基于动态请求的车辆路径问题的混合启发式算法

IF 5 3区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Egyptian Informatics Journal Pub Date : 2025-03-01 DOI:10.1016/j.eij.2025.100638

Shifeng Chen , Yanlan Yin , Haitao Sang , Wu Deng

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引用次数: 0

摘要

提出了一种结合贪婪随机自适应搜索算法（GRASP）和可变邻域下降算法（VND）的混合启发式算法来解决带有动态请求的车辆路由问题。VRPDR是经典车辆路径问题（VRP）的一个动态分支，其特征是客户需求随着时间的推移而出现，其目标是通过设计一组路线来服务所有客户，从而使总行程距离最小化。该方法首先利用GRASP构建初始解，然后利用VND进行探索和细化。混合方法旨在利用两种算法的优势。通过在动态拾取实例和动态交付实例两组基准实例上的测试，确定了动态拾取实例15个新的最优解，动态交付实例11个新的最优解。这些结果清楚地表明，所提出的算法与文献中记录的算法竞争有利。

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A hybrid GRASP and VND heuristic for vehicle routing problem with dynamic requests

This paper describes a hybrid heuristic that integrates the Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) to address the Vehicle Routing Problem with Dynamic Requests (VRPDR). The VRPDR, a dynamic offshoot of the classical Vehicle Routing Problem (VRP), features customer requests emerging over time, with the objective of minimizing the total travel distance by devising a set of routes to serve all customers. The proposed method initially employs GRASP to construct an initial solution, followed by VND for exploration and refinement. The hybrid approach aims to utilize the strengths of both algorithms. Through testing on two sets of benchmark instances, namely dynamic pickup instances and dynamic delivery instances, 15 new optimal solutions are identified for the former and 11 for the latter. These results clearly demonstrate that the proposed algorithm competes favorably with the algorithms documented in the literature.

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来源期刊

Egyptian Informatics Journal Decision Sciences-Management Science and Operations Research

CiteScore

11.10

自引率

1.90%

发文量

审稿时长

110 days

期刊介绍： The Egyptian Informatics Journal is published by the Faculty of Computers and Artificial Intelligence, Cairo University. This Journal provides a forum for the state-of-the-art research and development in the fields of computing, including computer sciences, information technologies, information systems, operations research and decision support. Innovative and not-previously-published work in subjects covered by the Journal is encouraged to be submitted, whether from academic, research or commercial sources.