An Integrated Fuzzy Multi-Criteria Approach for Partner Selection in Horizontal Cooperation

Horizontal cooperation has emerged as a key strategic tool in modern transport and logistics, enabling firms operating at the same supply chain level to enhance operational efficiency, reduce costs, and advance sustainability goals through shared resources and joint distribution. Despite its proven benefits, effective partner selection remains a complex challenge due to the multiple, often conflicting criteria involved and the lack of comprehensive frameworks that jointly address economic, social, and environmental dimensions. Existing approaches frequently overlook the inherent uncertainty and dynamic nature of transportation networks, creating a clear research gap in providing robust decision-support tools that integrate expert judgment under ambiguity. Addressing this gap, this paper proposes an integrated fuzzy multi-criteria decision-making (MCDM) framework for partner selection in horizontal cooperation. The framework combines the Fuzzy Extent Analysis with the Analytic Hierarchy Process (Fuzzy EW-AHP) to determine the relative importance of criteria and applies the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to rank potential partners. By leveraging fuzzy logic, the model effectively translates subjective expert assessments into quantitative evaluations, overcoming the limitations of traditional crisp approaches. The framework is validated through computational experiments simulating a fourth-party logistics scenario, supported by sensitivity analyses that confirm its stability under varying weight scenarios. The findings demonstrate the framework’s ability to enhance informed, sustainable partner choices, ensuring alignment with strategic goals and sustainability commitments. This study contributes to theory by bridging the gap between fragmented criteria and the need for an integrated, uncertainty-resilient partner selection model. Practically, it offers managers a structured, adaptable decision-support tool suitable for diverse collaborative contexts. Future research should further refine and extend the proposed framework by integrating dynamic, real-time data streams, testing the methodology on larger and more diverse datasets, and developing accessible digital decision-support systems to facilitate its practical implementation. Such advancements would enhance managerial capacity to make robust, transparent, and sustainability-oriented partnership decisions within increasingly complex and dynamic transport and logistics networks.