A Dynamic Heuristic Algorithm for Management of Public Health Emergencies in Unreliable Settings

Prompt and efficient intervention is vital in reducing the impacts of sudden disease outbreaks, natural disasters, or terrorist attacks. This can only be achieved if there is a fit-for-purpose logistics plan in place, incorporating geographical, time, location demand, and vehicular capacity constraints. This response plan will be immediately activated at the onset of such events. Our algorithm involves three stages: Selection of depot and clustering; route construction; and real-time route improvement. In the initial phase, locations to be visited are strategically clustered near a depot. The second phase iteratively constructs routes through all locations clustered in a depot, subject to time, demand, and vehicle capacity constraints. Pruning is heuristically implemented until a feasible set of solutions is obtained. The third phase accepts real-time changes, dynamically re-constructs, and improves routing. Computational application of our algorithm to test instances in a most-at-risk region, clearly indicate its efficiency. It yields an optimal set of routes, incorporating real-time demands and capable of implementing either single or multi-depot scenarios.