Harnessing the Power of Metaheuristic Algorithms for Optimal Logistics Management in Epidemic Response

Abstract

This paper investigates the use of metaheuristics as computational tools for managing epidemic logistics. Epidemics pose severe risks to world health, necessitating coordinated, effective, and prompt response plans. Effective logistics management is a critical component of this, requiring, among other things, timely distribution of vaccinations and the efficient deployment of workers and resources. Such logistical difficulties are frequently dynamic and complex, demanding more sophisticated computational techniques. The complex logistic optimization problems are addressed by metaheuristics, which offer higher-level problem-solving techniques. The Multi-Depot Vehicle Routing Problem (MD- VRP), a common metaheuristic, and its solution in the context of epidemic logistics are the specific topic of this paper. The objective of MDVRP, which is categorized as an NP-Hard issue, is to efficiently distribute supplies from many depots to numerous demand nodes (hospitals, clinics). Due to this problem’s complexity, time-sensitivity, scalability concerns, and dynamic and uncertain situations, traditional methods frequently fail to solve it effectively. However, the genetic algorithm can potentially improve the MDVRP inside epidemic logis- tics, delivering effective and adaptable solutions in a fair amount of time. This work advances knowledge of the function of metaheuristics in improving epidemic response logistics through a thorough literature analysis, potential applications discussion, and case study illustration. We acknowledge the necessity for additional study in customizing these algorithms considering the many uncertainties and dynamic aspects in the real-world application as we come to a close.