Effects of Non-Homogeneous Learning on the Performance of Serial Production Systems A Simulation Study

Abstract

This paper presents a simulation study to investigate the effects of non-homogeneous learning on the performance of serial production systems. Discrete Event Simulation (DES) models were developed and to show how different learning rates of workstations of the system affect the average throughput time of a production run. The results of this simulation study underlined that the learning rate of individual workstations has a significant influence on the overall performance of a non-homogeneous learning system. In addition, if downstream workstations have slower learning than the upstream workstations, it adversely affects the average throughput time more than the converse. Moreover, it was observed that; higher the gap between the learning rates, higher the adverse effects of learning on average throughput time. The contribution of the work presented in this paper is two-fold. First, it presents a DES model which incorporates non-homogeneous learning into a serial production system. Secondly, the results of the simulation experimentation give insights into the effects of non-homogeneous learning on overall system performance. Thus, this study shows the importance of including non-homogeneous learning in performance prediction.