Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/325183 
Year of Publication: 
2023
Citation: 
[Journal:] EURO Journal on Transportation and Logistics (EJTL) [ISSN:] 2192-4384 [Volume:] 12 [Issue:] 1 [Article No.:] 100111 [Year:] 2023 [Pages:] 112-
Publisher: 
Elsevier, Amsterdam
Abstract: 
Panic-buying and shortages of essential commodities is common during early phases of a disaster or an epidemic. The goal of this paper is develop a methodology which includes social media information in optimization models of searching essential commodities during disasters and improves the efficiency of search. Specific contributions in the data processing of social media posts include the development of an event localizer that probabilistically infers the location and time of shortage of commodity based on social media information. Contributions in the mathematical model development include an integer programming formulation of the resultant search problem on a graph, with the two objective different objective functions: (a) Maximizing probability of finding the commodity (b) Minimizing expected time to find the commodity given the commodity is found. The methodology is validated using a case study on gasoline search during the Hurricane Irma evacuations. We found that social media posts can predict shortage at gas station for four major cities of Florida accurately with a MAPE of 12%. We also found that addition of social media information to the search process improved the average search time by 41.74%.
Subjects: 
Bayesian models
Humanitarian logistics
Search theory
Social sensing
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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