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A lot streaming scheduling algorithm with variable sub-lots for solving a multi-objective flexible job shop scheduling problem
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AI
DOI：10.1016/j.asoc.2025.113184.png)
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Abstract
En 中文
To adapt to the current market change of customized production, a long-distance guided gray wolf optimization algorithm is proposed to solve the lot streaming scheduling problem. Aiming to simulate the real production situation, the problem takes into account the switching time of the jobs as well as the limitation of the number of work equipment. A multi-objective optimization model is also proposed to minimize the maximum completion time and machine idle rate. Secondly, an operation-level encoding and the corresponding adaptive segmented decoding method are designed to improve the search efficiency of the algorithm and effectively reduce the solution space. Moreover, an innovative two-segment hierarchical initialization strategy is designed to ensure the high quality of the initial population, which initializes the workpiece order and the number of machines, respectively. In addition, an improved social hierarchy is proposed to properly guide the wolf pack search direction to avoid falling into local optimization. Finally, the effectiveness of the proposed method is verified with examples of different scales, and the superiority of this method is proved by comparing it with existing methods.
Keywords:
Flexible job shop scheduling with lot streaming
Variable sub-lots
Multi-objective optimization
Gray wolf optimizer