Performance measurement of debt collection firms using spherical fuzzy aggregation operators

Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi

doi:10.1007/978-3-030-23756-1_63

Performance measurement of debt collection firms using spherical fuzzy aggregation operators

Cengiz Kahraman^*, Sezi Cevik Onar, Basar Oztaysi

^*Bu çalışma için yazışmadan sorumlu yazar

Endüstri Mühendisliği Bölümü

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

11 Atıf (Scopus)

Özet

Debt collection is a problematic area of the lenders. When the lenders cannot collect their debts, they rent debt collection firms for their debts to be collected from borrowers. Selection among numerous collection firms is another challenging problem with several criteria under uncertainty. Based on the past performances of the collection firms, we rank these firms using a spherical fuzzy multiattribute decision making approach. Spherical fuzzy sets are a new extension of ordinary fuzzy sets developed by Kutlu Gundogdu and Kahraman [1] based on the independent membership, nonmembership, and hesitancy degrees on the unit sphere. The proposed multiattribute decision making method uses aggregation operators for spherical fuzzy sets and score functions. We present the application of a real project in Turkey.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editörler	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Yayınlayan	Springer Verlag
Sayfalar	506-514
Sayfa sayısı	9
ISBN (Basılı)	9783030237554
DOI'lar	https://doi.org/10.1007/978-3-030-23756-1_63
Yayın durumu	Yayınlandı - 2020
Etkinlik	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Süre: 23 Tem 2019 → 25 Tem 2019

Yayın serisi

Adı	Advances in Intelligent Systems and Computing
Hacim	1029
ISSN (Basılı)	2194-5357
ISSN (Elektronik)	2194-5365

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	23/07/19 → 25/07/19

Bibliyografik not

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

Belgeye Erişim

10.1007/978-3-030-23756-1_63

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Kahraman, C., Onar, S. C., & Oztaysi, B. (2020). Performance measurement of debt collection firms using spherical fuzzy aggregation operators. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference (pp. 506-514). (Advances in Intelligent Systems and Computing; Hac. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_63

Kahraman, Cengiz ; Onar, Sezi Cevik ; Oztaysi, Basar. / Performance measurement of debt collection firms using spherical fuzzy aggregation operators. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. editör / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A.Cagri Tolga. Springer Verlag, 2020. pp. 506-514 (Advances in Intelligent Systems and Computing).

@inproceedings{bc77d6e03d24447eb231c3cb518c9492,

title = "Performance measurement of debt collection firms using spherical fuzzy aggregation operators",

abstract = "Debt collection is a problematic area of the lenders. When the lenders cannot collect their debts, they rent debt collection firms for their debts to be collected from borrowers. Selection among numerous collection firms is another challenging problem with several criteria under uncertainty. Based on the past performances of the collection firms, we rank these firms using a spherical fuzzy multiattribute decision making approach. Spherical fuzzy sets are a new extension of ordinary fuzzy sets developed by Kutlu Gundogdu and Kahraman [1] based on the independent membership, nonmembership, and hesitancy degrees on the unit sphere. The proposed multiattribute decision making method uses aggregation operators for spherical fuzzy sets and score functions. We present the application of a real project in Turkey.",

keywords = "Aggregation operator, Debt collection, Law firms, Performance measurement, Spherical fuzzy sets",

author = "Cengiz Kahraman and Onar, {Sezi Cevik} and Basar Oztaysi",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2019 ; Conference date: 23-07-2019 Through 25-07-2019",

year = "2020",

doi = "10.1007/978-3-030-23756-1_63",

language = "English",

isbn = "9783030237554",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "506--514",

editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Selcuk Cebi and A.Cagri Tolga",

booktitle = "Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference",

address = "Germany",

}

Kahraman, C , Onar, SC & Oztaysi, B 2020, Performance measurement of debt collection firms using spherical fuzzy aggregation operators. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Advances in Intelligent Systems and Computing, hac. 1029, Springer Verlag, pp. 506-514, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_63

Performance measurement of debt collection firms using spherical fuzzy aggregation operators. / Kahraman, Cengiz ; Onar, Sezi Cevik ; Oztaysi, Basar.
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A.Cagri Tolga. Springer Verlag, 2020. p. 506-514 (Advances in Intelligent Systems and Computing; Hac. 1029).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Performance measurement of debt collection firms using spherical fuzzy aggregation operators

AU - Kahraman, Cengiz

AU - Onar, Sezi Cevik

AU - Oztaysi, Basar

PY - 2020

Y1 - 2020

N2 - Debt collection is a problematic area of the lenders. When the lenders cannot collect their debts, they rent debt collection firms for their debts to be collected from borrowers. Selection among numerous collection firms is another challenging problem with several criteria under uncertainty. Based on the past performances of the collection firms, we rank these firms using a spherical fuzzy multiattribute decision making approach. Spherical fuzzy sets are a new extension of ordinary fuzzy sets developed by Kutlu Gundogdu and Kahraman [1] based on the independent membership, nonmembership, and hesitancy degrees on the unit sphere. The proposed multiattribute decision making method uses aggregation operators for spherical fuzzy sets and score functions. We present the application of a real project in Turkey.

AB - Debt collection is a problematic area of the lenders. When the lenders cannot collect their debts, they rent debt collection firms for their debts to be collected from borrowers. Selection among numerous collection firms is another challenging problem with several criteria under uncertainty. Based on the past performances of the collection firms, we rank these firms using a spherical fuzzy multiattribute decision making approach. Spherical fuzzy sets are a new extension of ordinary fuzzy sets developed by Kutlu Gundogdu and Kahraman [1] based on the independent membership, nonmembership, and hesitancy degrees on the unit sphere. The proposed multiattribute decision making method uses aggregation operators for spherical fuzzy sets and score functions. We present the application of a real project in Turkey.

KW - Aggregation operator

KW - Debt collection

KW - Law firms

KW - Performance measurement

KW - Spherical fuzzy sets

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U2 - 10.1007/978-3-030-23756-1_63

DO - 10.1007/978-3-030-23756-1_63

M3 - Conference contribution

AN - SCOPUS:85069500285

SN - 9783030237554

T3 - Advances in Intelligent Systems and Computing

SP - 506

EP - 514

BT - Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A.Cagri

PB - Springer Verlag

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2019

Y2 - 23 July 2019 through 25 July 2019

ER -

Kahraman C , Onar SC , Oztaysi B. Performance measurement of debt collection firms using spherical fuzzy aggregation operators. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editörler, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Springer Verlag. 2020. p. 506-514. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_63

Performance measurement of debt collection firms using spherical fuzzy aggregation operators

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Yayın serisi

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Bibliyografik not

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

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