Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

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dc.contributor.authorKayıkcı, Yaşanur
dc.date.accessioned2021-01-08T21:51:20Z
dc.date.available2021-01-08T21:51:20Z
dc.date.issued2020
dc.departmentTAÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.descriptionKayikci, Yasanur/0000-0003-2406-3164en_US
dc.descriptionWOS:000530054900001en_US
dc.description.abstractPurpose As the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains. Design/methodology/approach A causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted. Findings The result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure. Originality/value In this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.
dc.identifier.doi10.1108/JEIM-08-2019-0232
dc.identifier.issn1741-0398
dc.identifier.issn1758-7409
dc.identifier.scopus2-s2.0-85106363457
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://doi.org/10.1108/JEIM-08-2019-0232
dc.identifier.urihttps://hdl.handle.net/20.500.12846/148
dc.identifier.wosWOS:000530054900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKayıkcı, Yaşanur
dc.language.isoen
dc.publisherEmerald Group Publishing Ltd
dc.relation.ispartofJournal Of Enterprise Information Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectResilienceen_US
dc.subjectStreaming Dataen_US
dc.subjectLogistics Infrastructureen_US
dc.subjectEnvironmental Performanceen_US
dc.subjectFuzzy Cognitive Mapsen_US
dc.subjectFuzzy Analytic Hierarchy Processen_US
dc.subjectSustainabilityen_US
dc.titleStream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure
dc.typeArticle

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