A Hybrid Metaheuristic Algorithm with Novel Decoding Methods for Flexible Flow Shop Scheduling Considering Human Fatigue - Du, Hangming; Qiao, Fei; Wang, Junkai; Lu, Hong (Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics, 2021)
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.