A general framework for assessing the value of social data for disaster response logistics planning

• Published in: European journal of operational research Vol. 269; no. 2; pp. 486 - 500
• Main Authors: Kirac, Emre, Milburn, Ashlea Bennett
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-09-2018
• Subjects: Disaster management; Disaster relief routing; Humanitarian logistics; Social media; Team orienteering problem; Humanitarian logistics; Team orienteering problem; Social media; Disaster management; Disaster relief routing
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2018.02.011

•Provides examples of current usage of social data by emergency managers.•Introduces general framework for assessing value of social data for disaster response planning.•Demonstrates framework on a mobile relief supply delivery example.•Develops case study based on 2010 Haiti earthquake that includes real social data.
Social media may play a critical role in disaster response by identifying needs in a shorter amount of time and thereby improving situational awareness. However, needs identified through social media initially have not been verified and some may be inaccurate. This can create a barrier to its use during disaster response decision making. Consequently, a key tradeoff between the timeliness and accuracy of social data for disaster response logistics planning must be assessed. This study aims to stimulate interest in a research agenda aimed at evaluating this tradeoff. A general framework for investigating whether it is worthwhile to act on user-generated data prior to its absolute verification in the context of disaster response logistics planning is presented. While the framework is applicable to a variety of logistics planning problems, this paper demonstrates its use via an application in mobile delivery of disaster response commodities. A case study motivated by the 2010 Haiti earthquake is developed using real social data, and is presented for use in conjunction with the framework. The types of insights that are possible via the framework are revealed through the small computational study included in the paper.