A new focus on risk reduction: an ad hoc decision support system for humanitarian relief logistics. Issue 3 (28th May 2015)

• Record Type: Journal Article
• Authors: Schätter, Frank; Wiens, Marcus; Schultmann, Frank
• Is Part Of: Ecosystem health and sustainability. Volume 1:Issue 3(2015)
• Page Start: 1
• Page End: 11
• Publication Date: 2015-05-28
• Subjects: ad hoc decision support -- humanitarian relief logistics -- information and communication technology -- multi-criteria decision analysis -- public safety-critical supply chains -- robustness -- scenario techniques; Ecosystem health -- Periodicals; Environmental health -- Periodicals; Environmental policy -- Periodicals; Environmental engineering -- Periodicals; Environmental ethics -- Periodicals; Ecosystem management -- Periodicals; Ecosystem management; Environmental engineering; Environmental ethics; Environmental health; Environmental policy; Periodicals; 577
• DOI: 10.1890/EHS14-0020.1
• Languages: English
• ISSNs: 2096-4129

Abstract

Abstract : Particularly in the early phases of a disaster, logistical decisions are needed to be made quickly and under high pressure for the decision‐makers, knowing that their decisions may have direct consequences on the affected society and all future decisions. Proactive risk reduction may be helpful in providing decision‐makers with optimal strategies in advance. However, disasters are characterized by severe uncertainty and complexity, limited knowledge about the causes of the disaster, and continuous change of the situation in unpredicted ways. Following these assumptions, we believe that adequate proactive risk reduction measures are not practical. We propose strengthening the focus on ad hoc decision support to capture information in almost real time and to process information efficiently to reveal uncertainties that had not been previously predicted. Therefore, we present an ad hoc decision support system that uses scenario techniques to capture uncertainty by future developments of a situation and an optimization model to compute promising decision options. By combining these aspects in a dynamic manner and integrating new information continuously, it can be ensured that a decision is always based on the best currently available and processed information. And finally, to identify a robust decision option that is provided as a decision recommendation to the decision‐makers, methods of multi‐attribute decision making (MADM) are applied. Our approach is illustrated for a facility location decision problem arising in humanitarian relief logistics where the objective is to identify robust locations for tent hospitals to serve injured people in the immediate aftermath of the Haiti Earthquake 2010.