An intelligent automated guided vehicle scheduling framework for manufacturing: Balancing energy, efficiency, and task completion

•Developed MO-MIP model to optimize AGV scheduling in manufacturing systems•Applied NSGA-II and NSGA-III for efficient multi-objective optimization•Tested on three distinct manufacturing workshop scenarios•Outperformed traditional algorithms across three industrial simulations In recent years, the w...

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Veröffentlicht in:	Swarm and evolutionary computation Jg. 98; S. 102127
Hauptverfasser:	Huo, Xiang, Nie, Lei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2025
Schlagworte:	Automatic guided vehicle Multi-objective mixed integer programming Non-dominated sorting genetic algorithm Reference point-based non-dominated sorting genetic algorithm and task scheduling Multi-objective mixed integer programming Non-dominated sorting genetic algorithm Automatic guided vehicle Reference point-based non-dominated sorting genetic algorithm and task scheduling
ISSN:	2210-6502
Online-Zugang:	Volltext
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