A Fuzzy Profit Maximization Model Using Communities Viable Leaders for Information Diffusion in Dynamic Drivers Collaboration Networks

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dc.authoridmohammadi, hasan/0009-0005-4483-1214
dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridBADIEE, AGHDAS/0000-0001-7122-0922
dc.authoridDezhboro, Amirhossein/0000-0002-7141-5743
dc.authorwosidmohammadi, hasan/IYT-3594-2023
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.authorwosidBADIEE, AGHDAS/L-1930-2016
dc.contributor.authorKalantari, Hamed
dc.contributor.authorBadiee, Aghdas
dc.contributor.authorDezhboro, Amirhossein
dc.contributor.authorMohammadi, Hasan
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2024-05-19T14:40:13Z
dc.date.available2024-05-19T14:40:13Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractAssigning shipping orders to the most appropriate driver in the shortest time but with the highest profit is one of the major concerns of transportation companies. Many studies have been conducted on transportation service procurement systems; however, due to the lack of a framework for modeling human interactions, none of them has used the concept of information diffusion for this purpose. In this article, a monoplex weighted drivers' collaboration network is developed to model drivers' relationships within the transportation system. Besides, to identify and track communities during a given time interval of the network, a new community detection algorithm, called dynamic overlapping community detection (DOCD) algorithm, is designed, which can identify viable leaders in each community. In addition to detecting community leaders, the algorithm is able to monitor, assess, and detect the durability of these community leaders over time, which other algorithms are not able to. To evaluate the performance of the algorithm, it is compared with five different algorithms in terms of 14 evaluation measures. The results show the proposed DOCD algorithm outperforms the other algorithms with 88% superiority in the evaluation measures. Then, a fuzzy profit maximization model is developed using information diffused by the identified communities' viable leaders and information diffusion power of each community. Analyzing a real case study obtains two achievements in the form of high-risk scenario and low-risk scenario for well-known and novice transportation companies, respectively. Therefore, the obtained results show that transportation companies allocate orders to drivers based on their reputation and risk levels.en_US
dc.identifier.doi10.1109/TFUZZ.2022.3155275
dc.identifier.endpage379en_US
dc.identifier.issn1063-6706
dc.identifier.issn1941-0034
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85125743394en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage370en_US
dc.identifier.urihttps://doi.org10.1109/TFUZZ.2022.3155275
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4927
dc.identifier.volume31en_US
dc.identifier.wosWOS:000965345500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectCollaborationen_US
dc.subjectVehiclesen_US
dc.subjectHeuristic Algorithmsen_US
dc.subjectProcurementen_US
dc.subjectStochastic Processesen_US
dc.subjectRoadsen_US
dc.subjectCostsen_US
dc.subjectDynamic Drivers' Collaboration Networken_US
dc.subjectFuzzy Profit Maximizationen_US
dc.subjectInformation Diffusionen_US
dc.subjectMonoplex Networksen_US
dc.subjectOverlapping Community Detectionen_US
dc.subjectTransportation Service Procurement (Tsp)en_US
dc.titleA Fuzzy Profit Maximization Model Using Communities Viable Leaders for Information Diffusion in Dynamic Drivers Collaboration Networksen_US
dc.typeArticleen_US

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