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

^*Corresponding author for this work

Department of Industrial Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

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.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Publisher	Springer Verlag
Pages	506-514
Number of pages	9
ISBN (Print)	9783030237554
DOIs	https://doi.org/10.1007/978-3-030-23756-1_63
Publication status	Published - 2020
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Duration: 23 Jul 2019 → 25 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1029
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Country/Territory	Turkey
City	Istanbul
Period	23/07/19 → 25/07/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Aggregation operator
Debt collection
Law firms
Performance measurement
Spherical fuzzy sets

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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

Cite this

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; Vol. 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. editor / 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).

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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",

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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, vol. 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; Vol. 1029).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

AU - Kahraman, Cengiz

AU - Onar, Sezi Cevik

AU - Oztaysi, Basar

PY - 2020

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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.

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KW - Debt collection

KW - Law firms

KW - Performance measurement

KW - Spherical fuzzy sets

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AN - SCOPUS:85069500285

SN - 9783030237554

T3 - Advances in Intelligent Systems and Computing

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

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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, editors, 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

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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