A hybrid teaching and learning-based optimization algorithm for distributed sand casting job-shop scheduling problem

• Published in: Applied soft computing Vol. 120; p. 108694
• Main Authors: Tang, Hongtao, Fang, Bo, Liu, Rong, Li, Yibing, Guo, Shunsheng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2022
• Subjects: Different process routings sand casting factory; Distributed flexible job-shop scheduling problem; Elastically start processing time constraint interval; Hybrid teaching–learning-based optimization algorithm; Three-layer encoding solution; Elastically start processing time constraint interval; Different process routings sand casting factory; Three-layer encoding solution; Hybrid teaching–learning-based optimization algorithm; Distributed flexible job-shop scheduling problem
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2022.108694

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. •A distributed sand casting job-shop scheduling problem optimization model is established with the consideration of sand casting characteristics.•Hybrid teaching–learning-based​ optimization (HTLBO) algorithm is proposed to solve distributed sand casting job-shop scheduling problem.•A three-layer coding solution and five neighborhood structures under local search strategies are designed in proposed algorithm.•Proving the performance of HTLBO in a real sand casting enterprise case.