Resource selection in support of humanitarian operations: a case of the United States Navy
Abstract
Purpose
The purpose of this paper is to present a model for selecting specific assets to be used in relief and disaster response missions based on the capabilities of, and contributions to, the demanded need for humanitarian assistance and disaster relief. During past disasters, the US Navy (USN) has responded with whatever ships were in the area regardless of their contribution to the need. The authors use data from the USN that has been gathered in other studies as an illustration of how the model may be applied to allocate the most useful vessels at the lowest cost.
Design/methodology/approach
A simple optimization model is used that utilizes scored capabilities as well as the estimated costs associated with US Naval vessels that will allocate the most useful assets at the lowest economic cost.
Findings
The model selects the most effective assets while minimizing the estimated economic cost. The US Naval assets that contribute the most effective humanitarian assistance and disaster response capability at the lowest cost are amphibious ships, leased commercial vessels and ready reserve force cargo ships.
Originality/value
This research fills a critical gap in the literature as there is no research that takes into account national Navy capability and proposes a solution to find those assets that are most mission and cost effective. As the USN looks for ways to cut costs while meeting mission priorities it will be necessary to determine which ship classes and types contribute the most while saving cost. The model introduced in this research provides insight into where investments should be made to meet strategic goals.
Description
The article of record as published may be found at http://dx.doi.org/10.1108/JHLSCM-07-2017-0036
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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