Publication:
A machine learning approach to deal with ambiguity in the humanitarian decision-making

dc.contributor.authorGrass, E.
dc.contributor.authorOrtmann, J.
dc.contributor.authorKoyuncu, Burcu Balçık
dc.contributor.authorRei, W.
dc.contributor.departmentIndustrial Engineering
dc.contributor.ozuauthorKOYUNCU, Burcu Balçık
dc.date.accessioned2023-11-01T09:58:15Z
dc.date.available2023-11-01T09:58:15Z
dc.date.issued2023-09
dc.description.abstractOne of the major challenges for humanitarian organizations in response planning is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in postdisaster settings may involve missing elements and inconsistencies, which can hamper effective humanitarian decision-making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision-makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision-making. To the best of our knowledge, the integration of ambiguous information into decision-making by combining a cluster machine learning method with stochastic optimization has not been done before. We illustrate the proposed approach on a realistic case study that focuses on locating shelters to serve internally displaced people (IDP) in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates in making shelter location decisions. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively. With the newly proposed methodology, the decision-maker is able to analyze the degree of ambiguity in the data and the degree of consensus between different data sources to ultimately make better decisions for delivering humanitarian aid.en_US
dc.description.versionPublisher versionen_US
dc.identifier.doi10.1111/poms.14018en_US
dc.identifier.endpage2974en_US
dc.identifier.issn1059-1478en_US
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85158837965
dc.identifier.startpage2956en_US
dc.identifier.urihttp://hdl.handle.net/10679/8912
dc.identifier.urihttps://doi.org/10.1111/poms.14018
dc.identifier.volume32en_US
dc.identifier.wos000983954000001
dc.language.isoengen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.publisherWileyen_US
dc.relation.ispartofProduction and Operations Management
dc.relation.publicationcategoryInternational Refereed Journal
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution-NonCommercial-NoDerivs 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.keywordsAmbiguityen_US
dc.subject.keywordsClusteringen_US
dc.subject.keywordsData aggregationen_US
dc.subject.keywordsHumanitarian decision-makingen_US
dc.subject.keywordsNeeds assessmenten_US
dc.titleA machine learning approach to deal with ambiguity in the humanitarian decision-makingen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isOrgUnitOfPublication5dd73c02-fd2d-43e0-9a23-71bab9ae0b6b
relation.isOrgUnitOfPublication.latestForDiscovery5dd73c02-fd2d-43e0-9a23-71bab9ae0b6b

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