Dynamic Computational Approach for Routing and Scheduling Last-Mile Distribution

Abstract

This paper proposes an efficient dynamic mathematical programming approach to route and schedule downstream last-mile distributions of goods and assign a set of delivery points to delivery persons for minimizing the number of delivery persons required over multiple time periods while satisfying predetermined time restrictions. This approach aims to develop a decomposition MIP-based procedure that dynamically changes the members of the delivery point sets. This procedure allows for easily solvable optimization models, reducing computational efforts and minimizing memory usage and execution times. To demonstrate the effectiveness of the proposed approaches, the paper presents two datasets of 25 last-mile case-problems. The key findings include significant computational improvements in terms of reduced numbers of constraints and variables, which result in reduced computational effort, time, and memory usage required for optimizing the routes, schedules, and assignments of last-mile distributions