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A symbiotic human–machine learning approach for production ramp-up

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posted on 2017-08-09, 09:01 authored by Stefanos Doltsinis, Pedro FerreiraPedro Ferreira, Niels LohseNiels Lohse
Constantly shorter product lifecycles and the high number of product variants necessitate frequent production system reconfigurations and changeovers. Shortening ramp-up and changeover times is essential to achieve the agility required to respond to these challenges. This work investigates a symbiotic human–machine environment, which combines a formal framework for capturing structured ramp-up experiences from expert production engineers with a reinforcement learning method to formulate effective ramp-up policies. Such learned policies have been shown to reduce unnecessary iterations in human decision-making processes by suggesting the most appropriate actions for different ramp-up states. One of the key challenges for machine learning based methods, particularly for episodic problems with complex state-spaces, such as ramp-up, is the exploration strategy that can maximize the information gain while minimizing the number of exploration steps required to find good policies. This paper proposes an exploration strategy for reinforcement learning, guided by a human expert. The proposed approach combines human intelligence with machine’s capability for processing data quickly, accurately, and reliably. The efficiency of the proposed human exploration guided machine learning strategy is assessed by comparing it with three machine-based exploration strategies. To test and compare the four strategies, a ramp-up emulator was built, based on system experimentation and user experience. The results of the experiments show that human-guided exploration can achieve close to optimal behavior, with far less data than what is needed for traditional machine-based strategies.

Funding

This work was supported in part by the European Commission, as part of the FP7 NMP FRAME project (CP-FP 229208-2), and in part by the EPSRC Centre for Innovated Manufacturing in Intelligent Automation (EP/IO33467/1).

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Human-Machine Systems

Volume

48

Issue

3

Pages

229-240

Citation

DOLTSINIS, S., FERREIRA, P. and LOHSE, N., 2018. A symbiotic human–machine learning approach for production ramp-up. IEEE Transactions on Human-Machine Systems, 48 (3), pp.229-240.

Publisher

IEEE

Version

  • VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

Acceptance date

2017-05-04

Publication date

2017-07-18

Notes

This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 3.0 Unported Licence (CC BY 3.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/3.0/

ISSN

2168-2291

eISSN

2168-2305

Language

  • en

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