Development of a neural network algorithm for estimating the makespan in jobshop production scheduling

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dc.contributor.authorYıldız, İncilay
dc.contributor.authorSaygın, Abdülvahap
dc.contributor.authorÇolak, Selçuk
dc.contributor.authorAbut, Fatih
dc.date.accessioned2023-08-21T06:34:36Z
dc.date.available2023-08-21T06:34:36Z
dc.date.issued2023en_US
dc.departmentFakülteler, Uygulamalı Bilimler Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.description.abstractSince production scheduling is considered a short-term plan for future production planning, the advantages of effective scheduling and control and their contribution to the production process are numerous. Efficient use of resources improves productivity and ensures that customer orders are met on time. Even the simplest scheduling system has a complex solution structure. Long lead times also make it difficult to estimate the demand accurately. Therefore, it is important to solve scheduling problems effectively for such difficult-to-manage production processes. Job shop scheduling (JSS) problems are among the combinatorial problems in the NP-hard problems class. As constraints increase in such problems, the solution space starts to go to infinity, making it increasingly difficult to find the exact optimum solution. For this reason, metaheuristic algorithms have been used to solve such problems in recent years. This study aims to develop an artificial neural network (ANN)-based application to produce an optimal or near-optimal solution for JSS. Using the job shop type production data of Taillard comparison problems, the total processing time (i.e., makespan) has been calculated with the proposed ANN application. The results have been compared with the results of related studies in the literature, and the algorithm's efficiency has been evaluated in detail.en_US
dc.identifier.citationYildiz, I., Saygin, A., Çolak, S., & Abut, F. (2023). Development of a neural network algorithm for estimating the makespan in jobshop production scheduling. Tehnički vjesnik, 30(4), 1257-1264.en_US
dc.identifier.endpage1264en_US
dc.identifier.issn1330-3651
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85166377694
dc.identifier.scopusqualityQ3
dc.identifier.startpage1257en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3683
dc.identifier.volume30en_US
dc.identifier.wosWOS:001027296400031
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorYıldız, İncilay
dc.language.isoen
dc.publisherStrojarski Faculteten_US
dc.relation.ispartofTehnicki Vjesnik
dc.relation.isversionof10.17559/TV-20220818161430en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectMakespanen_US
dc.subjectOptimizationen_US
dc.subjectScheduling Problemsen_US
dc.titleDevelopment of a neural network algorithm for estimating the makespan in jobshop production scheduling
dc.typeArticle

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