A flow shop batch scheduling and operator assignment model with time-changing effects of learning and forgetting to minimize total actual flow time

Abstract

Purpose: This paper aims to investigate simultaneous problems of batch scheduling and operator assignment with time-changing effects caused by learning and forgetting. Design/methodology/approach: A mathematical model was developed for the problems, and the decision variables of the model were operator assignment, the number of batches, batch sizes and the schedule of the resulting batches. A proposed algorithm worked by trying different number of batches, starting from one, and increasing the number of batches one by one until the objective function value did not improve anymore. Findings: We mathematically and numerically show that the closest batch to the due date always became the largest batch in the schedule, and the faster operators learn, the larger the difference between the closest batch to the due date and the other batches, the lower optimal number of batches, and the lower the total actual flow time. Originality/value: Previous papers have considered the existence of alternative operators but have not considered learning and forgetting, or have considered learning and forgetting but only in a single-stage system and without considering alternative operators.