Weighting performance indicators of debt collection offices by using hesitant fuzzy AHP

Basar Oztaysi; Mustafa Yücel; Sezi Cevik Onar; Ersan Öztürk; Cengiz Kahraman

doi:10.1007/978-3-030-23756-1_120

Weighting performance indicators of debt collection offices by using hesitant fuzzy AHP

Basar Oztaysi^*, Mustafa Yücel, Sezi Cevik Onar, Ersan Öztürk, Cengiz Kahraman

^*Corresponding author for this work

Department of Industrial Engineering

Turkcell Teknoloji

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

3 Citations (Scopus)

Abstract

Performance measurement (PM) is defined as the process of collecting, analyzing and/or reporting information regarding the performance of individuals, teams or the whole organization. The first and one of the most important steps in performance measurement is determining the performance indicators and their weights. In this study, we focus on performance measurement of law offices, which deal with follow-up of unpaid bills. We propose a decision model for weighting the performance indicators using interval valued intuitionistic fuzzy AHP.

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	1017-1024
Number of pages	8
ISBN (Print)	9783030237554
DOIs	https://doi.org/10.1007/978-3-030-23756-1_120
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

Analytic hierarchy process
Hesitant fuzzy sets
Key performance indicators
Performance measurement

Access to Document

10.1007/978-3-030-23756-1_120

Cite this

Oztaysi, B., Yücel, M., Onar, S. C., Öztürk, E., & Kahraman, C. (2020). Weighting performance indicators of debt collection offices by using hesitant fuzzy AHP. 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. 1017-1024). (Advances in Intelligent Systems and Computing; Vol. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_120

Oztaysi, Basar ; Yücel, Mustafa ; Onar, Sezi Cevik et al. / Weighting performance indicators of debt collection offices by using hesitant fuzzy AHP. 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. 1017-1024 (Advances in Intelligent Systems and Computing).

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abstract = "Performance measurement (PM) is defined as the process of collecting, analyzing and/or reporting information regarding the performance of individuals, teams or the whole organization. The first and one of the most important steps in performance measurement is determining the performance indicators and their weights. In this study, we focus on performance measurement of law offices, which deal with follow-up of unpaid bills. We propose a decision model for weighting the performance indicators using interval valued intuitionistic fuzzy AHP.",

keywords = "Analytic hierarchy process, Hesitant fuzzy sets, Key performance indicators, Performance measurement",

author = "Basar Oztaysi and Mustafa Y{\"u}cel and Onar, {Sezi Cevik} and Ersan {\"O}zt{\"u}rk and Cengiz Kahraman",

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year = "2020",

doi = "10.1007/978-3-030-23756-1_120",

language = "English",

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series = "Advances in Intelligent Systems and Computing",

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

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Oztaysi, B, Yücel, M, Onar, SC, Öztürk, E & Kahraman, C 2020, Weighting performance indicators of debt collection offices by using hesitant fuzzy AHP. 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. 1017-1024, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_120

Weighting performance indicators of debt collection offices by using hesitant fuzzy AHP. / Oztaysi, Basar; Yücel, Mustafa; Onar, Sezi Cevik et al.
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. 1017-1024 (Advances in Intelligent Systems and Computing; Vol. 1029).

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

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AU - Yücel, Mustafa

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AU - Öztürk, Ersan

AU - Kahraman, Cengiz

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AB - Performance measurement (PM) is defined as the process of collecting, analyzing and/or reporting information regarding the performance of individuals, teams or the whole organization. The first and one of the most important steps in performance measurement is determining the performance indicators and their weights. In this study, we focus on performance measurement of law offices, which deal with follow-up of unpaid bills. We propose a decision model for weighting the performance indicators using interval valued intuitionistic fuzzy AHP.

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Oztaysi B, Yücel M, Onar SC, Öztürk E, Kahraman C. Weighting performance indicators of debt collection offices by using hesitant fuzzy AHP. 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. 1017-1024. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_120

Weighting performance indicators of debt collection offices by using hesitant fuzzy AHP

Abstract

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Bibliographical note

Keywords

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