Abstract
Green Pickup-and-Delivery Problem with Time-Windows (Green-PDPTW) is a new sub-problem of the Capacitated Vehicle Routing Problem (CVRP). It aims to solve PDPTW in a way that emits the least amount of greenhouse gases. Adaptive Large Neighborhood Search (ALNS) is a commonly used algorithm to solve such problems, but usually, it focuses more on expanding the search range rather than giving a clear search direction. Therefore, we propose Distance-based ALNS (DALNS), using the distance between customers as an important factor when generating initial solution and destroy solutions to searching. We also add a heuristic on the number of orders to be removed in each iteration of DALNS. From simulation experiments, we draw the conclusion that DALNS has a significant effect on reducing greenhouse gas emissions and retaining higher economic benefits for the enterprise at the same time. In addition, we find that DALNS shows great performance on instances where customers are clustered and a load of vehicles is high.
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References
Affi, M., Derbel, H., Jarboui, B.: Variable neighborhood search algorithm for the green vehicle routing problem. Int. J. Ind. Eng. Comput. 9(2), 195–204 (2018)
Christiaens J., Vanden Berghe, G.: Slack induction by string removals for vehicle routing problems. Institute for Operations Research and the Management Sciences (02) (2019)
Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manag. Sci. 6(1), 80–91 (1959)
Defra, J.: Environmental reporting guidelines: including mandatory greenhouse gas emissions reporting guidance, report. Department for Environment Food & Rural Affairs. https://www.gov.uk/government/publications. Accessed 17 Apr 2020
Hemmelmayr, C.V., Cordeau, J.F., Cranic, T.: An adaptive large neighborhood search heuristic for two-echelon vehicle routing problems arising in city logistics. Comput. Oper. Res. 39, 3215–3228 (2012)
Kirkpatrick, S., Gelatt, C., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Kunnapapdeelert, S., Kachitvichyanukul, V.: New enhanced differential evolution algorithms for solving multi-depot vehicle routing problem with multiple pickup and delivery requests. Int. J. Serv. Oper. Manag. 31(3), 370–395 (2018)
Li, H., Lim, A.: A metaheuristic for the pickup and delivery problem with time windows. Int. J. Artif. Intell. Tools 12(02), 173–186 (2003)
Montoya, A., Gueret, C., Mendoza, J.E., Villegas, J.G.: A multi-space sampling heuristic for the green vehicle routing problem. Transp. Res. Part C: Emerg. Technol. 70, 113–128 (2016)
Prescott-Gagnon, E., Desaulniers, G., Rousseau, L.-M.: Heuristics for an oil delivery vehicle routing problem. Flex. Serv. Manuf. J. 26(4), 516–539 (2012). https://doi.org/10.1007/s10696-012-9169-9
Ribeiro, G.M., Laporte, G.: An adaptive large neighborhood search heuristic for the cumulative capacitated vehicle routing problem. Comput. Oper. Res. 39, 728–735 (2012)
Roberto, D., Mauceri, S., Carroll, P.: A genetic algorithm for a green vehicle routing problem. Electron. Notes Discret. Math. 64, 65–74 (2018)
Ropke S., Pisinger D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Technical report, Department of Computer Science, University of Copenhagen (2004)
Shaw, P.: A new local search algorithm providing high quality solutions to vehicle routing problems. APES Group. Dept of Computer Science, University of Strathclyde, Scotland, UK (1997)
Li, S., Benchmark, L. https://www.sintef.no/projectweb/top/pdptw/li-lim-benchmark/100-customers. Accessed 17 Apr 2020
Toth, P., Vigo, D. (eds): The Vehicle Routing Problem. Monographs on Discrete Mathematics and Applications, p. 9. Society for Industrial and Applied Mathematics, Philadelphia (2002)
Ubeda, S., Faulin, J., Serrano, A., Arcelus, F.J.: Solving the green capacitated vehicle routing problem using a tabu search algorithm. Lect. Notes Manag. Sci. 6(1), 141–149 (2014)
Acknowledgement
This work is financially supported by National key R&D program under Grant No. 2017YFB0803002 and No. 2016YFB0800804, National Natural Science Foundation of China under Grant No. 61672195 and No. 61732022.
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Lu, J., Huang, H. (2020). Distance-Based Adaptive Large Neighborhood Search Algorithm for Green-PDPTW. In: Zhang, Z., Li, W., Du, DZ. (eds) Algorithmic Aspects in Information and Management. AAIM 2020. Lecture Notes in Computer Science(), vol 12290. Springer, Cham. https://doi.org/10.1007/978-3-030-57602-8_33
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DOI: https://doi.org/10.1007/978-3-030-57602-8_33
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