A Hybrid Metaheuristic Algorithm with Novel Decoding Methods for Flexible Flow Shop Scheduling Considering Human Fatigue

Human are the key production resources of enterprises, and factors such as human skills and fatigue will affect the implementation of the scheduling strategy. Aiming at the dual resource constrained flexible flow shop scheduling problem (DRC-FFSP), this paper proposes a mixed integer programming (MI...

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Vydáno v:Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics s. 2328 - 2333
Hlavní autoři: Du, Hangming, Qiao, Fei, Wang, Junkai, Lu, Hong
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 17.10.2021
Témata:
Adaptation models
Fatigue
Integer programming
Job shop scheduling
Manufacturing processes
Metaheuristics
Production
ISSN:2577-1655
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Shrnutí:Human are the key production resources of enterprises, and factors such as human skills and fatigue will affect the implementation of the scheduling strategy. Aiming at the dual resource constrained flexible flow shop scheduling problem (DRC-FFSP), this paper proposes a mixed integer programming (MIP) model for the flexible flow shop to minimize makespan, with the constraints of machines and heterogeneous human who have different skills and characteristics. According to the characteristics of the model, two paradigms of a hybrid metaheuristic algorithm (HMA) are proposed, which combine genetic algorithm with two novel heuristic decoding methods, respectively. A new methodology which aims to provide an adaptable assignment heuristic algorithm is designed to allocate human during manufacturing process to meet the fatigue constraint. A case study from benchmarks demonstrates the effectiveness of the proposed model and algorithms. Furthermore, different production scales are designed to verify the superiority and stability of the two paradigms. The experimental results show that the scheduling strategy based on the hybrid metaheuristic algorithm can meet the human fatigue constraint while ensuring economic benefits.
ISSN:2577-1655
DOI:10.1109/SMC52423.2021.9658692