Integrating customer preferences into operational decision-making for prioritizing emerging technologies in last-mile delivery

Abstract

The rapid expansion of e-commerce has positioned last-mile delivery as the most critical and resource-intensive stage of modern supply chains. Firms must balance multiple and often conflicting objectives: reducing costs, minimizing environmental impacts, and meeting growing customer expectations for speed, reliability, and personalization. While previous research has focused on operational efficiency and routing optimization, limited attention has been given to frameworks that integrate customer preferences with technology-enabled decisionmaking. This study develops a hybrid decision support model by extending the classical Simple Weight Calculation (SIWEC) method with grey numbers (G-SIWEC), capable of handling uncertainty in subjective judgments to generate robust criterion weights. These weights are incorporated into a multi-objective optimization model, solved using the Weighted Sum Scalarization Method (WSSM), to minimize delivery costs and emissions while maximizing service quality. The model explicitly considers emerging delivery technologies, including drone, Autonomous Vehicle (AV), and bicycle (e-bike), to explore innovative, sustainable, and customer-centric delivery strategies. Findings highlight technology-specific patterns: drone and bicycle excel in lightweight, eco-friendly deliveries, AV dominates mid-range logistics, and conventional truck remains indispensable for heavy loads. By linking customer preferences to technology-driven operational decisions, this work provides practical insights for managers and policymakers seeking to design efficient, sustainable, and innovative last-mile delivery systems for e-commerce. These implications should be interpreted within the context of the numerical experiment conducted in this study.