A hybrid priority-based genetic algorithm for simultaneous pickup and delivery problems in reverse logistics with time windows and multiple decision-makers

Abstract

This article puts forward a hybrid priority-based nested genetic algorithm with fuzzy logic controller and fuzzy random simulation (hpn-GA with FLC–FRS) for solving a variant of the vehicle routing problem. To meet all the complex restrictions contained in practical reverse logistics, a new mathematical model is developed for simultaneous pickup and delivery problems with time windows and multiple decision-makers (SPDTW–MDM). Then, a hpn-GA with FLC–FRS is proposed, where the priority-based initializing method makes the initializing more applicable, a nested procedure structure handles multiple decision-makers, a fuzzy logic controller helps adjust the mutation rate, and a fuzzy random simulation is used to deal with uncertainties. Finally, in the case study, GA parameters are tuned by Taguchi method and result analyses are presented to highlight the performance of the optimization method for the SPDTW–MDM, while algorithm comparisons by instance applications in different scales show its efficiency and effectiveness.

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Acknowledgements

This research was supported by Natural Science Foundation of China (Grant Nos. 71640013, 71601134, 71401020, and 71702167) and China Postdoctoral Science Foundation (Grant No. 2018T110609).

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Correspondence to Fang Yan.

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Ma, Y., Li, Z., Yan, F. et al. A hybrid priority-based genetic algorithm for simultaneous pickup and delivery problems in reverse logistics with time windows and multiple decision-makers. Soft Comput 23, 6697–6714 (2019). https://doi.org/10.1007/s00500-019-03754-5

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Keywords

  • Vehicle routing problem
  • Simultaneous pickup and delivery
  • Reverse logistics
  • Genetic algorithm
  • Fuzzy random variable