期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 91, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2020.103582
关键词
Dynamic vehicle routing; Demand coverage diversity; Ant colony optimization
类别
资金
- National Natural Science Foundation of China [61672033, 61822301, U1804262, 61772290]
- recruitment program for Leading Talent Team of Anhui Province [2019-16]
- State Key Laboratory of Synthetical Automation for Process Industries, China [PALN201805]
- Anhui Provincial Natural Science Foundation, China [1808085J06, 1908085MF219]
- Key Program of Natural Science Project of Educational Commission of Anhui Province, China [KJ2017A013]
- Fundamental Research Funds for the Central Universities, China [63192616]
Dynamic vehicle routing problem (DVRP) has attracted increasing attention due to its wide applications in logistics. Compared with the static vehicle routing problem, DVRP is characterized by the prior unknown customer requests dynamically appearing in route execution. Nevertheless, the newly appeared customers pose a great challenge to route optimizer, since the optimized route may be contrarily of bad quality when including the new customers that are far from planned routes in route planning. To address this issue, in this paper we propose a demand coverage diversity based metaheuristic, termed ACO-CD, in the framework of ant colony algorithm. In ACO-CD, a demand coverage diversity adaptation method is suggested to maintain the diversity of covered customers in routes so that the optimizer can effectively response to the newly appeared customer requests. Experimental results on 27 DVRP test instances demonstrate the effectiveness of the proposed demand coverage diversity adaptation method and the superiority of the proposed ACO-CD over four state-of-the-art DVRP algorithms in terms of solution quality.
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