Integrated scheduling problem of multi-load AGVs and parallel machines considering the recovery process

In modern manufacturing workshops, parallel machine scheduling and automated guided vehicle (AGV) scheduling are two closely coupled problems. However, the two problems are often solved independently, which reduces the performance of manufacturing system to a large extent. To address this issue, thi...

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Veröffentlicht in:Swarm and evolutionary computation Jg. 94; S. 101861
Hauptverfasser: Fan, Xin, Sang, Hongyan, Tian, Mengxi, Yu, Yang, Chen, Song
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.04.2025
Schlagworte:
Automated guided vehicle scheduling
Discrete grey wolf algorithm
Local search
Parallel machines scheduling
Discrete grey wolf algorithm
Local search
Automated guided vehicle scheduling
Parallel machines scheduling
ISSN:2210-6502
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Zusammenfassung:In modern manufacturing workshops, parallel machine scheduling and automated guided vehicle (AGV) scheduling are two closely coupled problems. However, the two problems are often solved independently, which reduces the performance of manufacturing system to a large extent. To address this issue, this paper investigates the integrated scheduling problem of multi-load AGV and parallel machine considering the recovery process (MAGVPM-R). Firstly, a mathematical model is established to optimize the completion time. Second, a weight priority integration heuristic (WPIH) and four neighborhood operators are designed based on MAGVPM-R characteristics. Third, a discrete grey wolf optimization (DGWO) algorithm is proposed. Finally, the mathematical model is validated using the GUROBI solver and the performance of DGWO is tested with 100 instances of different scales. The experimental results show that DGWO solves the MAGVPM-R problem better than other competing algorithms.
ISSN:2210-6502
DOI:10.1016/j.swevo.2025.101861