Solving multi-scenario hybrid flow shop scheduling problem based on an improved probe machine model

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Dades bibliogràfiques
Publicat a:	PLoS One vol. 20, no. 9 (Sep 2025), p. e0330020
Autor principal:	Tian, Xiang
Altres autors:	Kong, Yang, Liu, Xiyu
Publicat:	Public Library of Science
Matèries:	Scheduling Parallel processing Software Integer programming Algorithms Combinatorial analysis Optimization Approximation Job shops Linear programming Methods Manufacturing Critical path Heuristic Traveling salesman problem Job shop scheduling Economic
Accés en línia:	Citation/Abstract Full Text Full Text - PDF
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Resum:	The hybrid flow-shop scheduling problem is widely present and applied in industries such as production, manufacturing, transportation, and aerospace. In recent years, due to the advantages of nonlinear access and fully parallel processing, the probe machine has shown powerful computing capabilities and promising applications in solving various combinatorial optimization problems. This work firstly proposes an Improved Probe Machine with Multi-Level Probe Operations (IPMMPO) and ingeniously designs general data libraries and probe libraries tailored for multi-scenario HFS problems, including HFS with identical parallel machines and HFS with unrelated parallel machines, no-wait scenario, and standard scenario. Secondly, based on the data libraries of the IPMMPO, two tuple sets suitable for constraint programming modeling are further designed as data preprocessing. Next, a CP model (IPMMPO-CP) applicable to multi-scenario HFS problems is proposed. Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. The results demonstrate that the proposed IPMMPO-CP outperforms the compared algorithms and models.
ISSN:	1932-6203
DOI:	10.1371/journal.pone.0330020
Font:	Health & Medical Collection