Customer-oriented multi-objective optimization on a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows

Article type: Research Article

Authors: Zhou, Tong^a | Zhang, Shuai^a | Zhang, Dongping^{b; *} | Chan, Verner^c | Yang, Sihan^a | Chen, Mengjiao^a

Affiliations: [a] School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China | [b] College of Information Engineering, China Jiliang University, Hangzhou, China | [c] Shenzhen Bepsun Industry e-Commerce Systems Co., Ltd., Shenzhen, China

Correspondence: [*] Corresponding author. Dongping Zhang, Ph.D., Professor, College of Information Engineering, China Jiliang University, Hangzhou, 310018, China. E-mail: 06A0303103@cjlu.edu.cn.

Abstract: With the increasing demand for express delivery and enhancement of sustainable logistics, the collaborative multi-depot delivery based on electric vehicles has gradually attracted the attention of logistics industry. However, most of the existing studies assumed that the products required by different customers could be delivered from any homogeneous depot, ignoring the limitations in facilities and environment of depots in reality. Thus, this study proposed a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows and battery swapping, which not only involves the multi-heterogeneous-depot to meet different customer demands, but also considers the constraints of mixed time windows to ensure timely delivery. Furthermore, a customer-oriented multi-objective optimization model minimizing both travel costs and time window penalty costs is proposed to effectively improve both delivery efficiency and customer satisfaction. To solve this model, an extended non-dominated sorting genetic algorithm-II is proposed. This combines a new coding scheme, a new initial population generation method, three crossover operators, three mutation operators, and a particular local search strategy to improve the performance of the algorithm. Experiments were conducted to verify the effectiveness of the proposed algorithm in solving the proposed model.

Keywords: Electric vehicle routing problem, multi-objective optimization, collaborative multi-heterogeneous-depot, mixed time windows, nondominated sorting genetic algorithm-II

DOI: 10.3233/JIFS-223298

Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3787-3805, 2023