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An effective self-adaptive iterated greedy algorithm for a multi-AGVs scheduling problem with charging and maintenance by Wen-qiang Zou; Quan-ke Pan; Lei-lei Meng; Hong-yan Sang; Yu-yan Han; Jun-qing Li is a Computer Science article available to read on EtoBox.

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The automatic guided vehicle (AGV) scheduling problem in matrix manufacturing workshop has been a research hotspot in recent years because of its wide industrial applications. However, the research on multi-AGVs scheduling problem with charging and maintenance (MAGVSCM) is rarely reported. In the paper, the MAGVSCM is investigated to reduce the total cost composed of travel cost, penalty cost and vehicle cost. To this end, a mixed-integer linear programming model and a self-adaptive iterated greedy (SAIG) algorithm are proposed. Based on the problem characteristics, a new solution presentation and the solution accelerated evaluation method are presented and applied in the SAIG. In the SAIG, several nearest-neighbor-based improved heuristics are proposed and used to generate an initial solution. A destruction procedure with self-adaptive strategy is developed to enhance the exploration capability of the algorithm. A merging local search method is used to reduce the number of AGVs as much as possible without lowering the total cost. An acceptance criterion with restart strategy is designed to determine the current solution in the next iteration. A large number of experimental results

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Author

Wen-qiang Zou; Quan-ke Pan; Lei-lei Meng; Hong-yan Sang; Yu-yan Han; Jun-qing Li

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)