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标题:A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows
时间:2020-01-15 18:09:42
DOI:10.1016/j.cor.2015.05.004
作者:Koc, Cagri;Bektas, Tolga;Jabali, Ola
关键词:Vehicle routing;Time windows;Heterogeneous fleet;Genetic algorithm;Neighborhood search;
出版源: 《Computers & Operations Research》 ,64 :11-27
摘要:This paper presents a hybrid evolutionary algorithm (HEA) to solve heterogeneous fleet vehicle routing problems with time windows. There are two main types of such problems, namely the fleet size and mix vehicle routing problem with time windows (F) and the heterogeneous fixed fleet vehicle routing problem with time windows (H), where the latter, in contrast to the former, assumes a limited availability of vehicles. The main objective is to minimize the fixed vehicle cost and the distribution cost, where the latter can be defined with respect to en-route time (T) or distance (D). The proposed unified algorithm is able to solve the four variants of heterogeneous fleet routing problem, called FT, FD, HT and HD, where the last variant is new. The HEA successfully combines several metaheuristics and offers a number of new advanced efficient procedures tailored to handle the heterogeneous fleet dimension. Extensive computational experiments on benchmark instances have shown that the HEA is highly effective on FT, FD and HT. In particular, out of the 360 instances we obtained 75 new best solutions and matched 102 within reasonable computational times. New benchmark results on HD are also presented. (C) 2015 Elsevier Ltd. All rights reserved.
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页数:18 PAGES
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目录:
  • A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows
    • Introduction
    • Description of the hybrid evolutionary algorithm
      • Overview of the hybrid evolutionary algorithm
      • Education
        • Removal operators
        • Insertion operators
        • Adaptive weight adjustment procedure
      • Initialization
      • Parent selection
      • Crossover
      • Split algorithm
      • Intensification
      • Survivor selection
      • Diversification
    • Computational experiments
      • Data sets and experimental settings
      • Comparative analysis
    • Conclusions
    • Acknowledgment
    • Appendix
    • References

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