Multi-Objective Multi-Resource Task Allocation For Collaborative Robots Systems

Irene Granata; Maurizio Faccio; Yuval Cohen

doi:10.1016/j.ifacol.2023.10.483

Multi-Objective Multi-Resource Task Allocation For Collaborative Robots Systems

Irene Granata, Maurizio Faccio, Yuval Cohen

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

Abstract

The shift from Industry 4.0 to Industry 5.0 has led to a greater focus on workers' needs in the workplace. Collaborative robots have been introduced to promote a fair division of tasks and reduce physical and mental strain on workers. However, there is a lack of research on how to implement human-centered task allocation. This study proposes a model for multi-objective task allocation, including minimizing makespan, energy expenditure, and mental workload. The study also suggests a method for evaluating mental workload. Results show that the strictness of task sequence affects makespan and energy expenditure, and a new constraint related to idle times is proposed. The optimal level of worker saturation is one that minimizes makespan while minimizing increases in energy expenditure and mental workload.

Original language	English
Title of host publication	IFAC-PapersOnLine
Editors	Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
Publisher	Elsevier B.V.
Pages	5643-5648
Number of pages	6
Edition	2
ISBN (Electronic)	9781713872344
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.483
State	Published - 1 Jul 2023
Externally published	Yes
Event	22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023

Publication series

Name	IFAC-PapersOnLine
Number	2
Volume	56
ISSN (Electronic)	2405-8963

Conference

Conference	22nd IFAC World Congress
Country/Territory	Japan
City	Yokohama
Period	9/07/23 → 14/07/23

Bibliographical note

Publisher Copyright:
Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Keywords

Human-automation integration
Human-centric manufacturing
Industry 4.0
Industry 5.0
Intelligent manufacturing systems

Access to Document

10.1016/j.ifacol.2023.10.483

Cite this

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Multi-Objective Multi-Resource Task Allocation For Collaborative Robots Systems. / Granata, Irene; Faccio, Maurizio; Cohen, Yuval.
IFAC-PapersOnLine. ed. / Hideaki Ishii; Yoshio Ebihara; Jun-ichi Imura; Masaki Yamakita. 2. ed. Elsevier B.V., 2023. p. 5643-5648 (IFAC-PapersOnLine; Vol. 56, No. 2).

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

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N2 - The shift from Industry 4.0 to Industry 5.0 has led to a greater focus on workers' needs in the workplace. Collaborative robots have been introduced to promote a fair division of tasks and reduce physical and mental strain on workers. However, there is a lack of research on how to implement human-centered task allocation. This study proposes a model for multi-objective task allocation, including minimizing makespan, energy expenditure, and mental workload. The study also suggests a method for evaluating mental workload. Results show that the strictness of task sequence affects makespan and energy expenditure, and a new constraint related to idle times is proposed. The optimal level of worker saturation is one that minimizes makespan while minimizing increases in energy expenditure and mental workload.

AB - The shift from Industry 4.0 to Industry 5.0 has led to a greater focus on workers' needs in the workplace. Collaborative robots have been introduced to promote a fair division of tasks and reduce physical and mental strain on workers. However, there is a lack of research on how to implement human-centered task allocation. This study proposes a model for multi-objective task allocation, including minimizing makespan, energy expenditure, and mental workload. The study also suggests a method for evaluating mental workload. Results show that the strictness of task sequence affects makespan and energy expenditure, and a new constraint related to idle times is proposed. The optimal level of worker saturation is one that minimizes makespan while minimizing increases in energy expenditure and mental workload.

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KW - Industry 5.0

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ER -

Multi-Objective Multi-Resource Task Allocation For Collaborative Robots Systems

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Keywords

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