dc.identifier.uri |
http://dx.doi.org/10.15488/9676 |
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dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/9732 |
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dc.contributor.author |
Rinciog, Alexandru
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dc.contributor.author |
Mieth, Carina
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dc.contributor.author |
Scheikl, Paul Maria
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dc.contributor.author |
Meyer, Anney
|
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dc.date.accessioned |
2020-03-16T15:21:41Z |
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dc.date.issued |
2020 |
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dc.identifier.citation |
Rinciog, Alexandru; Mieth, Carina; Scheikl, Paul Maria; Meyer, Anney: Sheet-Metal Production Scheduling Using AlphaGo Zero. In: Nyhuis, P.; Herberger, D.; Hübner, M. (Eds.): Proceedings of the Conference on Production Systems and Logistics : CPSL 2020. Hannover : publish-Ing., 2020, S. 342-352. DOI: https://doi.org/10.15488/9676 |
ger |
dc.description.abstract |
This work investigates the applicability of a reinforcement learning (RL) approach, specifically AlphaGo Zero (AZ), for optimizing sheet-metal (SM) production schedules with respect to tardiness and material waste. SM production scheduling is a complex job shop scheduling problem (JSSP) with dynamic operation times, routing flexibility and supplementary constraints. SM production systems are capable of processing a large number of highly heterogeneous jobs simultaneously. While very large relative to the JSSP literature, the SM-JSSP instances investigated in this work are small relative to the SM production reality. Given the high dimensionality of the SM-JSSP, computation of an optimal schedule is not tractable. Simple heuristic solutions often deliver bad results. We use AZ to selectively search the solution space. To this end, a single player AZ version is pretrained using supervised learning on schedules generated by a heuristic, fine-tuned using RL and evaluated through comparison with a heuristic baseline and Monte Carlo Tree Search. It will be shown that AZ outperforms the other approaches. The work’s scientific contribution is twofold: On the one hand, a novel scheduling problem is formalized such that it can be tackled using RL approaches. On the other hand, it is proved that AZ can be successfully modified to provide a solution for the problem at hand, whereby a new line of research into real-world applications of AZ is opened. |
eng |
dc.language.iso |
eng |
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dc.publisher |
Hannover : publish-Ing. |
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dc.relation.ispartof |
https://doi.org/10.15488/9640 |
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dc.relation.ispartof |
Proceedings of the Conference on Production Systems and Logistics : CPSL 2020 |
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dc.rights |
CC BY 3.0 DE |
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dc.rights.uri |
https://creativecommons.org/licenses/by/3.0/de/ |
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dc.subject |
Sheet-Metal Production |
eng |
dc.subject |
Production Scheduling |
eng |
dc.subject |
Reinforcement Learning |
eng |
dc.subject |
Job Shop Scheduling Problem |
eng |
dc.subject |
Monte Carlo Tree Search |
eng |
dc.subject |
AlphaGo Zero |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
620 | Ingenieurwissenschaften und Maschinenbau
|
ger |
dc.title |
Sheet-Metal Production Scheduling Using AlphaGo Zero |
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dc.type |
BookPart |
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dc.type |
Text |
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dc.description.version |
publishedVersion |
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tib.accessRights |
frei zug�nglich |
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