Optimizing cell load regulation capability in dynamic cell manufacturing systems

Variation in production cell load arises from machine loads exceeding their capacity and the constraints of cellular capacity. This issue has become increasingly critical in scheduling cellular manufacturing systems. In this paper, we propose a novel approach for scheduling in dynamic cellular manufacturing systems. The objective is to minimize cell load variations and associated costs while achieving a balance between internal manufacturing and subcontracting. To address this, we developed a mixed-integer linear programming (MILP) mathematical model, which was solved using LINGO 19.0 software. The model focuses on reducing cell load variation, minimizing associated costs, and optimizing the balance between internal production and subcontracting. Extensive computational experiments use medium-scale problem instances with randomly generated demand scenarios. The results demonstrate the effectiveness of the proposed model in generating optimal solutions, significantly reducing cell load variation and related costs. Furthermore, computational efficiency is notable, with solutions obtained in very low processing times. This underscores the model's practical applicability and robustness in addressing real-world scheduling challenges in cellular manufacturing systems.