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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/25990

Title: A symbiotic human–machine learning approach for production ramp-up
Authors: Doltsinis, Stefanos
Ferreira, Pedro
Lohse, Niels
Keywords: Decision support
Machine learning
Ramp-up
Symbiotic human–machine systems
Issue Date: 2017
Publisher: © IEEE
Citation: DOLTSINIS, S., FERREIRA, P. and LOHSE, N., 2017. A symbiotic human–machine learning approach for production ramp-up. IEEE Transactions on Human-Machine Systems, doi:10.1109/THMS.2017.2717885.
Abstract: 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.
Description: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: 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).
Version: Accepted for publication
DOI: 10.1109/THMS.2017.2717885
URI: https://dspace.lboro.ac.uk/2134/25990
Publisher Link: http://dx.doi.org/10.1109/THMS.2017.2717885
ISSN: 2168-2291
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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