Operations Research Techniques for Adaptive Scheduling in Emergency Response Logistics

Abstract

Strikes of natural disasters or a sudden outburst of pandemics poses a threat to entire regions’ sustenance. And response
logistics has the task to ensure deliverance of services on rapidly scheduled timelines. As compared to traditional static
scheduling, adaptive operations research techniques are better suited for an agile response to crises as they are
responsive to given data in real-time. The aim of this work is to construct a modular framework capable of robust
planning with information driven via real-time feedback loops. In order to accommodate conditions such as disruption
of supply chains, alteration in demand, and damage to infrastructure, we utilize a complex constructed with branching
tree structures, dynamic systems along with setting quota systems (which is known as Stochastic Programming).
Through these techniques we can make simulations and update schedules to utilize data in real-time spurring elevated
responsiveness. Evaluating the model with historical disaster response data showcases improved resource usage and
servicing rates captured through a graph, bolstering shifting from rigid systems that offer borders of unyielding
schedules to open and flexible frameworks able to embrace change. This showcases the evolving area of responsive
and flexible logistics open to give real-time solutions to government institutions and worldwide urgent assistance and
non-profit organizations. Furthermore, adding information from real-time sources increases the quality of responding
to emergencies.