Optimized Task Planning of Transfer Robots Using Reinforcement Learning

Ji Whan Park; Sang Do Noh

doi:10.1007/978-3-031-43670-3_41

Optimized Task Planning of Transfer Robots Using Reinforcement Learning

Ji Whan Park
, Sang Do Noh

Department of Systems Management Engineering

Sungkyunkwan University

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

Abstract

Industry 4.0, which has been actively studied in recent years, requires various digital transformation technologies. These technologies collect various data from processes and machines, provide useful information to the manufacturing system, and integrate them into the virtual world by synchronizing the physical manufacturing resources and their digital models. It has become a very important and necessary method to reflect the status of a manufacturing shop floor using data, analyzing it, and finding better approaches through simulations. In this study, a transfer robot used in various production lines was analyzed to determine the range of tasks. Generally, each transfer robot in a production line has a specific range of tasks, and task allocation has a significant impact on the overall productivity. In this study, the range of tasks performed by transfer robots was optimizes by applying reinforcement learning algorithms and simulations, and the result was applied to a real display panel production line. Based on the results, this study shows that the convergent application of data analytics, production simulation, and artificial intelligence algorithms contributes to increased productivity of production lines.

Original language	English
Title of host publication	Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings
Editors	Erlend Alfnes, Anita Romsdal, Jan Ola Strandhagen, Gregor von Cieminski, David Romero
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	591-602
Number of pages	12
ISBN (Print)	9783031436697
DOIs	https://doi.org/10.1007/978-3-031-43670-3_41
State	Published - 2023
Event	IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023 - Trondheim, Norway Duration: 17 Sep 2023 → 21 Sep 2023

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	691 AICT
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023
Country/Territory	Norway
City	Trondheim
Period	17/09/23 → 21/09/23

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure

Keywords

reinforcement learning
simulation
transfer robot

Access to Document

10.1007/978-3-031-43670-3_41

Cite this

Park, J. W., & Do Noh, S. (2023). Optimized Task Planning of Transfer Robots Using Reinforcement Learning. In E. Alfnes, A. Romsdal, J. O. Strandhagen, G. von Cieminski, & D. Romero (Eds.), Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings (pp. 591-602). (IFIP Advances in Information and Communication Technology; Vol. 691 AICT). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43670-3_41

Park, Ji Whan ; Do Noh, Sang. / Optimized Task Planning of Transfer Robots Using Reinforcement Learning. Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings. editor / Erlend Alfnes ; Anita Romsdal ; Jan Ola Strandhagen ; Gregor von Cieminski ; David Romero. Springer Science and Business Media Deutschland GmbH, 2023. pp. 591-602 (IFIP Advances in Information and Communication Technology).

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title = "Optimized Task Planning of Transfer Robots Using Reinforcement Learning",

abstract = "Industry 4.0, which has been actively studied in recent years, requires various digital transformation technologies. These technologies collect various data from processes and machines, provide useful information to the manufacturing system, and integrate them into the virtual world by synchronizing the physical manufacturing resources and their digital models. It has become a very important and necessary method to reflect the status of a manufacturing shop floor using data, analyzing it, and finding better approaches through simulations. In this study, a transfer robot used in various production lines was analyzed to determine the range of tasks. Generally, each transfer robot in a production line has a specific range of tasks, and task allocation has a significant impact on the overall productivity. In this study, the range of tasks performed by transfer robots was optimizes by applying reinforcement learning algorithms and simulations, and the result was applied to a real display panel production line. Based on the results, this study shows that the convergent application of data analytics, production simulation, and artificial intelligence algorithms contributes to increased productivity of production lines.",

keywords = "reinforcement learning, simulation, transfer robot",

author = "Park, \{Ji Whan\} and \{Do Noh\}, Sang",

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year = "2023",

doi = "10.1007/978-3-031-43670-3\_41",

language = "English",

isbn = "9783031436697",

series = "IFIP Advances in Information and Communication Technology",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "591--602",

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booktitle = "Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings",

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Park, JW & Do Noh, S 2023, Optimized Task Planning of Transfer Robots Using Reinforcement Learning. in E Alfnes, A Romsdal, JO Strandhagen, G von Cieminski & D Romero (eds), Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings. IFIP Advances in Information and Communication Technology, vol. 691 AICT, Springer Science and Business Media Deutschland GmbH, pp. 591-602, IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023, Trondheim, Norway, 17/09/23. https://doi.org/10.1007/978-3-031-43670-3_41

Optimized Task Planning of Transfer Robots Using Reinforcement Learning. / Park, Ji Whan; Do Noh, Sang.
Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings. ed. / Erlend Alfnes; Anita Romsdal; Jan Ola Strandhagen; Gregor von Cieminski; David Romero. Springer Science and Business Media Deutschland GmbH, 2023. p. 591-602 (IFIP Advances in Information and Communication Technology; Vol. 691 AICT).

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

TY - GEN

T1 - Optimized Task Planning of Transfer Robots Using Reinforcement Learning

AU - Park, Ji Whan

AU - Do Noh, Sang

PY - 2023

Y1 - 2023

N2 - Industry 4.0, which has been actively studied in recent years, requires various digital transformation technologies. These technologies collect various data from processes and machines, provide useful information to the manufacturing system, and integrate them into the virtual world by synchronizing the physical manufacturing resources and their digital models. It has become a very important and necessary method to reflect the status of a manufacturing shop floor using data, analyzing it, and finding better approaches through simulations. In this study, a transfer robot used in various production lines was analyzed to determine the range of tasks. Generally, each transfer robot in a production line has a specific range of tasks, and task allocation has a significant impact on the overall productivity. In this study, the range of tasks performed by transfer robots was optimizes by applying reinforcement learning algorithms and simulations, and the result was applied to a real display panel production line. Based on the results, this study shows that the convergent application of data analytics, production simulation, and artificial intelligence algorithms contributes to increased productivity of production lines.

AB - Industry 4.0, which has been actively studied in recent years, requires various digital transformation technologies. These technologies collect various data from processes and machines, provide useful information to the manufacturing system, and integrate them into the virtual world by synchronizing the physical manufacturing resources and their digital models. It has become a very important and necessary method to reflect the status of a manufacturing shop floor using data, analyzing it, and finding better approaches through simulations. In this study, a transfer robot used in various production lines was analyzed to determine the range of tasks. Generally, each transfer robot in a production line has a specific range of tasks, and task allocation has a significant impact on the overall productivity. In this study, the range of tasks performed by transfer robots was optimizes by applying reinforcement learning algorithms and simulations, and the result was applied to a real display panel production line. Based on the results, this study shows that the convergent application of data analytics, production simulation, and artificial intelligence algorithms contributes to increased productivity of production lines.

KW - reinforcement learning

KW - simulation

KW - transfer robot

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DO - 10.1007/978-3-031-43670-3_41

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AN - SCOPUS:85174445766

SN - 9783031436697

T3 - IFIP Advances in Information and Communication Technology

SP - 591

EP - 602

BT - Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings

A2 - Alfnes, Erlend

A2 - Romsdal, Anita

A2 - Strandhagen, Jan Ola

A2 - von Cieminski, Gregor

A2 - Romero, David

PB - Springer Science and Business Media Deutschland GmbH

T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023

Y2 - 17 September 2023 through 21 September 2023

ER -

Park JW, Do Noh S. Optimized Task Planning of Transfer Robots Using Reinforcement Learning. In Alfnes E, Romsdal A, Strandhagen JO, von Cieminski G, Romero D, editors, Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 591-602. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-031-43670-3_41

Optimized Task Planning of Transfer Robots Using Reinforcement Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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