A New Method for Resource Allocation Optimization in Disaster Reduction and Risk Governance

Abstract

How to allocate and use resources play a crucial role in disaster reduction and risk governance (DRRG). The challenge comes largely from two aspects: the resources available for allocation are usually limited in quantity; and the multiple stakeholders involved in DRRG often have conflicting interests in the allocation of these limited resources. Therefore resource allocation in DRRG can be formulated as a constrained multiobjective optimization problem (MOOP). The Pareto front is a key concept in resolving a MOOP, and it is associated with the complete set of optimal solutions. However, most existing methods for solving a MOOPs only calculate a part or an approximation of the Pareto front, and thus can hardly provide the most effective or accurate support to decision-makers in DRRG. This article introduces a new method whose goal is to find the complete Pareto front that resolves the resource allocation optimization problem in DRRG. The theoretical conditions needed to guarantee finding a complete Pareto front are given and a practicable, ripple-spreading algorithm is developed to calculate the complete Pareto front. A resource allocation problem of risk governance in agriculture is then used as a case study to test the applicability and reliability of the proposed method. The results demonstrate the advantages of the proposed method in terms of both solution quality and computational efficiency when compared with traditional methods.

Keywords

Disaster reduction / Multiobjective optimization / Pareto front / Resource allocation / Risk governance / Ripple-spreading algorithm

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