Pre-positioning of emergency supplies: does putting a price on human life help to save lives?

Renata Turkeš (), Daniel Palhazi Cuervo and Kenneth Sörensen
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Renata Turkeš: University of Antwerp Operations Research Group ANT/OR
Daniel Palhazi Cuervo: University of Antwerp Operations Research Group ANT/OR
Kenneth Sörensen: University of Antwerp Operations Research Group ANT/OR

Annals of Operations Research, 2019, vol. 283, issue 1, No 35, 865-895

Abstract: Abstract The number of people affected by natural disasters or displaced by conflict, persecution, violence or human rights violations has been steadily increasing, doubling in a decade and reaching 141.1 million in 2017 (United Nations Office for the Coordination of Humanitarian Affairs (OCHA) in Global humanitarian overview 2017 (June status report), 2017. http://www.unocha.org/sites/unocha/files/dms/GHO-JuneStatusReport2017-EN.pdf). Fortunately, such trends have been accompanied by a growing research interest in the field of humanitarian logistics that investigates mechanisms which can improve assistance to disaster-affected communities and thus minimize human suffering. In spite of acknowledging a major difference between such an objective and the priorities of business logistics, many authors still adopt disaster relief problem formulations that aim to minimize costs. In this paper, we list a number of issues with the cost-minimizing approach, placing emphasis on the significant challenge of determining the controversial economic value of human suffering that is usually a part of such formulations. These issues can easily be circumvented by the alternative formulations that maximize response, i.e., minimize unmet demand directly. The aim of our study is to investigate if cost-minimizing formulations are ever more suitable to find good emergency strategies than the the alternative models that minimize unmet demand. The discussion about the two approaches is illustrated with the problem of increasing emergency preparedness by pre-positioning relief items at strategic locations. We evaluate the two formulations of the pre-positioning problem using a number of randomly generated instances and a case study focused on hurricane threat in the Gulf Coast area of the United States. The optimal solution of the model that minimizes unmet demand always meets at least the same percentage of demand as the cost-minimizing model, and is obtained in comparable computation time. Our study therefore suggests that putting a price on human life can and ergo should be avoided.

Keywords: Humanitarian logistics; Pre-positioning emergency supplies; Community operational research; Stochastic programming (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10479-017-2702-1

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