Deep Learning Approach to Technician Routing and Scheduling Problem

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dc.contributor.authorPekel, Engin
dc.date.accessioned2023-01-26T05:51:31Z
dc.date.available2023-01-26T05:51:31Z
dc.date.issued2022en_US
dc.departmentHitit Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThis paper proposes a hybrid algorithm including the Adam algorithm and body change operator (BCO). Feasible solutions to technician routing and scheduling problems (TRSP) are investigated by performing deep learning based on the Adam algorithm and the hybridization of Adam-BCO. TRSP is a problem where all tasks are routed, and technicians are scheduled. In the deep learning method based on the Adam algorithm and Adam-BCO algorithm, the weights of the network are updated, and these weights are evaluated as Greedy approach, and routing and scheduling are performed. The performance of the Adam-BCO algorithm is experimentally compared with the Adam and BCO algorithm by solving the TRSP on the instances developed from the literature. The numerical results evidence that Adam-BCO offers faster and better solutions considering Adam and BCO algorithm. The average solution time increases from 0.14 minutes to 4.03 minutes, but in return, Gap decreases from 9.99% to 5.71%. The hybridization of both algorithms through deep learning provides an effective and feasible solution, as evidenced by the results.
dc.identifier.citationPekel, E. (2022). Deep Learning Approach to Technician Routing and Scheduling Problem. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(2), 191-206.
dc.identifier.doi10.14201/adcaij.27393
dc.identifier.endpage206en_US
dc.identifier.issn2255-2863
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ4
dc.identifier.startpage191en_US
dc.identifier.urihttps://doi.org/10.14201/adcaij.27393
dc.identifier.urihttps://hdl.handle.net/11491/8418
dc.identifier.volume11en_US
dc.identifier.wosWOS:000883014800005
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEDICIONES UNIV SALAMANCA
dc.relation.ispartofADCAIJ
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAdam algorithmen_US
dc.subjectDeep learningen_US
dc.subjectOptimizationen_US
dc.subjectTechnician routing and schedulingen_US
dc.titleDeep Learning Approach to Technician Routing and Scheduling Problem
dc.typeArticle

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