Data Driven Approach to Order Picking Time Prediction Using Fuzzy Clustering and ANN

Özgür Kabak; Nurullah Güleç

doi:10.1007/978-3-030-85626-7_3

Data Driven Approach to Order Picking Time Prediction Using Fuzzy Clustering and ANN

Özgür Kabak, Nurullah Güleç^*

^*Corresponding author for this work

Department of Industrial Engineering

Yildirim Beyazit Universitesi

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

3 Citations (Scopus)

Abstract

The developments in technology increase the amount of data produced in manufacturing and service systems. This leads to the preference for data-driven approaches instead of model-based approaches in the management of processes. As one of the most costly and labor-intensive parts of supply chain processes, warehouse operations are very critical for the effective management of supply chains. Picking time is an important parameter for warehouse problems. This study aims to develop a data-driven approach to predict picking time in an automobile spare parts warehouse by considering the characteristics of the pickers. We integrated fuzzy clustering and Artificial Neural Networks (ANN) for predicting picking times accurately. In our novel approach, pickers have been grouped to decrease the number of inputs by using a fuzzy clustering method. ANN model is trained to estimate the picking time of new orders by using fuzzy membership information and historical picking data. Picking time predictions can be used as the first step in solving many warehouse problems.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
Editors	Cengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	18-26
Number of pages	9
ISBN (Print)	9783030856250
DOIs	https://doi.org/10.1007/978-3-030-85626-7_3
Publication status	Published - 2022
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey Duration: 24 Aug 2021 → 26 Aug 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	307
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2021
Country/Territory	Turkey
City	Istanbul
Period	24/08/21 → 26/08/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

ANN
Data-driven modelling
Fuzzy clustering

Access to Document

10.1007/978-3-030-85626-7_3

Cite this

Kabak, Ö., & Güleç, N. (2022). Data Driven Approach to Order Picking Time Prediction Using Fuzzy Clustering and ANN. In C. Kahraman, S. Cebi, S. Cevik Onar, B. Oztaysi, A. C. Tolga, & I. U. Sari (Eds.), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference (pp. 18-26). (Lecture Notes in Networks and Systems; Vol. 307). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85626-7_3

Kabak, Özgür ; Güleç, Nurullah. / Data Driven Approach to Order Picking Time Prediction Using Fuzzy Clustering and ANN. Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. editor / Cengiz Kahraman ; Selcuk Cebi ; Sezi Cevik Onar ; Basar Oztaysi ; A. Cagri Tolga ; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. pp. 18-26 (Lecture Notes in Networks and Systems).

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abstract = "The developments in technology increase the amount of data produced in manufacturing and service systems. This leads to the preference for data-driven approaches instead of model-based approaches in the management of processes. As one of the most costly and labor-intensive parts of supply chain processes, warehouse operations are very critical for the effective management of supply chains. Picking time is an important parameter for warehouse problems. This study aims to develop a data-driven approach to predict picking time in an automobile spare parts warehouse by considering the characteristics of the pickers. We integrated fuzzy clustering and Artificial Neural Networks (ANN) for predicting picking times accurately. In our novel approach, pickers have been grouped to decrease the number of inputs by using a fuzzy clustering method. ANN model is trained to estimate the picking time of new orders by using fuzzy membership information and historical picking data. Picking time predictions can be used as the first step in solving many warehouse problems.",

keywords = "ANN, Data-driven modelling, Fuzzy clustering",

author = "{\"O}zg{\"u}r Kabak and Nurullah G{\"u}le{\c c}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2021 ; Conference date: 24-08-2021 Through 26-08-2021",

year = "2022",

doi = "10.1007/978-3-030-85626-7_3",

language = "English",

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Kabak, Ö & Güleç, N 2022, Data Driven Approach to Order Picking Time Prediction Using Fuzzy Clustering and ANN. in C Kahraman, S Cebi, S Cevik Onar, B Oztaysi, AC Tolga & IU Sari (eds), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Lecture Notes in Networks and Systems, vol. 307, Springer Science and Business Media Deutschland GmbH, pp. 18-26, International Conference on Intelligent and Fuzzy Systems, INFUS 2021, Istanbul, Turkey, 24/08/21. https://doi.org/10.1007/978-3-030-85626-7_3

Data Driven Approach to Order Picking Time Prediction Using Fuzzy Clustering and ANN. / Kabak, Özgür; Güleç, Nurullah.
Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. ed. / Cengiz Kahraman; Selcuk Cebi; Sezi Cevik Onar; Basar Oztaysi; A. Cagri Tolga; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. p. 18-26 (Lecture Notes in Networks and Systems; Vol. 307).

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

TY - GEN

T1 - Data Driven Approach to Order Picking Time Prediction Using Fuzzy Clustering and ANN

AU - Kabak, Özgür

AU - Güleç, Nurullah

PY - 2022

Y1 - 2022

N2 - The developments in technology increase the amount of data produced in manufacturing and service systems. This leads to the preference for data-driven approaches instead of model-based approaches in the management of processes. As one of the most costly and labor-intensive parts of supply chain processes, warehouse operations are very critical for the effective management of supply chains. Picking time is an important parameter for warehouse problems. This study aims to develop a data-driven approach to predict picking time in an automobile spare parts warehouse by considering the characteristics of the pickers. We integrated fuzzy clustering and Artificial Neural Networks (ANN) for predicting picking times accurately. In our novel approach, pickers have been grouped to decrease the number of inputs by using a fuzzy clustering method. ANN model is trained to estimate the picking time of new orders by using fuzzy membership information and historical picking data. Picking time predictions can be used as the first step in solving many warehouse problems.

AB - The developments in technology increase the amount of data produced in manufacturing and service systems. This leads to the preference for data-driven approaches instead of model-based approaches in the management of processes. As one of the most costly and labor-intensive parts of supply chain processes, warehouse operations are very critical for the effective management of supply chains. Picking time is an important parameter for warehouse problems. This study aims to develop a data-driven approach to predict picking time in an automobile spare parts warehouse by considering the characteristics of the pickers. We integrated fuzzy clustering and Artificial Neural Networks (ANN) for predicting picking times accurately. In our novel approach, pickers have been grouped to decrease the number of inputs by using a fuzzy clustering method. ANN model is trained to estimate the picking time of new orders by using fuzzy membership information and historical picking data. Picking time predictions can be used as the first step in solving many warehouse problems.

KW - ANN

KW - Data-driven modelling

KW - Fuzzy clustering

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DO - 10.1007/978-3-030-85626-7_3

M3 - Conference contribution

AN - SCOPUS:85115080911

SN - 9783030856250

T3 - Lecture Notes in Networks and Systems

SP - 18

EP - 26

BT - Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference

A2 - Kahraman, Cengiz

A2 - Cebi, Selcuk

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Tolga, A. Cagri

A2 - Sari, Irem Ucal

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2021

Y2 - 24 August 2021 through 26 August 2021

ER -

Kabak Ö, Güleç N. Data Driven Approach to Order Picking Time Prediction Using Fuzzy Clustering and ANN. In Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga AC, Sari IU, editors, Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Springer Science and Business Media Deutschland GmbH. 2022. p. 18-26. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-85626-7_3

Data Driven Approach to Order Picking Time Prediction Using Fuzzy Clustering and ANN

Abstract

Publication series

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Bibliographical note

Keywords

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