Dynamic scheduling in a flow shop using Deep Reinforcement Learning - Artificial Intelligence for Sustainable and Resilient Production Systems PART IV, IFIP WG 5.7 International Conference, APMS 2021 Access content directly
Conference Papers Year : 2021

Dynamic scheduling in a flow shop using Deep Reinforcement Learning

Abstract

Machine Learning (ML) techniques and algorithms, which are emerging technologies in Industry 4.0, present new possibilities for complex scheduling methods. Since different rules can be applied to different circumstances, it can be difficult for the decision-maker to choose the right rule at any given time. The purpose of the paper is to build an "intelligent" tool that adapts its choices in response to changes in the state of the production line. A Deep Q-Network (DQN), a typical Deep Reinforcement Learning (DRL) method, is proposed for creating a self-optimizing scheduling policy. The system has a set of known dispatching rules for each machine's queue, from which the best one is dynamically chosen, according to the system state. The novelty of the paper is how the reward function, state, and action space are modelled. A series of experiments were conducted to determine the best DQN network size and the most influential hyperparameters for training.
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Dates and versions

hal-04032866 , version 1 (16-03-2023)

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  • HAL Id : hal-04032866 , version 1

Cite

Maria Grazia Marchesano, Guido Guizzi, Liberatina Carmela Santillo, Silvestro Vespoli. Dynamic scheduling in a flow shop using Deep Reinforcement Learning. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.152-106. ⟨hal-04032866⟩

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