The Multi-phase spatial meta-heuristic algorithm for public health emergency transportation
This work addresses emergency transportation planning for public health crises, offering an incremental improvement in algorithm efficiency for domain-specific applications.
The study tackled the Receive Reload and Store Problem for delivering Medical Countermeasures during bio-terrorist attacks by adapting the p-median problem to emergency response planning, proposing an efficient algorithm to find feasible routes under constraints like time and capacity, and demonstrating its impact on decision-making through a case study.
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.