Analyzing the Impact of Uncertain Parameters in Reverse Supply Chain Network Using Fuzzy Alpha-Cut Approach

Optimizing reverse supply chains (RSC) design is crucial for sustainability, impacting the collection and processing of used products. This study introduces a fuzzy mixed-integer linear programming (FMILP) model employing the fuzzy alpha-cut method to determine optimal locations for collection points under uncertainty. The model considers uncertainty in the quantity of used products at customer locations and the selling prices at collection points and retailers. Six scenarios were analyzed, covering individual and combined uncertainties. Each scenario utilized triangular fuzzy numbers with alpha-cuts to assess robustness at medium (a=0.5) and high (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a=1$</tex>) confidence levels, providing upper and lower bounds of uncertainty. Results indicate that combined uncertainties significantly affect RSC performance compared to individual uncertainties. A comparison with exact solutions highlights the influence of uncertainty parameters on decision-making in reverse supply chains.