Evaluating sustainable last-mile delivery modes using IF-MAIRCA: The perspective of local authorities

Significant changes have been observed in the shopping habits of urban populations, particularly following the onset of the COVID-19 pandemic. Shopping by physically going to the store has begun to be replaced by e-commerce shopping, offering advantages such as time savings, a wide variety of options, and opportunities for price comparison. Although online shopping enables modern urbanites to meet their needs comfortably, it increases the volume of commercial traffic in the city, resulting in emissions, parking problems, and air and noise pollution. For last-mile delivery (LMD)—one of the main contributors to commercial traffic in urban centers and defined as delivering orders to end customers—various alternative modes have been introduced in many countries and cities. These alternatives aim to replace conventional home delivery to reduce costs, lower energy consumption and emissions, eliminate failed deliveries, and address challenges such as low vehicle load rates and traffic congestion. Achieving these gains is closely related to the attitude of local authorities and the incentives and infrastructure provided by them. In this study, conventional home delivery and five alternative LMD modes (e-van, e-bike, drone, AGV, droid) are evaluated with a Multi-Criteria Decision-Making (MCDM) approach over a wide set of criteria from the perspective of local authorities. The IF-MAIRCA (Intuitionistic Fuzzy Multi-Attributive Ideal-Real Comparative Analysis) method is used to take into account the uncertainty in LMD processes and the intuition of decision-makers in the problem. The proposed framework is illustrated on a real-life problem in Kayseri, Türkiye. According to the results, the e-van was ranked first, followed by the e-bike, and the droid was ranked last. To test the consistency of the results obtained from IF-MAIRCA, a comparative analysis is conducted. An extended TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), Grey Relational Analysis, and WASPAS (Weighted Aggregated Sum Product Assessment) methods are employed in an intuitionistic fuzzy environment for this purpose. Sensitivity analysis is also carried out to assess the robustness of the results against variations in the criteria weights. Analyses have shown that the performance rankings of alternatives obtained using different MCDM methods and varying criteria weights are generally consistent.