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Comprehensive MILP Formulation and Solution for Simultaneous Scheduling of Machines and AGVs in a Partitioned Flexible Manufacturing System

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Veröffentlicht in:Machines vol. 13, no. 6 (2025), p. 519-541
1. Verfasser: Zhuang, Cheng
Weitere Verfasser: Qu Jingbo, Wang, Tianyu, Lin, Liyong, Bi Youyi, Li, Mian
Veröffentlicht:
MDPI AG
Schlagworte:
Linear programming
Integer programming
Workpieces
Performance evaluation
Workshops
Initial conditions
Optimization
Greedy algorithms
Process planning
Flexible manufacturing systems
Production planning
Automated guided vehicles
Strategic planning
Heuristic
Energy consumption
Heuristic methods
Efficiency
Mathematical programming
Scheduling
Genetic algorithms
Flexibility
Layouts
Mixed integer
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Abstract:This paper proposes a comprehensive Mixed-Integer Linear Programming (MILP) formulation for the simultaneous scheduling of machines and Automated Guided Vehicles (AGVs) within a partitioned Flexible Manufacturing System (FMS). The main objective is to numerically optimize the simultaneous scheduling of machines and AGVs while considering various workshop layouts and operational constraints. Three different workshop layouts are analyzed, with varying numbers of machines in partitioned workshop areas A and B, to evaluate the performance and effectiveness of the proposed model. The model is tested in multiple scenarios that combine different layouts with varying numbers of workpieces, followed by an extension to consider dynamic initial conditions in a more generalized MILP framework. Results demonstrate that the proposed MILP formulation efficiently generates globally optimal solutions and consistently outperforms a greedy algorithm enhanced by A*-inspired heuristics. Although computationally intensive for large scenarios, the MILP’s optimal results serve as an exact benchmark for evaluating faster heuristic methods. In addition, the study provides practical insight into the integration of AGVs in modern manufacturing systems, paving the way for more flexible and efficient production planning. The findings of this research are expected to contribute to the development of advanced scheduling strategies in automated manufacturing systems.
ISSN:2075-1702
DOI:10.3390/machines13060519
Quelle:Engineering Database