A New Mathematical Model and Metaheuristic Algorithm for Scheduling Optimization in Flexible Manufacturing Systems

Although scheduling in traditional manufacturing environments, such as flow and job shops, has received considerable attention, there remains a notable lack of research on scheduling in advanced flexible manufacturing systems. This study investigates the scheduling processes in flexible manufacturing systems that feature both machine and routing flexibility. To address this challenge, we introduce two mixed-integer linear programming models. The first model emphasizes the positioning of tasks, while the second concentrates on the sequencing of operations. These models are adept at optimally resolving smaller-scale scheduling problems. Recognizing the NP-hard complexity of the scheduling challenge, we subsequently develop a robust genetic algorithm tailored for larger-scale applications, capitalizing on the characteristics of the optimal schedule. Our computational experiments demonstrate the efficacy of the proposed algorithm, revealing its ability to produce high-quality solutions within a reasonable computational timeframe. This research is dedicated to enhancing scheduling optimization in flexible manufacturing systems and offers a novel perspective on the design of scheduling algorithms.

کلیدواژه ها:

scheduling ، flexible manufacturing systems ، mathematical model ، genetic algorithm