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Towards Standardising Reinforcement Learning Approaches for Production Scheduling Problems

Rinciog, Alexandru; Meyer, Anne ORCID iD icon

Abstract:

Recent years have seen a rise in interest in terms of using machine learning, particularly reinforcement learning (RL), for production scheduling problems of varying degrees of complexity. The general approach is to break down the scheduling problem into a Markov Decision Process (MDP), whereupon a simulation implementing the MDP is used to train an RL agent. Since existing studies rely on (sometimes) complex simulations for which the code is unavailable, the experiments presented are hard, or, in the case of stochastic environments, impossible to reproduce accurately. Furthermore, there is a vast array of RL designs to choose from. To make RL methods widely applicable in production scheduling and work out their strength for the industry, the standardisation of model descriptions - both production setup and RL design - and validation scheme are a prerequisite. Our contribution is threefold: First, we standardize the description of production setups used in RL studies based on established nomenclature. Secondly, we classify RL design choices from existing publications. Lastly, we propose recommendations for a validation scheme focusing on reproducibility and sufficient benchmarking.


Verlagsausgabe §
DOI: 10.5445/IR/1000164520
Veröffentlicht am 17.11.2023
Originalveröffentlichung
DOI: 10.1016/j.procir.2022.05.117
Scopus
Zitationen: 10
Dimensions
Zitationen: 14
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationsmanagement im Ingenieurwesen (IMI)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 26.05.2022
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000164520
Erschienen in Procedia CIRP
Verlag Elsevier
Band 107
Seiten 1112–1119
Bemerkung zur Veröffentlichung Part of special issue Leading manufacturing systems transformation – Proceedings of the 55th CIRP Conference on Manufacturing Systems – CMS 2022 - held from June the 29th to July the 1st 2022 in Lugano
Externe Relationen arXiv
Schlagwörter Production Scheduling, Reinforcement Learning, Standardization, Validation
Nachgewiesen in Scopus
Dimensions
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