电动汽车-无人机成本最优路由问题的自适应记忆算法

IF 7.9 1区工程技术 Q1 ENGINEERING, CIVIL

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI:10.1109/TITS.2024.3467219

Setyo Tri Windras Mara;Ruhul Sarker;Daryl Essam;Saber Elsayed

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引用次数: 0

摘要

本文考虑的是由电动汽车和无人机组成的车队协同运送货物的问题。为了确定电动汽车-无人机路由问题的最优路线，该问题被表述为一个混合整数线性程序，以最小化总运营成本。为解决该模型，我们开发了一种自适应记忆算法，该算法采用了多算子概念、基于 Q 学习的选择机制和一组局部搜索算子，用于探索问题的复杂搜索空间。通过大量的数值实验，我们证明了我们建议的有效性，并揭示了一些有趣的管理见解。

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An Adaptive Memetic Algorithm for a Cost-Optimal Electric Vehicle-Drone Routing Problem

This paper considers a fleet of electric vehicles and drones that deliver goods collaboratively. To determine the optimal routes of this electric vehicle-drone routing problem, the problem is formulated as a mixed-integer linear program to minimize the total operational costs. To solve the model, we develop an adaptive memetic algorithm that employs a multi-operator concept with a Q-learning-based selection mechanism and a set of local search operators for exploring the complex search space of the problem. Using extensive numerical experiments, we prove the effectiveness of our proposal and reveal some interesting managerial insights.

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来源期刊

IEEE Transactions on Intelligent Transportation Systems 工程技术-工程：电子与电气

CiteScore

14.80

自引率

12.90%

发文量

1872

审稿时长

7.5 months

期刊介绍： The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.