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

^*Bu çalışma için yazışmadan sorumlu yazar

Endüstri Mühendisliği Bölümü

Turkcell Teknoloji

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

3 Atıf (Scopus)

Özet

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.

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	1017-1024
Sayfa sayısı	8
ISBN (Basılı)	9783030237554
DOI'lar	https://doi.org/10.1007/978-3-030-23756-1_120
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.

Finansman

This work is supported by Scientific and Technological Research Council of Turkey (TÜBİTAK), TEYDEB 1505, Grant No: 5170012.

Finansörler	Finansör numarası
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu	5170012

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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; Hac. 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. editör / 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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title = "Weighting performance indicators of debt collection offices by using hesitant fuzzy AHP",

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",

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_120",

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isbn = "9783030237554",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

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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, hac. 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; Hac. 1029).

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

TY - GEN

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AU - Oztaysi, Basar

AU - Yücel, Mustafa

AU - Onar, Sezi Cevik

AU - Öztürk, Ersan

AU - Kahraman, Cengiz

PY - 2020

Y1 - 2020

N2 - 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.

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.

KW - Analytic hierarchy process

KW - Hesitant fuzzy sets

KW - Key performance indicators

KW - Performance measurement

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A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

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PB - Springer Verlag

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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, editörler, 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

Özet

Yayın serisi

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Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

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