A Two-Stage Enhanced Genetic Algorithm for the Multi-Depot Vehicle Routing Problem in Epidemic Logistics Management

Abstract

Effective epidemic logistics management is essential to curbing infectious disease spread, mitigating outbreaks, and saving the lives of several infected people through the distribution of life saving medicines. Here we explore how best to distribute medical supplies during an out- break. During any epidemic outbreak, the problem is to distribute material in the least possible time. This research attempts to optimize medical supply distribution during epidemic outbreaks as a key priority. The problem of distribution of essential medical aid during an epidemic break has been converted into a Multi-Depot Vehicle Routing Problem (MDVRP). Our proposed solution relies on a two-stage enhanced Genetic Algorithm (GA), which incorporates domain specific knowledge with advanced GA techniques to find near optimal solutions efficiently. At first, GA is used on MDVRP with objectives such as minimizing total distance and time while optimizing resource allocation, ultimately producing an initial set of vehicle routes to serve affected people from multiple points across a region. To further improve solution quality, the second stage of an algorithm is introduced; during this phase, each demand node location in the solution group is treated like a subdepot and GA is used to find the optimal route optimizing routing purposes. Compared to more traditional methods, our two-stage enhanced GA is evaluated against several realistic epidemic logistics scenarios and shows significant improvements in solution quality, robustness, and computational efficiency, ultimately saving the lives of those needing immediate healthcare aid during epidemics.