The Multi-phase spatial meta-heuristic algorithm for public health emergency transportation

TL;DR

This paper introduces a multi-phase spatial meta-heuristic algorithm for optimizing emergency transportation of medical countermeasures, improving response planning efficiency during bio-terrorist attacks.

Contribution

It adapts the p-median problem to emergency response planning and presents RE-PLAN, a system for efficient route optimization in public health emergencies.

Findings

Algorithm achieves reasonable computational times.

Enhanced decision-making in emergency planning.

Demonstrated effectiveness through a case study.

Abstract

The delivery of Medical Countermeasures(MCMs) for mass prophylaxis in the case of a bio-terrorist attack is an active research topic that has interested the research community over the past decades. The objective of this study is to design an efficient algorithm for the Receive Reload and Store Problem(RSS) in which we aim to find feasible routes to deliver MCMs to a target population considering time, physical, and human resources, and capacity limitations. For doing this, we adapt the p-median problem to the POD-based emergency response planning procedures and propose an efficient algorithm solution to perform the p-median in reasonable computational time. We present RE-PLAN, the Response PLan Analyzer system that contains some RSS solutions developed at The Center for Computational Epidemiology and Response Analysis (CeCERA) at the University of North Texas. Finally, we analyze a study case where we show how the computational performance of the algorithm can impact the process of decision making and emergency planning in the short and long terms.