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Volume 54 Issue 3
Jun.  2019
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Article Contents
Journal of Southwest Jiaotong University  > 2019  >  54(3): 565-572.
DUAN Zhengyu, LEI Zengxiang, SUN Shuo, YANG Dongyuan. Multi-Objective Robust Optimisation Method for Stochastic Time-Dependent Vehicle Routing Problem[J]. Journal of Southwest Jiaotong University, 2019, 54(3): 565-572. doi: 10.3969/j.issn.0258-2724.20170617
Citation: DUAN Zhengyu, LEI Zengxiang, SUN Shuo, YANG Dongyuan. Multi-Objective Robust Optimisation Method for Stochastic Time-Dependent Vehicle Routing Problem[J]. Journal of Southwest Jiaotong University, 2019, 54(3): 565-572. doi: 10.3969/j.issn.0258-2724.20170617
shu

Multi-Objective Robust Optimisation Method for Stochastic Time-Dependent Vehicle Routing Problem

doi: 10.3969/j.issn.0258-2724.20170617
  • Received Date: 15 Aug 2017
  • Rev Recd Date: 23 Oct 2017
    • Available Online: 23 Feb 2019
  • Publish Date: 01 Jun 2019
    Abstract
  • The vehicle routing problem (VRP) is a core issue of distribution logistics. In order to improve the timeliness of deliveries, a multi-objective robust optimisation model based on the minimax criterion was proposed for the stochastic time-dependent vehicle routing problem (STDVRP) with hard time windows, considering both the stochastic and time-varying nature of link travel times. A non-dominated sorting ant colony optimisation (NSACO) algorithm was designed to solve this multi-objective optimisation model for the STDVRP. The NSACO algorithm was compared with the non-dominated sorting genetic algorithm II (NSGA-II) through computational instances. The results show that for the Pareto boundary of the minimised number of vehicles, the average number of vehicles for NSACO is 3.33% less than that of NSGA-II, and for the Pareto boundary of the minimised worst travel time, the average worst travel time for NSACO is 17.49% less than that of NSGA-II.

     

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