Journal of System Simulation ›› 2024, Vol. 36 ›› Issue (11): 2528-2541.doi: 10.16182/j.issn1004731x.joss.23-0863

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A Hybrid Genetic Search Algorithm for Capacitated Electric Vehicle Routing Problem

Jin Dongyao1,2, Liu Min1,2, Zhu Yena1,2, Zhao Yijiang1,2   

  1. 1.School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
    2.Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China
  • Received:2023-07-09 Revised:2023-09-21 Online:2024-11-13 Published:2024-11-19
  • Contact: Liu Min

Abstract:

The capacitated electric vehicle routing problem (CEVRP) is an NP-hard combinatorial optimization problem in logistics distribution, aiming to minimize the total delivery distance of electric vehicles while satisfying carrying capacity and battery charge constraints. A hybrid genetic search algorithm is proposed to solve CEVRP by decomposing it into two subproblems: capacitated vehicle routing problem (CVRP) and fixed-route vehicle charging problem (FRVCP). A coding scheme with a two-layer chromosome structure is designed to represent the decision variables of these two subproblems. A Split operation is employed to generate vehicle routes for solving CVRP, and five neighborhood search operators, including Relocate, 2-Opt, 2-Opt*, SWAP, and SWAP*, are adopted to perform local optimization on the routes. To solve the FRVCP, a backtracking-based charging strategy is proposed to insert the appropriate charging station number into the routes. The proposed algorithm is compared with five methods on CEVRP benchmark instances. Experimental results show that the proposed algorithm can find better solutions than the other methods in most instances and is especially suitable for solving large-scale CEVRP.

Key words: electric vehicle routing problem, combinatorial optimization, hybrid genetic search, charging strategy, neighborhood search

CLC Number: