Solving flexible flow-shop problem using a hybrid multi criteria Taguchi based computer simulation model and DEA approach

In this paper, an efficient approach for production line policy and planning problem is presented. Here, all of activities are simulated by incorporating learning effects using historical data. After validation process, “what if analysis” is carried out for different scenarios derived from the Taguchi method. Several performance measures estimated for all simulation runs. Then for such homogenized multi-criteria problem, data envelopment analysis (DEA) is used to select the preferred policy. In order to show the applicability of the proposed approach, the data for a series production line is used. Results show that the proposed approach could help managers to identify the preferred strategy considering and investigating various parameters and policies. Finally this study introduces an integrated multi-criteria approach for optimum maintenance policy and planning. In this algorithm, many relevant parameters cover any uncertainty using statistical distribution. We used DEA as a multi-criteria decision making techniques to seek more appropriate assignment. Therefore, using a combination of computer simulation model and an attribute-deductive tool such as DEA, a near optimal solution can be achieved.