Solving Capacitated Location Routing Problem by Variable Neighborhood Descent and GA-Artificial Neural Network Hybrid Method
This paper aims to find the optimal depot locations and vehicle routings for spare parts of an automotive company considering future demands. The capacitated location-routing problem (CLRP), which has been practiced by various methods, is performed to find the optimal depot locations and routings by additionally using the artificial neural network (ANN). A novel multi-stage approach, which is performed to lower transportation cost, is carried out in CLRP. Initially, important factors for customer demand are tested with an univariate analysis and used as inputs in the prediction step. Then, genetic algorithm (GA) and ANN are hybridized and applied to provide future demands. The location of depots and the routings of the vehicles are determined by using the variable neighborhood descent (VND) algorithm. Five neighborhood structures, which are either routing or location type, are implemented in both shaking and local search steps. GA-ANN and VND are applied in the related steps successfully. Thanks to the performed VND algorithm, the company lowers its transportation cost by 2.35% for the current year, and has the opportunity to determine optimal depot locations and vehicle routings by evaluating the best and the worst cases of demand quantity for ten years ahead.
Berglund, P. G., & Kwon, C. (2014). Robust facility location problem for hazardous waste transportation. Networks and Spatial Economics, 14(1), 91-116.
Chen, H. L., Yang, B., Wang, G., Liu, J., Xu, X., Wang, S. J., & Liu, D. Y. (2011). A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowledge-Based Systems, 24(8), 1348-1359.
Drexl, M. (2013). Applications of the vehicle routing problem with trailers and transshipments. European Journal of Operational Research, 227(2), 275-283.
Duhamel, C., Lacomme, P., Prins, C., &Prodhon, C. (2010). A GRASP× ELS approach for the capacitated location-routing problem. Computers & Operations Research, 37(11), 1912-1923.
Escobar, J. W. (2014). Heuristic algorithms for the capacitated location-routing problem and the multi-depot vehicle routing problem. 4OR, 12(1), 99.
Hansen P. & Mladenović N. (2003). Variable neighborhood search. Handbook of metaheuristics. Boston, Dordrecht, London: Kluwer Academic Publisher, 145-184.
Jarboui, B., Derbel, H., Hanafi, S., & Mladenović, N. (2013). Variable neighborhood search for location routing. Computers & Operations Research, 40(1), 47-57.
Kadiyala, A., Kaur, D., & Kumar, A. Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus. Journal of the Air & Waste Management Association 63(2) (2013) 205-218.
Karaoglan, I., & Altiparmak, F. (2015). A memetic algorithm for the capacitated location-routing problem with mixed backhauls. Computers & Operations Research, 55, 200-216.
Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers & operations research, 24(11), 1097-1100.
Pekel, E., & Soner Kara, S. (2017). Passenger Flow Prediction Based on Newly Adopted Algorithms. Applied Artificial Intelligence, 31(1), 64-79.
Prodhon, C., & Prins, C. (2014). A survey of recent research on location-routing problems. European Journal of Operational Research, 238(1), 1-17.
Copyright (c) 2018 Engin PEKEL, Selin SONER KARA
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