Abstract
This paper addresses an open vehicle routing problem with predetermined time windows under uncertain travel times (OVRP-UT). A novel light-robust-optimization model is proposed by integrating the goal programming formulations with set-based descriptions of the problem data, which can enable as many customers as possible to meet their demands within a group of predetermined time windows. An effective memetic algorithm (MA) is presented for solving the OVRP-UT model. We design a heuristic-based initialization mechanism to generate an initial population with a high level of quality and diversity. We design a timely-vertices based crossover operator and mutation operator to give birth to the offspring with high quality and good structure built in the search process. We provide a hybrid selection mechanism and a population updating strategy to remain the diversity of the population. We develop a self-adapted crossover and mutation rate to help the MA suit the different phases during the search process. A comprehensive simulation experiment based on the 320 benchmark instances demonstrates the effectiveness of the proposed algorithm.
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Acknowledgements
This research is partially supported by the National Science Foundation of China 61973203 and 51575212, and Shanghai Key Laboratory of Power station Automation Technology.
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Sun, L., Pan, Qk., Jing, XL. et al. A light-robust-optimization model and an effective memetic algorithm for an open vehicle routing problem under uncertain travel times. Memetic Comp. 13, 149–167 (2021). https://doi.org/10.1007/s12293-020-00322-5
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DOI: https://doi.org/10.1007/s12293-020-00322-5