehicle routing and inventory problem using column generation. Computers and Operations Research. 2010;37:1308-317. doi: 10.1016/j.cor.2009.09.014
Kleywegt A, Nori VS, Savelsbergh MWP. The stochastic inventory routing problem with direct deliveries. Transportation Science. 2002;36:94-118. doi: 10.1287/trsc.36.1.94.574
Vidovic M, Popovic D, Ratkovic B. Mixed integer and heuristics model or the inventory routing problem in fuel delivery. Int. J. Production Economics. 2014;147:593-604. doi: 10.1016/j.ijpe.2013.04.034
Andersson H, Hoff A, Christiansen M, Hasle G, Løkketangen A. Invited review: industrial aspects and literature survey: combined inventory management and routing. Computers & Operations Research. 2010;37:1515-536. doi: 10.1016/j.cor.2009.11.009
Daskin M, Coullard C, Shen ZJ. An inventory location model: formulation, solution algorithm and computational results. Annals of Operations Research. 2002;110:83-106. doi: 10.1023/A:1020763400324
Shen ZJ. A multi commodity supply chain design problem. IIE Transactions. 2005;37:753-62. doi: 10.1080/07408170590961120
Ma H, Davidrajuh R. An iterative approach for distribution chain design in agile virtual environment. Ind. Manage. Data System. 2005;105(6):815-34. doi: 10.1108/02635570510607012
Miranda P, Garrido R. Inventory service-level optimization within distribution network design problem. International Journal of Production Economics. 2009;122(1):276-85. doi: 10.1016/j.ijpe.2009.06.010
Liu SC, Lee SB. A two-phase heuristic method for the multi-depot location routing problem taking inventory control decisions into consideration. The International Journal of Advanced Manufacturing Technology. 2003;22:941-50. doi: 10.1007/s00170-003-1639-5
Ambrosino D, Scutellà MG. Distribution network design: new problems and related models. European Journal of Operational Research. 2005;165(3):610-624. doi: 10.1016/j.ejor.2003.04.009
Guerrero WJ, Prodhon C, Velasco N, Amaya CA. Hybrid heuristic for the inventory location-routing problem with deterministic demand. Int. J. Prod. Econ. 2013;146(1):359-70. doi: 10.1016/j.ijpe.2013.07.025
Ahmadi Javid A, Azad N. Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review. 2010;46(5):582-97. doi: 10.1016/j.tre.2009.06.005
Hiassat A, Diabat A. Location-inventory-routing-problem with perishable products. Paper presented at: 41st International Conference on Computers and Industrial Engineering; 2011 October 23-25; Los Angeles, California, USA.
Lerhlaly S, Lebbar M, Allaoui H, Ouazar D, Afifi S. An Integrated Inventory Location Routing Problem Considering CO2 Emissions. Contemporary Engineering Sciences. 2016;9(7):303-314. doi: 10.12988/ces.2016.512326
Gutiérrez MG, Silva CW. Inventory Location Routing Problem: A Column Generation Approach. Paoer presented at: International Conference on Industrial Engineering and Operations Management; 2012 July 3-6; Istanbul, Turkey.
Zhang Y, Qi M, Miao L, Liu E. Hybrid metaheuristic solutions to inventory location routing problem. Transportation Research Part E. 2014;70:305-23. doi: 10.1016/j.tre.2014.07.010
Bae ST, Hwang HS, Cho GS. Goan MJ. Integrated GAVRP solver for multi-depot system. Computers & Industrial Engineering. 2007;53(2):233-40. doi: 10.1016/j.cic.2007.06.014
[28] Oliveira FB, Enayatifar R, Sadaei HJ. Guimarães FG. Potvin, JY. A cooperative coevolutionary algorithm for the multi-depot vehicle routing problem. Expert Systems with Applications. 2016;43(1):117-30. doi: 10.1016/j.eswa.2015.08.030
Mirabi M, Ghomi SF, Jolai F. Efficient stochastic hybrid heuristics for the multi-depot vehicle routing problem. Robotics and Computer-Integrated Manufacturing. 2010;26(6):564-69. doi: 10.1016/j.rcim.2010.06.023
Kuo Y. Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Computers & Industrial Engineering. 2010;59(1):157-65. doi: 10.1016/j.cie.2010.03.012
US Department of Energy. Fuel Economy Guide; 2008. Available from: http://www.fueleconomy.gov
Ebrahimi Qazvini Z, Amalnick S, Mina H. A green multi-depot location routing model with split-delivery and time window. Int. J. Management Concepts and Philosophy. 2016;9(4):271-282. doi: 10.1504/IJMCP.2016.079837
Ćirović G, Pamučar D, Božanić D. Green logistic vehicle routing problem: routing light delivery vehicles in urban areas using a neuro-fuzzy model. Expert Systems with Applications. 2014;41(9):4245-58. doi: 10.1016/j.eswa.2014.01.005
Qian J, Eglese R. Fuel emissions optimization in vehicle routing problems with time varying speeds. European Journal of Operational Research. 2016;248(3):840-48. doi: 10.1016/j.ejor.2015.09.009
Felipe Á, Ortuño MT, Righini G, Tirado G. A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transportation Research Part E: Logistics and Transportation Review. 2014;71(1):111-28. doi: 10.1016/j.tre.2014.09.003
Kramer R, Maculan N, Subramanian A, Vidal T. A speed and departure time optimization algorithm for the pollution-routing problem. European Journal of Operational Research. 2015;247(3):782-87. doi: 10.1016/j.ejor.2015.06.037
Koç Ç, Karaoglan I. The green vehicle routing problem: a heuristic based exact solution approach. Applied Soft Computing. 2016;39(1):154-64. doi: 10.1016/j.asoc.2015.10.064
Ramezanzadeh BR, Kianfar F. A fuzzy mathematical model for supplier selection and order allocation considering green vehicle routing problem. Int. J. Logistics Systems and Management. 2017;27(2):151-163. doi: 10.1504/IJLSM.2017.083811
Beamon MB. Measuring supply chain performance. International Journal of Operations & Production Management. 1999;19(3):275-92. doi: 10.1108/01443579910249714
Guan R, Zhao X. On contracts for VMI program with continuous review (r, Q) policy. European Journal of Operational Research. 2010;207(2):656-67. doi: 10.1016/j.ejor.2010.04.037
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.
The mission of our journal is to provide high quality communication of research results, which is crucial for the effectiveness of scientific research, the promotion of scientific information, new ideas, and original research in the field of traffic and transportation engineering.
1848-4069
0353-5320
Proud publisher
Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal
