4.7 Article

An improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectives

期刊

JOURNAL OF CLEANER PRODUCTION
卷 227, 期 -, 页码 1161-1172

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2019.03.185

关键词

Vehicle routing problem (VRP); Improved ant colony optimization (IACO) algorithm; Multi-depot green vehicle routing problem (MDGVRP)

资金

  1. National Social Science Foundation of China [14BJL045]
  2. Shandong provincial social science planning research project [18BCXJ03]
  3. Qingdao social science planning project [QDSKL1801043]
  4. Shandong university humanities and social science research project [J18RB189]

向作者/读者索取更多资源

Vehicle routing problem (VRP) is one of the widely researched areas in transportation science, mainly due to the potential cost savings and service improvement opportunities which brings to organizations involved in physical distribution of goods. In this paper, we develop a multi-depot green vehicle routing problem (MDGVRP) by maximizing revenue and minimizing costs, time and emission, and then, apply an improved ant colony optimization (IACO) algorithm that aims to efficiently solve the problem. The IACO model developed in this research uses an innovative approach in updating the pheromone that results in better solutions. The results achieved through the IACO demonstrate satisfying performance, which have higher solution quality when compared to the conventional ACO. The IACO algorithm used in this paper demonstrated a good level of responsiveness and simplicity when solving MDGVRP with multiple objectives. (C) 2019 Published by Elsevier Ltd.

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