A scenario robust Bi-objective model for integrating disaster mitigation and preparedness

Abstract

Policymakers often overlook the synergies between disaster management's mitigation and preparedness policies. We tap into this potential aided by a scenario robust bi-objective model we develop in this study. These competing objectives include mitigation costs associated with the readiness of a disaster-prone region and preparedness social costs consisting of logistics, deprivation, and fatality costs. We devote our attention to analyzing the tradeoffs with the minimization of these differing costs. Noting that disaster management settings typically present data accuracy challenges, we model uncertain parameters within each scenario using easily estimable deterministic uncertainty sets. We propose a novel framework that utilizes secondary data to estimate willingness to pay for essential goods such as food and water. Using this framework, we develop deprivation functions to account for human suffering due to shortages and delayed arrival of food and water supplies. Policymakers can gain valuable guidance regarding inter-system benefits using our models and insights from their practical deployment. Specifically, benefits accrued from a vertical collaboration between agencies handling mitigation and preparedness policymaking currently operating in silos, engaging in suboptimal policies. We illustrate our model application using an extensive case study featuring a hurricane-prone region. As an integral component of our analytical models, we deploy empirical models to estimate key parameters such as the readiness of the region. Our results provide fresh policy insights into collaborative strategies policymakers can adopt for effective disaster management through a cost-benefit analysis. Particularly, it can aid ethics boards of disaster agencies that provide guidance and oversight on ethical issues related to disaster response and recovery efforts with objective evidence.