Dynamic scheduling in a job-shop production system with reinforcement learning

Kardos, Csaba; Laflamme, Catherine; Gallina, V.; Sihn, Wilfried

doi:10.1016/j.procir.2020.05.210

Cited by 33 publications

(15 citation statements)

References 19 publications

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“…Shahrabi et al 37 used RL to improve the scheduling performance for dynamic job-shop scheduling problems, considering random job arrivals and machine failures. Kardos et al 38 designed a scheduling algorithm based on Q-learning to solve the dynamic job-shop scheduling problem for reducing the average lead-time of production orders. However, it is difficult for basic Q-learning algorithms to adapt to problems with a large action space.…”

Section: Literature Reviewmentioning

confidence: 99%

Reinforcement learning for online optimization of job-shop scheduling in a smart manufacturing factory

Zhou

Zhu

Tang

et al. 2022

Advances in Mechanical Engineering

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The job-shop scheduling problem (JSSP) is a complex combinatorial problem, especially in dynamic environments. Low-volume-high-mix orders contain various design specifications that bring a large number of uncertainties to manufacturing systems. Traditional scheduling methods are limited in handling diverse manufacturing resources in a dynamic environment. In recent years, artificial intelligence (AI) arouses the interests of researchers in solving dynamic scheduling problems. However, it is difficult to optimize the scheduling policies for online decision making while considering multiple objectives. Therefore, this paper proposes a smart scheduler to handle real-time jobs and unexpected events in smart manufacturing factories. New composite reward functions are formulated to improve the decision-making abilities and learning efficiency of the smart scheduler. Based on deep reinforcement learning (RL), the smart scheduler autonomously learns to schedule manufacturing resources in real time and improve its decision-making abilities dynamically. We evaluate and validate the proposed scheduling model with a series of experiments on a smart factory testbed. Experimental results show that the smart scheduler not only achieves good learning and scheduling performances by optimizing the composite reward functions, but also copes with unexpected events (e.g. urgent or simultaneous orders, machine failures) and balances between efficiency and profits.

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Section: Literature Reviewmentioning

confidence: 99%

Reinforcement learning for online optimization of job-shop scheduling in a smart manufacturing factory

Zhou

Zhu

Tang

et al. 2022

Advances in Mechanical Engineering

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“…The simulation results showed that the performance of the designed algorithm was better than the existing rules. Combining the learning and optimization, Martínez et al [61] proposed a two-stage method to solve the flexible job shop scheduling problem. In the first stage, Q-learning was used to realize machine assignment and job scheduling and the feasible solution can be generated.…”

Section: Rl For Job Shop Schedulingmentioning

confidence: 99%

A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling

Wang

Pan

Wang

2021

Complex Syst. Model. Simul.

161

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As the critical component of manufacturing systems, production scheduling aims to optimize objectives in terms of profit, efficiency, and energy consumption by reasonably determining the main factors including processing path, machine assignment, execute time and so on. Due to the large scale and strongly coupled constraints nature, as well as the real-time solving requirement in certain scenarios, it faces great challenges in solving the manufacturing scheduling problems. With the development of machine learning, Reinforcement Learning (RL) has made breakthroughs in a variety of decision-making problems. For manufacturing scheduling problems, in this paper we summarize the designs of state and action, tease out RLbased algorithm for scheduling, review the applications of RL for different types of scheduling problems, and then discuss the fusion modes of reinforcement learning and meta-heuristics. Finally, we analyze the existing problems in current research, and point out the future research direction and significant contents to promote the research and applications of RL-based scheduling optimization.

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“…The reward functions as well as the action-space are shaped by hand according to the scenario. Kardos et al (2021) used Q-learning to select a service provider for the next operation in a small scenario with only a few steps. The approach presented by May et al (2021) uses multiple agents for the routing and the scheduling.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning

Heger

Voß

2021

International Journal of Production Research

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Given the fact that finding the optimal sequence in a flexible flow shop is usually an NP-hard problem, priority-based sequencing rules are applied in many real-world scenarios. In this contribution, an innovative reinforcement learning approach is used as a hyper-heuristic to dynamically adjust the k-values of the ATCS sequencing rule in a complex manufacturing scenario. For different product mixes as well as different utilisation levels, the reinforcement learning approach is trained and compared to the k-values found with an extensive simulation study. This contribution presents a human comprehensible hyper-heuristic, which is able to adjust the k-values to internal and external stimuli and can reduce the mean tardiness up to 5%.

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Dynamic scheduling in a job-shop production system with reinforcement learning

Cited by 33 publications

References 19 publications

Reinforcement learning for online optimization of job-shop scheduling in a smart manufacturing factory

Reinforcement learning for online optimization of job-shop scheduling in a smart manufacturing factory

A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling

Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning

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