Intuitionistic Fuzzy Topsis Approach for Maintenance Strategy Selection in Production

The selection of a maintenance strategy is crucial for ensuring efficiency, safety, and cost optimization in the automotive industry. However, the decision-making process can be complex due to various conflicting criteria and the uncertainties inherent in expert judgments. This study presents a structured decision-making framework that utilizes the Intuitionistic Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IF-TOPSIS) to address these challenges. This method allows for a thorough evaluation of four maintenance alternatives—Reactive, Primitive, Preventive, and Predictive Maintenance—based on six key criteria: safety, cost, operational factors, reliability, risk, and added value. Expert assessments were modeled using intuitionistic fuzzy sets to capture the degrees of membership, non-membership, and hesitation. The IF-TOPSIS algorithm was employed to evaluate the relative closeness of each alternative to the ideal solution. The findings confirmed the most effective maintenance strategy by demonstrating its ability to balance performance, risk, and cost. Additionally, a sensitivity analysis verified the model’s stability under various constraint conditions. The IF-TOPSIS framework has been confirmed as a reliable and comprehensive decision support tool for maintenance planning and strategy formulation in industrial settings characterized by uncertainty and subjectivity. This research is expected to enhance the literature and the industry by demonstrating the practical effectiveness of intuitive fuzzy multi-criteria decision-making (MCDM) approaches in selecting maintenance strategies.