Design and Analysis of Novel Hybrid Multi-Objective Optimization Approach for Data-Driven Sustainable Delivery Systems

This study presents a novel two-stage solution method designed for sustainable last-mile delivery systems in urban areas. A proposed hybrid solution methodology includes multi-criteria decision-making system to select the most efficient logistics providers by considering different performance indicators, and a mixed-integer linear optimization model for last-mile cargo distributions by drones within metropolitan areas. We present a multi-objective modeling approach by considering time windows for customer services and charging operations of drones and outline important characteristics of the mathematical programming problem to minimize transportation cost (in the meantime carbon dioxide emissions) and total sustainability score of the system by using epsilon constraint method to find out the Pareto frontiers. The main novelty of the proposed solution methodology is the inclusion of many performance indicators of last-mile delivery systems into multi-objective models for design of a sustainable city logistics. Additionally, the proposed model is applied to an illustrative case by using real-life data of one of the metropolitan in Turkey. The approach is shown as comparative analysis of proposed method with other two state-of-art solution methodologies for multi-objective problems, after defining some pre-processing, symmetry breaking steps, valid inequalities, and logic cuts.