Double-assistant evolutionary multitasking algorithm for enhanced electric vehicle routing with backup batteries and battery swapping stations

Cai, Yanguang; Wu, Yanlin; Fang, Chuncheng

doi:10.1016/j.eswa.2023.121600

Cited by 4 publications

(1 citation statement)

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“…They applied the ALNS algorithm to solve the model on test problems, and the findings demonstrated the efficiency of the algorithm. Cai et al [59] investigated some of the main issues of EVRP with backup batteries, battery swapping stations, and time windows to simultaneously optimize different tasks by exploiting a novel meta-heuristic algorithm. They also validated their model and algorithm using 30 numerical examples as well as four real-world problems.…”

Section: Literature Reviewmentioning

confidence: 99%

A Sustainable Multi-Objective Model for Capacitated-Electric-Vehicle-Routing-Problem Considering Hard and Soft Time Windows as Well as Partial Recharging

Azadi,

Khalilzadeh,

Antucheviciene

et al. 2024

Biomimetics

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Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied in recent years. This paper deals with an extended version of EVRP, in which electric vehicles (EVs) deliver goods to customers. The limited battery capacity of EVs causes their operational domains to be less than those of gasoline vehicles. For this purpose, several charging stations are considered in this study for EVs. In addition, depending on the operational domain, a full charge may not be needed, which reduces the operation time. Therefore, partial recharging is also taken into account in the present research. This problem is formulated as a multi-objective integer linear programming model, whose objective functions include economic, environmental, and social aspects. Then, the preemptive fuzzy goal programming method (PFGP) is exploited as an exact method to solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired by nature, including MOSA, MOGWO, MOPSO, and NSGAII_TLBO, are utilized to solve large-sized problems. The results obtained from solving the numerous test problems demonstrate that the hybrid meta-heuristic algorithm can provide efficient solutions in terms of quality and non-dominated solutions in all test problems. In addition, the performance of the algorithms was compared in terms of four indexes: time, MID, MOCV, and HV. Moreover, statistical analysis is performed to investigate whether there is a significant difference between the performance of the algorithms. The results indicate that the MOSA algorithm performs better in terms of the time index. On the other hand, the NSGA-II-TLBO algorithm outperforms in terms of the MID, MOCV, and HV indexes.

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Section: Literature Reviewmentioning

confidence: 99%