The purpose of the current work is to present an intuitionistic fuzzy (IF) concept-based structured framework for selecting the optimal maintenance policy in a sugar mill.
The proposed framework utilizes IF concept based multi-criteria decision making (MCDM) approaches, specifically IF-analytic hierarchy process (IF-AHP) and IF-Technique for Order of Preference by Similarity to Ideal Solution (IF-TOPSIS). IF theory based MCDM approaches utilises the hesitation present in the maintenance experts to encounter the uncertainties/vagueness to much higher degree of accuracy in decision-making. The suggested framework is used to evaluate and select the optimal maintenance policy based on six different criteria namely safety factor, cost factor, maintenance factor, reliability, risk and added values.
The IF-AHP approach has been employed to calculate the weights of the criteria and sub-criteria, while the IF-TOPSIS approach was utilized to rank the maintenance strategies. Based on the results, with a relative coefficient value of 0.7204, corrective maintenance (CM) is determined to be the best maintenance policy. For certifying the consistency of the recommended structured framework, sensitivity analysis (SA) has been also conducted.
The ranking results obtained from the analysis are provided to the maintenance management of the considered sugar mill for its further implementation and validation. The findings of this work are also applicable to all other sugar mill industries which are installed globally.
The analysis results has been supplied to the maintenance manager of considered sugar mill industry. The implementation policy of the results will be shared with the higher management and hence once implemented the results could be tested and verified.
The developed framework so implemented to the considered sugar mill industry is original in nature. Also, consideration of hesitation effect in the collected raw data under the developed framework provide more authenticated decision results which proves to be useful in achieving higher availability and profitability of the industry.
