Application of Hybrid GA-SA Heuristic for Green Location Routing Problem with Simultaneous Pickup and Delivery
Volume 1, Issue 1, December 2020, Pages: 1-10
Received: Aug. 18, 2020; Accepted: Sep. 1, 2020; Published: Sep. 10, 2020
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Setareh Abedinzadeh, Department of Industrial Engineering, University of Science and Culture, Tehran, Iran
Ali Ghoroghi, Department of Computer Engineering, University of Cardiff, Cardiff, Wales
Hamid Reza Erfanian, Department of Mathematics, University of Science and Culture, Tehran, Iran
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Satisfaction of customer, either in product quality point of view, or in delivery lead time point of view, is considered as a pivotal challenge among producers and distributers in supply chain. This leads to both augmentation of service level and declining the total costs of the supply chain. In this paper, we regarded a variant of the Location-Routing Problem (LRP) with consideration of green aspects, namely the green LRP with simultaneous pickup and delivery (GLRPSPD). This problem seeks to minimize total cost by simultaneously locating the distribution centers and designing the vehicle routes that satisfy pickup and delivery demand of each customer at the same time, in a way that ecological aspects are observed. The formulated problem was a mixed integer programming (MIP) model and it used, GAMS optimization software for solving that. Finally, to solve the real-size problem in an acceptable time, we considered a hybrid heuristic Genetic Algorithm-Simulated Annealing (GA-SA). The compared solutions of GAMS and those obtained from the hybrid GA-SA depicts that the hybrid heuristic GA-SA is proficient in terms of both computational time and the quality of the solutions obtained.
Location-routing Problem, Green Routing, Simultaneous Pickup and Delivery, Hybrid Heuristic Genetic Algorithm-Simulated Annealing
To cite this article
Setareh Abedinzadeh, Ali Ghoroghi, Hamid Reza Erfanian, Application of Hybrid GA-SA Heuristic for Green Location Routing Problem with Simultaneous Pickup and Delivery, Advances. Vol. 1, No. 1, 2020, pp. 1-10. doi: 10.11648/j.advances.20200101.11
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Christie, J. S., Satir, S., & Campus, T. P. (2006). Saving our energy sources and meeting Kyoto emission reduction targets while minimizing costs with application of vehicle logistics optimization. In Proceedings of the Annual Conference of the Transportation Association of Canada. Charlottetown, Prince Edward Island.
Figliozzi, M. A. (2009). Planning approximations to the average length of vehicle routing problems with time window constraints. Transportation Research Part B: Methodological, 43 (4), 438-447.
Goldberg, D. E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Publishing Company, Reading, MA.
Holland, J. H. (1975). Adaption in natural and artificial systems. Ann Arbor MI: The University of Michigan Press.
Ilgin, M. A., & Gupta, S. M. (2010). Environmentally conscious manufacturing and product recovery (ECMPRO): a review of the state of the art. Journal of environmental management, 91 (3), 563-591.
Jakobs, S. (1996). On genetic algorithms for the packing of polygons. European journal of operational research, 88 (1), 165-181.
Karaoglan, I., Altiparmak, F., Kara, I., & Dengiz, B. (2012). The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach. Omega, 41 (4), 465-477.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220 (4598), 671-680.
Kuo, Y., & Wang, C. C. (2011). Optimizing the VRP by minimizing fuel consumption. Management of Environmental Quality: An International Journal, 22 (4), 441 -451.
Leung, T. W., Yung, C. H., & Troutt, M. D. (2001). Applications of genetic search and simulated annealing to the two-dimensional non-guillotine cutting stock problem. Computers & industrial engineering, 40 (3), 201-214.
Lundy, M., & Mees, A. (1986). Convergence of an annealing algorithm. Mathematical programming, 34 (1), 111-124.
Melo, M. T., Nickel, S., & Saldanha-Da-Gama, F. (2009). Facility location and supply chain management–A review. European journal of operational research, 196 (2), 411 -412.
Min, H. (1989). The multiple vehicle routing problem with simultaneous delivery and pick-up points. Transportation Research Part A: General, 23 (5), 377-386.
Pradenas, L., Oportus, B., & Parada, V. (2013). Mitigation of greenhouse gas emissions in vehicle routing problems with backhauling. Expert Systems with Applications, 41 (8), 2985-2991.
Salhi, S., & Sari, M. (1997). A multi-level composite heuristic for the multi-depot vehicle fleet mix problem. European Journal of Operational Research, 113 (1), 95-112.
Smith, H. K., Laporte, G., & Harper, P. R. (2009). Locational analysis: highlights of growth to maturity. Journal of the Operational Research Society, s141 -s148.
Subramanian, A., Drummond, L. M. D. A., Bentes, C., Ochi, L. S., & Farias, R. (2010). A parallel heuristic for the vehicle routing problem with simultaneous pickup and delivery. Computers & Operations Research, 37 (11), 1899-1911.
Tasan, A. S., & Gen, M. (2012). A genetic algorithm based approach to vehicle routing problem with simultaneous pick-up and deliveries. Computers & Industrial Engineering, 62 (3), 755-761.
Urquhart, N., Hart, E., & Scott, C. (2010). Building low CO 2 solutions to the vehicle routing problem with Time Windows using an evolutionary algorithm. In Evolutionary Computation (CEC), 2111 IEEE Congress on (pp. 1 -6). IEEE.
Xiao, Y., Zhao, Q., Kaku, I., & Xu, Y. (2012). Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & Operations Research, 39 (7), 1419-1431.
Yong, P., & Xiaofeng, W. (2009). Research on a vehicle routing schedule to reduce fuel consumption. In Measuring Technology and Mechatronics Automation, 2119. ICMTMA'19. International Conference on (Vol. 3, pp. 825-827). IEEE.
Zachariadis, E. E., Tarantilis, C. D., & Kiranoudis, C. T. (2009). A guided tabu search for the vehicle routing problem with two-dimensional loading constraints. European Journal of Operational Research, 195 (3), 729-743.
Abedinzadeh, S., Ghoroghi, A., Afshar, S., & Barkhordari, M. (2017). A two echelon green supply chain with simultaneous pickup and delivery. International Journal of Transportation Engineering and Technology, 3 (2): 12-18.
Abedinzadeh, S., Arabmomeni, M., & Erfanian, HR. (2017). A Hybrid Genetic Algorithm-Simulated Annealing for Integrated Production-Distribution Scheduling in Supply Chain Management. Engineering Mathematics, 2 (1): 31-40.
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