Simulation-Based Optimization using DEA and DOE in Production System

Abstract

Production System (PS) is the process of planning, organizing, directing and controlling the tactical and strategic planning of the different components of the company, to transform inputs into finished products which must be effectively managed. Important parts of PS that always face major challenges in manufacturing systems include production strategy, resource allocation, logistics and production planning. Because of the importance of examining the various components of PS and evaluating its performance on key indicators of organization, it is necessary to evaluate the impact of any changes in these elements to the system prior to its creation. In this regard, the present study aimed to increasing the efficiency and determining the useful methods to evaluate and optimize the performance in different part of PS. To this end, an integrated Discrete-Event Simulation (DES), Design of Experiments (DOE), Data Envelopment Analysis (DEA), and Multi-Attribute Decision Making (MADM) models were implemented to analyze and optimize the real PS process. In the case study of the automobile manufacturing industry in Iran, the accurate analysis was applied to the proposed approach and its different aspects were considered as well. The results indicated that the proposed approach is a practical way for evaluating and optimizing the performance of different part of PS, compared to previous models and helps the manufacturing companies to make efficient decisions regarding increasing productivity while decreasing the essential problems.