A fuzzy approach on simulating and optimizing the performance of a discrete-event production line

Abstract

Applications of fuzzy set theory are spreading widely into different research areas. The main differences between fuzzy and non-fuzzy simulation approaches are related to the most important attribute of fuzzy approaches and that is modeling uncertain key system parameters (in this study, specifically by fuzzy triangular numbers) with vague data, and therefore obtaining more realistic systematic outputs, whilst utilizing overused non-fuzzy methodologies with the assumption on certainty of gathered data results in enormous computational errors in some cases. In this study, after a short introduction on related efforts in this area, we examine a fuzzy approach for simulating a production line system, which includes three phases: production and manufacturing system analysis, constraint and cost analysis and fuzzy modeling, implementation and simulating with GPSS/H. Since a general view on fuzzy set theory was required, such general concepts in simulating our fuzzy production line are being deliberated after the system analysis phase in an additional section and some references are included for the topics related to our approach. Moreover, by using this fuzzy methodology, optimum solutions in the stable production lines (especially in managerial situations) can be reached as well.