Optimizing Generator Selection in Industries Using Shannon Entropy and the TOPSIS Method: A New Approach for Intelligent Decision Making

Abstract

Generator selection is a key operational task in industries, as production processes often stop during power outages, which can have significant negative effects on operations. Therefore, investment in generators is significant. Part of generator selection involves evaluating and ranking different types of generators based on multiple dimensions. The evaluation and selection of generators require consideration of multiple objectives and criteria, which necessitate Multi-Criteria Decision-Making (MCDM) methods and related analyses. In this study, an MCDM method is presented for ranking and selecting generators in the industry. In this example, the selection of a generator based on four criteria (cost, reliability, spare parts, and reparability) is examined. In this example, three generators are evaluated using Shannon entropy weighting and the TOPSIS method. The results of the Shannon entropy method show that reparability has the highest weight and reliability has the lowest weight in the generator selection problem. Additionally, using the TOPSIS technique, we selected a suitable generator for our industry.