An effective multi-restart iterated greedy algorithm for multi-AGVs dispatching problem in the matrix manufacturing workshop

An excellent automated guided vehicle (AGV) scheduling scheme can effectively improve the productivity of automated manufacturing factories and reduce costs, so the AGV dispatching problem (AGVDP) has become a research hotspot. This paper studies AGVDP with AGV capacity and the latest delivery time...

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Bibliographic Details
Published in:Expert systems with applications Vol. 252; p. 124223
Main Authors: Liu, Zi-Jiang, Sang, Hong-Yan, Zheng, Chang-Zhe, Chi, Hao, Gao, Kai-Zhou, Han, Yu-Yan
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.10.2024
Subjects:
Automated guided vehicle
Dispatching
Iterated greedy algorithm
Matrix manufacturing workshop
Matrix manufacturing workshop
Iterated greedy algorithm
Dispatching
Automated guided vehicle
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:An excellent automated guided vehicle (AGV) scheduling scheme can effectively improve the productivity of automated manufacturing factories and reduce costs, so the AGV dispatching problem (AGVDP) has become a research hotspot. This paper studies AGVDP with AGV capacity and the latest delivery time to reduce the cost of the total objective consisting of AGV cost, travel cost, and penalty cost. To this end, a mixed-integer linear programming model and a multi-restart iterated greedy (MRIG) algorithm are proposed. According to the characteristics of the problem, an improved nearest neighbor-based heuristic algorithm (INNH) is proposed to generate an initial solution. INNH includes a balanced two-factor selection strategy and a multiple-try insertion strategy. In the MRIG, the destruction stage has been improved to have an adaptive parameter. To enhance the quality of the initial solution and the depth of the evolution, two improved reference local searches are proposed. Furthermore, a multi-restart strategy with a checking mechanism and five restart methods is designed to avoid MRIG falling into the local optimum. The results of sufficient statistical experiments show that the MRIG is obviously superior to the existing algorithms when solving AGVDP.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2024.124223