WORKER GROUPING AND ASSIGNMENT FOR SERIAL AND PARALLEL MANUFACTURING SYSTEMS CONSIDERING WORKERS' HETEROGENEITY AND TASK COMPLEXITY

The present study addresses the grouping-assignment problem of heterogeneous workers for serial and parallel manufacturing configurations considering the worker production rate as a function of learning by doing and knowledge transfer. A simulated experiment is presented for this end, considering the maximization of the system output as the optimization goal, and the system size and tasks heterogeneity as experimental factors. Three heuristic policies are compared based on the heterogeneity of the groups with respect to the individual knowledge transfer parameter. Research related to team formation for manufacturing systems is scarce and often does not considering workers' heterogeneity nor the knowledge transfer. The results highlight the importance of considering workers' heterogeneity for the grouping and assignment of workers. The implications of this study impact the managerial decision making process related to the grouping and allocation of workers to tasks as part of the production planning.