An Integrated Multi-Criteria Computer Simulation-AHP-TOPSIS Approach for Optimum Maintenance Planning by Incorporating Operator Error and Learning Effects

Abstract

The objective of this study is to optimize maintenance activities in a production system by incorporating cost and reliability. An integrated multi-criteria approach is presented to optimize maintenance planning and also define its policies. Modelling maintenance activities is usually nonlinear and complex since it includes various parameters. Therefore, a simulation optimization approach is presented to handle such complexities. Production and maintenance functions are estimated by means of historical data. In this study, maintenance activities with different scenarios along with probability of human error and learning effects are discussed. The scenarios are generated from a combination of inputs such as time between preventive maintenance, number of operators and skills. Different outputs such as reliability, machine availability, human errors and cost are obtained from these scenarios. The outputs are analysed to reach the optimized scenario by using an integrated analytical hierarchy process (AHP) and techniques for order preference by similarity to ideal solution (TOPSIS) method. The applicability of this approach is shown in an actual production line with four series machines. The results of this study can be helpful for decision makers to choose the best policy of maintenance planning. This is one of the first studies that optimize reliability and human cost by considering human error and learning effects through an integrated simulation, AHP and TOPSIS approach. Keywords

A hybrid decision support model using axiomatic fuzzy set theory in AHP and TOPSIS for multicriteria route selection

“…The positive-ideal solution is a solution that maximizes the benefit criteria and minimizes the cost criteria, whereas the negative-ideal solution maximizes the cost criteria and minimizes the benefit criteria [45]. In literature, there have been a large number of studies using TOPSIS for the solution of complex decision-making problems ( [2][3][4]24,25,41,48,49]). For this present study, the procedure of TOPSIS can be expressed in following steps:…”