Humanitarian and e-commerce supply chain group decision-making using spherical fuzzy rough multi-attribute border approximation area comparison method

Humanitarian supply chain management plays a crucial role in effectively delivering aid and allocating resources during crises. It involves coordinated logistics, inventory control, and collaboration among stakeholders to ensure timely and ethical support. The integration of artificial intelligence with human judgment enhances logistics efficiency, resource allocation, and adaptability. Key enablers such as advanced technology, robust infrastructure, and efficient communication systems support seamless operations across the supply chain. Structured around the phases of preparedness, response, and recovery, these processes guide effective resource mobilization. This study introduces a multi-criteria group decision-making outranking method to determine the most suitable phase for strengthening enablers in the humanitarian supply chain. The approach involves normalizing the decision matrix for comparability, computing the border approximation area by assessing distances from ideal and anti-ideal solutions, and ranking alternatives based on their relative closeness to the ideal. The methodology is validated through two case studies, selecting the optimal phase for improving supply chain enablers and opting the best logistic strategy in supply chain for e-commerce retailer. The phase F₁ and logistic strategy J₆ are chosen as the best options in the considered case studies as they have highest border approximation area relative to their scenarios. To validate the credibility of the proposed technique, the suggested method is compared with existing methods and the selection of same optimal choice assures the integrity of the proposed method. At the last, limitations and future directions are being discussed to address the pros and cons of the proposed method.