Ant Colony Optimization and Genetic Algorithm for Fuzzy Stochastic Production-Distribution Planning

Basit Öğe Kaydı
dc.contributor.authorSakalli, Umit Sami
dc.contributor.authorAtabas, Irfan
dc.date.accessioned2020-06-25T18:29:21Z
dc.date.available2020-06-25T18:29:21Z
dc.date.issued2018
dc.departmentKırıkkale Üniversitesi
dc.descriptionSAKALLI, Umit Sami/0000-0002-1695-3151
dc.description.abstractIn this paper, a tactical Production-Distribution Planning (PDP) has been handled in a fuzzy and stochastic environment for supply chain systems (SCS) which has four echelons (suppliers, plants, warehouses, retailers) with multi-products, multi-transport paths, and multi-time periods. The mathematical model of fuzzy stochastic PDP is a NP-hard problem for large SCS because of the binary variables which determine the transportation paths between echelons of the SCS and cannot be solved by optimization packages. In this study, therefore, two new meta-heuristic algorithms have been developed for solving fuzzy stochastic PDP: Ant Colony Optimization (ACO) and Genetic Algorithm (GA). The proposed meta-heuristic algorithms are designed for route optimization in PDP and integrated with the GAMS optimization package in order to solve the remaining mathematical model which determines the other decisions in SCS, such as procurement decisions, production decisions, etc. The solution procedure in the literature has been extended by aggregating proposed meta-heuristic algorithms. The ACO and GA algorithms have been performed for test problems which are randomly generated. The results of the test problem showed that the both ACO and GA are capable to solve the NP-hard PDP for a big size SCS. However, GA produce better solutions than the ACO.en_US
dc.identifier.citationSakallı Us, Atabas I. Ant Colony Optimization and Genetic Algorithm for Fuzzy Stochastic Production-Distribution Planning. Applied Sciences. 2018; 8(11):2042.en_US
dc.identifier.doi10.3390/app8112042
dc.identifier.issn2076-3417
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85055526316
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app8112042
dc.identifier.urihttps://hdl.handle.net/20.500.12587/7256
dc.identifier.volume8en_US
dc.identifier.wosWOS:000451302800032
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpien_US
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectproduction-distributionen_US
dc.subjectgenetic algorithmen_US
dc.subjectant colony optimizationen_US
dc.subjectfuzzyen_US
dc.subjectstochasticen_US
dc.titleAnt Colony Optimization and Genetic Algorithm for Fuzzy Stochastic Production-Distribution Planningen_US
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Ant Colony Optimization and Genetic Algorithm for Fuzzy Stochastic Production-Distribution Planning.pdf
Boyut:
3.17 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin/Full Text
İndir

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
Makale Koleksiyonu
Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu