A new last aggregation fuzzy compromise solution approach for evaluating sustainable third-party reverse logistics providers with an application to food industry

In today’s world, reverse logistics (RLs) activities have become increasingly crucial for companies looking for improved customer service, cost reduction, and sustainability perspectives. Limited resources, and insufficient technology and knowledge, have led manufacturing firms to cooperate with professional RL providers. However, evaluating the right third-party reverse logistics providers (3PRLPs) is a complex, uncertain multi-criteria decision-making (MCDM) problem, which is affected by many conflicting qualifications, the complexity of the human mind, and imprecise and uncertain information. This paper aims to introduce a novel framework that integrates fuzzy best worst method (BWM) and a new last aggregation fuzzy compromise solution for providing systematic decision support for organizations to select the most preferred partner. The reasons behind choosing both methods in an integrated way are that; the fuzzy BWM requires a smaller number of comparisons but can provide more reliable weights due to consistent criteria comparisons. Furthermore, the approaches based on compromise solutions are powerful decision-making tools because a compromise solution is a more feasible solution closest to the ideal that can be efficient in selecting the best alternative in the presence of conflicting decision criteria. Therefore, the fuzzy BWM approach is utilized to investigate the performance criteria of the 3PRLPs from economic, environmental, and social aspects of sustainability as well as risk factor. Expert weights and the preferences of sustainable 3PRLPs are then calculated simultaneously using a new last aggregation fuzzy compromise solution. Compared with the available literature, the proposed framework considers all possible sustainable criteria along with risk factor, which offers greater flexibility for experts to articulate their evaluations. In addition, the last aggregation fuzzy method eliminates distortion and loss of information and allows decision-makers to control the outcome’s precision. Moreover, the applicability of the proposed method is numerically demonstrated by developing a decision-support tool for the food industry. Sensitivity analysis and comparative analysis further illustrate the flexibility and practicability of proposed framework through which the decision-makers can make more accurate judgments regarding the evaluation of 3PRLPs.