Abstract
The paper studies a vehicle routing problem with simultaneous pickups and deliveries that arises in the retail sector, which considers a heterogeneous fleet of vehicles, time windows of the demands, practical restrictions on the drivers and a roster specifying the order of vehicle loading at the depot. The high competition in this industry requires that a viable optimisation approach must achieve a good balance of solution time, quality and robustness. In this paper, a novel iterated local search algorithm is proposed which dynamically reduces the neighbourhood so that only the most promising moves are considered. The results of computational experiments on real-world data demonstrate the high efficiency of the presented optimisation procedure in terms of computation time, stability of the optimisation procedure and solution quality.
Supported by an Australian Government Research Training Program Scholarship.
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Gu, H., MacMillan, L., Zhang, Y., Zinder, Y. (2021). Iterated Local Search with Neighbourhood Reduction for the Pickups and Deliveries Problem Arising in Retail Industry. In: Dorronsoro, B., Amodeo, L., Pavone, M., Ruiz, P. (eds) Optimization and Learning. OLA 2021. Communications in Computer and Information Science, vol 1443. Springer, Cham. https://doi.org/10.1007/978-3-030-85672-4_14
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