An optimization-based risk management framework with risk interdependence for effective disaster risk reduction

Abstract

Effective risk management is crucial in the field of disaster management to mitigate the impact of disasters. It involves proactive planning, resource allocation, and the implementation of response measures. By identifying and assessing risks, stakeholders can take preventive measures and establish contingency plans, fostering resilience, coordination, and efficient disaster response and recovery processes. In the context of globalization, organizations face numerous uncertainties and risks, necessitating the implementation of efficient risk management plans. While the conventional risk response analysis assumes independence among risks, in reality, they are interconnected. Therefore, evaluating risk interactions becomes vital in comprehensive risk management evaluations. This study proposes an innovative framework for disaster risk management based on a review of relevant literature, global project management standards, and the insights gained from previous studies dealing with disaster management projects. To identify appropriate risk response strategies, an optimization model is developed that incorporates a maximization utility function and directly captures potential risk interdependence. An exact optimization method and two metaheuristics are proposed to solve the developed optimization model for the selection of risk response strategies. A detailed assessment of the considered solution algorithms in terms of various performance indicators and sensitivity analysis of model parameters are conducted through a comparative study. This analysis provides valuable insights into the effectiveness and practical applicability of the proposed methodology. The outcomes of this study will benefit practitioners and policymakers involved in disaster management by improving their ability to make informed decisions and allocate resources effectively.

Keywords

• Project risk management
• Disaster risk reduction
• Optimization
• Computational intelligence
• Metaheuristics