AN INTELLIGENT HYBRID MULTI CRITERIA DECISION MAKING TECHNIQUE TO SOLVE A PLANT LAYOUT PROBLEM

Abstract

Multi criteria decision making (MCDM) techniques in today's organizations, as a key to performance measurement comes more to the foreground with the advancement in the high technology. During recent years, many studies have been conducted to obtain a ranking among many alternatives via measuring performance of each of them against many criteria. Managerial decision making problems like supplier selection, weapon selection, project selection, site selection etc are dealt with many multi criteria decision making methods like TOPSIS, AHP-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), ELECTRE, VIKOR etc in crisp throughout the literature. In this work, we first compare several MCDM methodologies to validate the consistency of them on a standard dataset of plant layout problem. We proposed M-TOPSIS, A-TOPSIS procedure to select a suitable layout for the comparative study. Results of M-TOPSIS and A-TOPSIS have been employed to build an unsupervised artificial neural network (ANN) to obtain a new ranking of alternatives. This study proposes an approach of deriving the rank value, in order to get optimal configuration, from the average of more than one set of rank results obtained through the deployment of MCDM methodologies.