Methods for Scheduling Problems Considering Experience, Learning, and Forgetting Effects

Abstract

Workers with different levels of experience and knowledge have different effects on job processing times. By taking into account three factors: the sum-of-processing-time, the job-position, and the experience of workers, a more general learning model is introduced for scheduling problems. We show that this model generalizes existing ones and brings the consideration of learning and forgetting effects closer to reality. We demonstrate that some single machine scheduling problems are polynomially solvable under this general model. Considering the forgetting effect caused by the idle time on the second machine, we construct a learning-forgetting model for the two-machine permutation flow shop scheduling problem with makespan minimization. A branch and bound method and four heuristics are presented to find optimal and approximate solutions respectively. The proposed heuristics are evaluated over a large number of randomly generated instances. Experimental results show that the proposed heuristics are effective and efficient.

Group Scheduling With Nonperiodical Maintenance and Deteriorating Effects

“…Basically, there are two types of learning and/or deteriorating effects [12]: position-based and time-based. The position-based ones [13] [14] depend on the number of processed jobs while the time-based ones take into account the processing time of all processed jobs so far. In addition, most single-machine group scheduling problems with learning and/or deteriorating effects assume a fixed number of groups and also a fixed number of jobs in each group, i.e., periodical maintenance.…”