Generation of an autonomous approach to fuzzy set-covering models for uncertainty management

Facility location models have been studied in the literature for decades as an outstanding branch of supply chain planning. Set-covering facility location models are among the most commonly used approaches to establishing and running a distribution network. However, real-life brings uncertain and imprecise parameters that need to be reflected in the model systematically and computably to achieve more efficient and precise solutions. That's why fuzzy set covering models have been introduced in the literature from various perspectives. This work aimed to handle real-life uncertainties in an unbiased and autonomous way and provide more precise solutions to fuzzy set-covering facility location models in real-life contexts. Therefore, we propose a novel approach, adopting the autonomous fuzzy methodology consisting of fuzzy trapezoidal set coverage to minimize the cost of establishing new facilities. This work's main innovative achievements are that i) the set-covering facility location models were equipped with autonomous uncertainty management ability, ii) the trapezoidal fuzzy set coverage constituted a perfect fit for the management of uncertainties in a realistic way in the model, and iii) the relevant fuzzification was executed without any human/expert intervention/supervision. The well-known Turkish Network Data demonstrated the proposed model's efficacy. Furthermore, the results show that the developed model contributed to the overall theoretical framework of fuzzy approach employment in optimization models and outperformed classical version in numerical experiments.