The rapid development of e-commerce has made last-mile delivery a critical bottleneck in logistics management, with its efficiency directly impacting operational costs, service quality, and environmental sustainability. To address the multi-criteria decision-making (MCDM) problem of parcel locker location selection, this study constructs an intelligent decision-support framework that integrates the Improved Fuzzy Step-wise Weight Assessment Ratio Analysis (IMF SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) methods. Based on real-world data from the Brčko Distribution Center of a regional logistics company (X Express), the research first employs the IMF SWARA method to determine fuzzy weights for six key criteria, including availability, frequency of user requests, and accessibility. The WASPAS method is then applied to comprehensively rank twelve candidate locations. Results indicate that location A2 is the optimal choice, followed by A4 and A3. The robustness of the model is verified through sensitivity analysis, including comparisons with other MCDM methods such as ARAS, EDAS, and MARCOS, as well as systematic variation tests of the $\lambda$ parameter in WASPAS. This framework provides logistics managers with a structured and quantifiable decision-making tool, facilitating data-driven optimization of last-mile delivery networks in complex urban environments and enhancing the sustainability and operational efficiency of logistics systems.
Open Access
News 
Journal Metrics
Journal Overview
Associated Journals