Multi-criteria decision-making methods in last-mile delivery

Abstract

This dissertation examines Last-Mile Delivery (LMD) issue through a Multi-Criteria Decision-Making (MCDM) lens and develops a new method to support more reliable and comprehensive LMD assessment. The research synthesizes foundations in city logistics, delivery systems, mathematics, MCDM theory, and operational research, and evaluates the limitations of existing approaches. The dissertation directly addresses a problem belonging to the pick-up and delivery domain, providing a structured decision-support solution for evaluating operational performance. Established MCDM techniques (TOPSIS, ARAS, WASPAS, and MARCOS) are applied as benchmarks, providing insights that guide the development of a novel MCDM framework featuring an adaptive normalization procedure and an original final-ranking formulation.

The proposed method is validated in a real-world LMD setting in Novi Sad, Serbia, where twelve delivery areas are evaluated using criteria related to parcel flows, failed deliveries, distance traveled, and the number of pick-up and delivery stops. The findings demonstrate strong robustness, practical usefulness, and managerial relevance.