Dual-Resource Optimization Configuration and Collaborative Scheduling for Flexible Job Shop Under Worker Flexibility Constraints

Worker resources exhibit high flexibility, adaptability, and individual differences. It is critical to complete manufacturing tasks with a suitable worker resource allocation plan, given the increasing labor costs and the dynamic changes in production demands. It is often assumed in studies on traditional flexible job shop scheduling problems that the attributes of manufacturing resources, such as the number of workers and their skill levels, are known and fixed. This paper focuses on achieving resource allocation via personnel flexibility quantification and dual-resource collaborative scheduling optimization. A method based on the worker ability matrix, proficiency, and skill level coefficients is introduced to quantify and evaluate worker flexibility and individual differences. On this basis, an integrated optimization model for worker allocation and production scheduling is established considering the maximum completion time, total manufacturing cost, and worker load balancing. An algorithm based on a two-level decision model is proposed to solve this problem. The upper-level model for worker skill optimization configuration is based on a rule-based greedy algorithm, and the lower-level collaborative model for production scheduling is based on a hybrid genetic-simulated annealing algorithm. In the proposed algorithm, population initialization and neighborhood search strategies based on mixed multi-rules and critical paths are designed. A feasible production guidance scheme for the job shop is obtained by iteratively solving the two-level decision model, which provides the foundation for subsequent production. Test case results validate that the proposed algorithm can determine multi-skilled worker configuration strategies and achieve excellent scheduling solutions, completing the production tasks quicker, with lower costs and a more balanced task distribution.