Worker assignment with learning-forgetting effect in cellular manufacturing system using adaptive memetic differential search algorithm

Due to rising labor costs, cross-trained worker assignment has become increasingly critical for constructing an efficient and flexible cellular manufacturing systems. Related studies concentrated on assigning skilled workers with different skill levels to tasks according to capacity or cost benefits. However, these studies have yet examined how workers' learning and forgetting affect total cost in the context of cross-training conducted in multiple cells. This study presents a new model of cross-training with learning and forgetting effects aiming at addressing the problem of worker assignment spanning multiple cells. Considering the computational complexity of this model, an adaptive memetic differential search algorithm is proposed. In the proposed algorithm, a subgradient method is employed to enhance the capability for local exploitation, and a dynamic Cauchy mutation-based method is developed to enhance the model's global exploration capability. Furthermore, an intelligent selection method based on previous effectiveness is implemented to balance exploration and exploitation and to ensure adaptability. Experimental results indicate the efficiency and effectiveness of the proposed models and of the developed algorithms.