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Improved artificial bee colony algorithm for vehicle routing problem with time windows

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  • Baozhen Yao
  • Qianqian Yan
  • Mengjie Zhang
  • Yunong Yang

Abstract

This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW.

Suggested Citation

  • Baozhen Yao & Qianqian Yan & Mengjie Zhang & Yunong Yang, 2017. "Improved artificial bee colony algorithm for vehicle routing problem with time windows," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-18, September.
    • Handle: RePEc:plo:pone00:0181275
    DOI: 10.1371/journal.pone.0181275
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    References listed on IDEAS

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    Cited by:

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    3. Vincent F. Yu & Winarno & Achmad Maulidin & A. A. N. Perwira Redi & Shih-Wei Lin & Chao-Lung Yang, 2021. "Simulated Annealing with Restart Strategy for the Path Cover Problem with Time Windows," Mathematics, MDPI, vol. 9(14), pages 1-22, July.
    4. Ahmad Hammami & Mohammad Hendijani Zadeh, 2022. "Predicting earnings management through machine learning ensemble classifiers," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1639-1660, December.

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