A parallel cooperative co-evolutionary algorithm for flexible job-shop scheduling with Workers’ heterogeneity - Li, Zhong-Kai; Li, Nan; Pan, Quan-Ke; Gao, Liang; Li, Wei-Min (Expert systems with applications, 2025)
The flexible job-shop scheduling is widely adopted in aviation structural parts manufacturing enterprises. However, the scarcity of workers with advanced processing skills has become a bottleneck for precision manufacturing workshops. Notably, there is a lack of research addressing the Flexible Job-shop Scheduling Problem with Heterogeneous Workers (FJSPWH). To fill this gap, this paper proposes a Parallel Cooperative Co-evolutionary Algorithm (PCCEA) to solve the FJSPWH. We examine the problem and develop two mixed-integer linear programming models, using the processing sequence method and the processing location method, respectively. To address the complexity of the FJSPWH, we introduce a two-dimensional solution representation, where the production sequence and the allocation of machines and workers are searched independently. A high-quality solution set library is constructed to generate solutions by efficiently matching production sequences with allocation schemes. Additionally, a scoring mechanism is employed to select the most suitable workers and machines for task redistribution on critical machines. Numerical experiments demonstrate the effectiveness of the PCCEA in solving the FJSPWH, and the results confirm that the proposed algorithm outperforms existing methods. Finally, a real-world production case study was conducted to validate the effectiveness of the proposed algorithm. The results demonstrate that the algorithm is well-suited for practical application, confirming its potential for enhancing actual manufacturing processes.