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Intelligent Scheduling of a Pulsating Assembly Flow Shop Considering a Multifunctional Automated Guided Vehicle

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Gepubliceerd in:Applied Sciences vol. 15, no. 5 (2025), p. 2593
Hoofdauteur: Song, Hailong
Andere auteurs: Yang, Shengluo, Yin, Shuoxin, Wang, Junyi, Xu, Zhigang
Gepubliceerd in:
MDPI AG
Onderwerpen:
Scheduling
Design
Job shops
Linear programming
Production planning
Deep learning
Assembly lines
Algorithms
Automation
Manufacturing
Mathematical models
Optimization
Efficiency
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Samenvatting:The pulsating assembly line is widely used in modern manufacturing, particularly in high-precision industries such as aerospace, where it greatly enhances production efficiency. To achieve overall optimization, both product scheduling and Automated Guided Vehicle (AGV) scheduling must be simultaneously optimized. However, existing research predominantly focuses on product scheduling, with limited attention given to AGV scheduling. This paper proposes an optimized solution for the pulsating assembly line scheduling problem, incorporating multifunctional AGV scheduling. A mathematical model is developed and three AGV selection strategies and three AGV standby strategies are designed to optimize AGV scheduling and control. To improve scheduling efficiency, nine heuristic strategies are introduced, along with the Variable Neighborhood Descent (VND) algorithm as a metaheuristic method for product scheduling. The VND algorithm refines the solution through multiple neighborhood searches, enhancing both the precision and efficiency of product scheduling. Our experimental results demonstrate that the proposed strategies significantly improve the production efficiency of pulsating assembly workshops, reduce AGV scheduling costs, and optimize overall production workflows. This study offers novel methods for intelligent scheduling in pulsating assembly workshops, contributing to the advancement of manufacturing toward “multiple varieties, small batches, and customization”.
ISSN:2076-3417
DOI:10.3390/app15052593
Bron:Publicly Available Content Database