Multiobjective problem modeling of the capacitated vehicle routing problem with urgency in a pandemic period

Basit Öğe Kaydı
dc.authorid0000-0002-3029-1123en_US
dc.authorid0000-0003-1236-7961en_US
dc.contributor.authorAltınöz, Mehmet
dc.contributor.authorAltınöz, Ökkes Tolga
dc.date.accessioned2023-09-05T05:37:18Z
dc.date.available2023-09-05T05:37:18Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Gerede Uygulamalı Bilimler Fakültesi, Uluslararası Ticaret ve Lojistik Bölümüen_US
dc.description.abstractThis research is based on the capacitated vehicle routing problem with urgency where each vertex corresponds to a medical facility with a urgency level and the traveling vehicle could be contaminated. This contamination is defined as the infectiousness rate, which is defined for each vertex and each vehicle. At each visited vertex, this rate for the vehicle will be increased. Therefore time-total distance it is desired to react to vertex as fast as possible- and infectiousness rate are main issues in the problem. This problem is solved with multiobjective optimization algorithms in this research. As a multiobjective problem, two objectives are defined for this model: the time and the infectiousness, and will be solved using multiobjective optimization algorithms which are nondominated sorting genetic algorithm (NSGAII), grid-based evolutionary algorithm GrEA, hypervolume estimation algorithm HypE, strength Pareto evolutionary algorithm shift-based density estimation SPEA2-SDE, and reference points-based evolutionary algorithm.en_US
dc.identifier.citationAltinoz, M., & Altinoz, O. T. (2023). Multiobjective problem modeling of the capacitated vehicle routing problem with urgency in a pandemic period. Neural Computing and Applications, 35(5), 3865-3882.en_US
dc.identifier.doi10.1007/s00521-022-07921-y
dc.identifier.endpage3882en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue5en_US
dc.identifier.pmid36267470en_US
dc.identifier.scopus2-s2.0-85139849866en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3865en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s00521-022-07921-y
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11655
dc.identifier.volume35en_US
dc.identifier.wosWOS:000869202600004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorAltınöz, Mehmet
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectVehicle Routing Problemen_US
dc.subjectMultiobjective Optimization Algorithmen_US
dc.subjectMany Objective Optimization Algorithmen_US
dc.subjectOptimizationen_US
dc.subjectGenetic Algorithmen_US
dc.subjectOptimizationen_US
dc.titleMultiobjective problem modeling of the capacitated vehicle routing problem with urgency in a pandemic perioden_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
mehmet-altinöz.pdf
Boyut:
889 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam metin/Full text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
Uluslararası Ticaret ve Lojistik Bölümü Koleksiyonu