Saiqi Zhou , Dezhi Zhang , Shiyan Fang , Shuangyan Li
{"title":"An adaptive hybrid neighborhood search algorithm for the electric vehicle pickup and delivery problem with time windows and partial charging","authors":"Saiqi Zhou , Dezhi Zhang , Shiyan Fang , Shuangyan Li","doi":"10.1016/j.swevo.2025.102042","DOIUrl":null,"url":null,"abstract":"<div><div>Environmental pressures and measures are compelling the extensive integration of electric vehicles into transportation and logistics systems. This paper focuses on addressing the electric vehicle pickup and delivery problem with time windows and partial charging, in which the amount of charging electricity at charging stations is flexible and determined based on the route schedules. A new effective mixed-integer linear programming model has been developed for the problem. To effectively tackle large-scale instances, we propose an adaptive hybrid neighborhood search algorithm, which is based on the framework of the adaptive large neighborhood search algorithm. The proposed algorithm incorporates various problem-oriented search operators being adaptively chosen for evolution. Meanwhile, dynamic programming-based charging approaches for both full and partial charging policies are presented. Numerical experiments are conducted using benchmark instances of the electric vehicle pickup and delivery problem to verify the effectiveness of our algorithm configurations and its overall performance. The solution results are compared against those obtained using the state-of-the-art algorithm, and the proposed algorithm identifies 21 new best solutions and exhibits greater stability, which demonstrates the competitiveness of the proposed algorithm. Furthermore, the analysis of charging policies provides interesting insights, highlighting the significant advantage of the partial charging policy in scenarios characterized by clustered customer distributions or short scheduling horizons.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102042"},"PeriodicalIF":8.2000,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225002007","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Environmental pressures and measures are compelling the extensive integration of electric vehicles into transportation and logistics systems. This paper focuses on addressing the electric vehicle pickup and delivery problem with time windows and partial charging, in which the amount of charging electricity at charging stations is flexible and determined based on the route schedules. A new effective mixed-integer linear programming model has been developed for the problem. To effectively tackle large-scale instances, we propose an adaptive hybrid neighborhood search algorithm, which is based on the framework of the adaptive large neighborhood search algorithm. The proposed algorithm incorporates various problem-oriented search operators being adaptively chosen for evolution. Meanwhile, dynamic programming-based charging approaches for both full and partial charging policies are presented. Numerical experiments are conducted using benchmark instances of the electric vehicle pickup and delivery problem to verify the effectiveness of our algorithm configurations and its overall performance. The solution results are compared against those obtained using the state-of-the-art algorithm, and the proposed algorithm identifies 21 new best solutions and exhibits greater stability, which demonstrates the competitiveness of the proposed algorithm. Furthermore, the analysis of charging policies provides interesting insights, highlighting the significant advantage of the partial charging policy in scenarios characterized by clustered customer distributions or short scheduling horizons.
期刊介绍:
Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.