We present a collaborative prepositioning strategy to strengthen the disaster preparedness of the Caribbean countries, which are frequently hit by hurricanes. Since different subsets of countries are affected in each hurricane season, significant risk pooling benefits can be achieved through horizontal collaboration, which involves joint ownership of prepositioned stocks. We worked with the intergovernmental Caribbean Disaster and Emergency Management Agency to design a collaborative prepositioning network in order to improve regional response capacity. We propose a novel insurance‐based method to allocate the costs incurred to establish and operate the proposed collaborative prepositioning network among the partner countries. We present a stochastic programming model, which determines the locations and amounts of relief supplies to store, as well as the investment to be made by each country such that their premium is related to the cost associated with the expected value and the standard deviation of their demand. We develop a realistic data set for the network by processing real‐world data. We conduct extensive numerical analyses and present insights that support practical implementation. We show that a significant reduction in total inventory can be achieved by applying collaborative prepositioning as opposed to a decentralized policy. Our results also demonstrate that reducing the replenishment lead time during the hurricane season and improving sea connectivity are essential to increasing the benefits resulting from the network.
In community health programs implemented in underserved areas, community healthcare workers (CHWs) prevent, diagnose, and treat the most common diseases. To ensure continuous in‐service training of CHWs, some countries have mentored highly skilled CHWs to become supervisors. Designing a network in such a context implies determining the number of CHWs and supervisors, as well as the routing of the supervisors. This can be defined as a location‐routing covering problem (LRCP), a variant of the location‐routing and the covering tour problems. To solve the LRCP, we propose set‐partitioning formulations and a procedure to generate only non‐dominated variables without losing optimality, which also allows to break the symmetry between variables. Finding the most appropriate mathematical model is important to solve real‐life instances, improve the quality of the solution, and reduce the total computation time. Therefore, we develop tools to assist with the design and analysis of a community healthcare network in order to increase health coverage for underserved areas. Results are presented for an application in Liberia, including sensitivity analyses on various parameters and managerial insights.
We study a multi‐country disaster preparedness partnership involving the joint prepositioning of emergency relief items. Our focus is the Caribbean region, which faces increasing disaster threats due to weather‐related events and has committed to share its resources for regional integration. We collaborate with the inter‐governmental Caribbean Disaster and Emergency Management Agency (CDEMA), which is interested in creating a methodology to equitably (fairly) allocate the costs necessary to operationalize this commitment. We present alternative cost allocation methods among the partner countries by considering their risk level and their ability to pay. Specifically, we adapt some techniques such as the Shapley value, the equal profit method, and the alternative cost avoided method, and we also propose a new insurance‐based allocation scheme to determine the country contributions. This mechanism, which is formulated as a linear programming model, sets country premiums by considering the expected value and the standard deviation of country demands and their gross national income. We discuss the structural properties of these methods and numerically evaluate their performance in achieving an equitable allocation scheme with respect to three equity indicators based on the Gini coefficient. Our proposed cost‐sharing mechanism not only achieves superior solutions compared with other methodologies with respect to the proposed equity metrics, but is also computationally efficient. We numerically illustrate how it can be used to obtain alternative cost allocation plans by giving different weights to disaster risk and economic standing parameters, and we analyze the benefits and fairness of the partnership in a transparent way.
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