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International Conference on Bio-Inspired Computing: Theories and Applications

BIC-TA 2019: Bio-inspired Computing: Theories and Applications pp 244–254Cite as

A Hybrid Ant Colony Optimization Algorithm for the Fleet Size and Mix Vehicle Routing Problem with Time Windows

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

Abstract

The fleet size and mix vehicle routing problem with time window (FSMVRPTW) is a combinatorial optimization and decision making problem. This problem requires the use of a fleet to provide services to customers at a minimum cost. In this paper, a hybrid ant colony algorithm for FSMVRPTW is proposed, which is composed of an insertion heuristic algorithm and an ant colony system. The ant colony optimization algorithm is used to generate an initial solution with the constraints of maximum capacity and time windows, and then the routing in the initial solution is divided into partial routing and individual customers. The insertion heuristic algorithm is used to reconstruct the solution by taking into acco unt the factors of time windows, distance, utilization and vehicle cost. Experiments on benchmark problems prove the feasibility of the algorithm. The hybrid algorithm expands the application of ant colony optimization algorithm and provides a new idea for solving FSMVRPTW.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Xiaodong Zhu & Ding Wang

Authors
  1. Xiaodong Zhu
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  2. Ding Wang
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Corresponding authors

Correspondence to Xiaodong Zhu or Ding Wang .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Zhu, X., Wang, D. (2020). A Hybrid Ant Colony Optimization Algorithm for the Fleet Size and Mix Vehicle Routing Problem with Time Windows. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_20

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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