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A hybrid teaching and learning-based optimization algorithm for distributed sand casting job-shop scheduling problem
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DOI：10.1016/j.asoc.2022.108694.png)
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Abstract
En 中文
Because of global manufacturing, the foundry production workshop has shifted from single-factory production to multi-factory production. The distributed flexible job-shop scheduling problem is studied in this paper, and a distributed sand casting job-shop scheduling problem optimization model is established. To solve this model, this paper proposes a hybrid teaching-learning-based optimization (HTLBO) algorithm that involves a three-layer coding solution and a variety of strategies for population initialization. The HTLBO consists of the teacher learning phase, teaching phase, and learning phase. To improve the quality of teachers in the proposed algorithm, this paper sets the dynamic teacher group and adopts the tabu search based on the critical path and key blocks to increase the number of teachers in the dynamic teacher group and conduct the process of the teacher learning phase. In the teaching and learning phase, a variety of crossover operators for teaching and learning operations is designed to realize the process of teaching and learning. Finally, the experimental results of a real sand casting enterprise case indicate that the proposed algorithm performs better than the other six algorithms. (C) 2022 Elsevier B.V. All rights reserved.
Keywords:
Hybrid teaching-learning-based optimization algorithm
Different process routings sand casting factory
Distributed flexible job-shop scheduling problem
Three-layer encoding solution
Elastically start processing time constraint interval
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6.6
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