Abstract
Supermarket supply chains represent an area in which optimisation of vehicle routes and scheduling can lead to huge cost and environmental savings. As just-in-time ordering practices become more common, traditionally fixed resupply routes and schedules are increasingly unable to meet the demands of the supermarkets. Instead, we model this as a dynamic pickup and delivery problem with soft time windows (PDPSTW). We present the variable neighbourhood descent with memory (VNDM) hybrid metaheuristic (HM) and compare its performance against Q-learning (QL), binary exponential back off (BEBO) and random descent (RD) hyperheuristics on published benchmark and real-world instances of the PDPSTW. We find that VNDM consistently generates the highest quality solutions, with the fewest routes or shortest distances, amongst the methods tested. It is capable of finding the best known solutions to 55 of 176 published benchmarks as well as producing the best results on our real-world data set, supplied by Transfaction Ltd.
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References
Belhaiza, S., Hansen, P., Laporte, G.: A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows. Comput. Oper. Res. 52(part B), 269–281 (2013)
Bent, R., Van Hentenryck, P.: A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 123–137. Springer, Heidelberg (2003)
Blocho, M.: A parallel algorithm for minimizing the fleet size in the pickup and delivery problem with time windows. In: Proceedings of the 22nd European MPI Users’ Group Meeting, pp. 20–21. ACM (2015)
Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: a survey. Appl. Soft Comput. 11(6), 4135–4151 (2011)
Bräysy, O.: A reactive variable neighborhood search for the vehicle-routing problem with time windows. INFORMS J. Comput. 15(4), 347–368 (2003)
Carić, T., Fosin, J., Galić, A., Gold, H., Reinholz, A.: Empirical analysis of two different metaheuristics for real-world vehicle routing problems. In: Bartz-Beielstein, T., Blesa Aguilera, M.J., Blum, C., Naujoks, B., Roli, A., Rudolph, G., Sampels, M. (eds.) HCI/ICCV 2007. LNCS, vol. 4771, pp. 31–44. Springer, Heidelberg (2007)
Cherkesly, M., Desaulniers, G., Laporte, G.: Branch-price-and-cut algorithns for the pickup and delivery problem with time windows and LIFO loading. Comput. Oper. Res. 62(1), 23–35 (2015)
Cordeau, J.F., Laporte, G.: A tabu search heuristic for the static multi-vehicle dial-a-ride problem. Transp. Res. Part B Methodol. 37, 579–594 (2003)
Cowling, P.I., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)
Desaulniers, G., Desrosiers, J., Solomon, M.M., Erdmann, A., Soumis, F.: VRP with pickup and delivery. In: Toth, P., Vigo, D. (eds.) The Vehicle Routing Problem, pp. 225–242. SIAM, Philadelphia (2002)
Dorer, K., Calisti, M.: An adaptive approach to dynamic transport optimization. In: Klugl, F., Bazzan, A., Ossowski, S. (eds.) Applications of Agent Technology in Traffic and Transportation. Whitestein Series in Software Agent Technologies, pp. 33–49. Birkhäuser, Basel (2005)
Dumas, Y., Desrosiers, J., Soumis, F.: The pickup and delivery problem with time windows. Eur. J. Oper. Res. 54(1), 7–22 (1991)
Gendreau, M., Hertz, A., Laporte, G.: New insertion and post optimization procedures for the traveling salesman problem. Oper. Res. 40(6), 1086–1095 (1992)
Gschwind, T., Irnich, S., Mainz, D.: Effective Handling of Dynamic Time Windows and Synchronization with Precedences for Exact Vehicle Routing. Technical report, Johannes Gutenberg University Mainz, Mainz, Germany. Retrieved from (2012). http://logistik.bwl.uni-mainz.de/
Hasle, G., Lie, K.A., Quak, E.: Geometric Modelling, Numerical Simuation, and Optimization. Applied Mathematics at SINTEF, vol. 54. Springer, Heidelberg (2007)
Hosny, M.I.: Investigating Heuristic and Meta-Heuristic Algorithms for Solving Pickup and Delivery Problems Manar Ibrahim Hosny School of Computer Science & Informatics. Ph.D. thesis, Cardiff University (2010)
Koning, D.: Using Column Generation for the Pickup and Delivery Problem with Disturbances. Masters thesis, Universiteit Utrecht (2011)
Laporte, G.: Fifty years of vehicle routing. Transp. Sci. 43(4), 408–416 (2009)
Li, H., Lim, A.: A metaheuristic for the pickup and delivery problem with time windows. In: Tools with Artificial Intelligence, pp. 160–167. IEEE (2001)
Ostertag, A., Doerner, K.F., Hartl, R.F.: A variable neighborhood search integrated in the POPMUSIC framework for solving large scale vehicle routing problems. In: Blesa, M.J., Blum, C., Cotta, C., Fernández, A.J., Gallardo, J.E., Roli, A., Sampels, M. (eds.) HM 2008. LNCS, vol. 5296, pp. 29–42. Springer, Heidelberg (2008)
Paraskevopoulos, D.C., Repoussis, P.P., Tarantilis, C.D., Ioannou, G., Prastacos, G.P.: A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows. J. Heuristics 14(5), 425–455 (2008)
Parragh, S.N., Doerner, K.F., Hartl, R.F.: Variable neighborhood search for the dial-a-ride problem. Comput. Oper. Res. 37(6), 1129–1138 (2009)
Pirkwieser, S., Raidl, G.R.: Multiple variable neighborhood search enriched with ILP techniques for the periodic vehicle routing problem with time windows. In: Blesa, M.J., Blum, C., Di Gaspero, L., Roli, A., Sampels, M., Schaerf, A. (eds.) HM 2009. LNCS, vol. 5818, pp. 45–59. Springer, Heidelberg (2009)
Quintiq: PDPTW World Records (2015). http://www.quintiq.com/optimization/pdptw-world-records.html
Remde, S., Cowling, P.I., Dahal, K., Colledge, N., Selensky, E.: An empirical study of hyperheuristics for managing very large sets of low level heuristics. J. Oper. Res. Soc. 63(3), 392–405 (2011)
Repoussis, P.P., Paraskevopoulos, D.C., Tarantilis, C.D., Ioannou, G.: A reactive greedy randomized variable neighborhood tabu search for the vehicle routing problem with time windows. In: Almeida, F., Blesa Aguilera, M.J., Blum, C., Moreno Vega, J.M., Pérez Pérez, M., Roli, A., Sampels, M. (eds.) HM 2006. LNCS, vol. 4030, pp. 124–138. Springer, Heidelberg (2006)
Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp. Sci. 40(4), 455–472 (2005)
Savelsbergh, M.W.P.: The vehicle routing problem with time windows: minimizing route duration. INFORMS J. Comput. 4(2), 146–154 (1992)
Statistica: Number of stores of leading grocery retailers in the United Kingdom (UK) as of (2013). http://www.statista.com/statistics/299155/number-of-stores-of-grocery-retailers-supermarkets-united-kingdom-uk/
TetraSoft, A.: MapBooking Algoritm for Pickup and Delivery Solutions with Time Windows and Capacity restraints. (2003). http://www.tetrasoft.dk/english-info/
Toth, P., Vigo, D.: Heuristic algorithms for the handicapped persons transportation problem. Transp. Sci. 31(1), 60–71 (1997)
Watkins, C.J.C.H., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)
Xu, H., Chen, Z.L., Rajagopal, S., Arunapuram, S.: Solving a practical pickup and delivery problem. Transp. Sci. 37(3), 347–364 (2003)
Acknowledgements
This work has been funded by the Large Scale Complex IT Systems (LSCITS) project of the EPSRC with help from Transfaction Ltd.
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Mourdjis, P., Chen, Y., Polack, F., Cowling, P., Robinson, M. (2016). Variable Neighbourhood Descent with Memory: A Hybrid Metaheuristic for Supermarket Resupply. In: Blesa, M., et al. Hybrid Metaheuristics. HM 2016. Lecture Notes in Computer Science(), vol 9668. Springer, Cham. https://doi.org/10.1007/978-3-319-39636-1_3
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