A reinforcement learning based approach for a multiple-load carrier scheduling problem

Chen, Ci; Xia, Beixin; Zhou, Binghai; Xi, Lifeng

doi:10.1007/s10845-013-0852-9

Cited by 25 publications

(8 citation statements)

References 33 publications

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“…Firstly, RL is applicable to sequential decision problems, for instance, using Markov Decision Processes (MDP). Secondly, RL's applicability to related adaptive control problems in manufacturing systems has already been shown (Chen et al 2015;Stricker et al 2018;Usuga Cadavid et al 2020). Especially Deep Reinforcement Learning's (DRL) success in the gaming industry (Mnih et al 2013;Silver et al 2016) proved the method's generalizability and its solution quality, being applied to complex, dynamic optimization problems.…”

Section: Earlier Work Leads To Reinforcement Learning Closing the Research Gapmentioning

confidence: 99%

Modular production control using deep reinforcement learning: proximal policy optimization

2021

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EU regulations on $$\textit{CO}_2$$ CO 2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent’s action, state, and reward design.

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Section: Earlier Work Leads To Reinforcement Learning Closing the Research Gapmentioning

confidence: 99%

Modular production control using deep reinforcement learning: proximal policy optimization

2021

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“…In Ref. [65], an RL-based method is proposed for dispatching material handling dolly trains in a general assembly line, wherein the dolly train delivers materials to workstations and carries multiple types of parts at a time. In Ref.…”

Section: Resource Allocationmentioning

confidence: 99%

“…[66] and product queue lengths in Ref. [65], drive the transition of the system states, which are difficult to obtain the complete state transition models. RL fits such sequential decision-making problems well and can solve them in a modelfree way with various algorithms.…”

Section: Resource Allocationmentioning

confidence: 99%

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Arinez

Chang

Gao

et al. 2020

Journal of Manufacturing Science and Engineering

290

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Today's manufacturing systems are becoming increasingly complex, dynamic and connected. The factory operation faces challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in Artificial Intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) Review the State-of-the-Art applications of AI to representative manufacturing problems, (2) Provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) Identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human robotic collaboration, process monitoring, diagnosis and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

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“…These techniques are very useful in complex problems whose space are not completely available or predictable. Machine learning techniques 5 fall into three main categories, that are supervised learning [1], unsupervised learning [2], and reinforcement learning [3]. In the case of supervised learning techniques, there is a set of training data for which the solutions are available and the learner tries to infer a function from the training data to map unseen data with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Reinforcement signals are the only information the agent receives about its acting, and reinforcement techniques are about learning optimal acting in an environment using the reinforcement signals. Reinforcement learning is applicable to problems in various fields of study such as prediction [4], scheduling [5], wireless networks [6,7], robotics [8], ensemble learning [9] to mention a few. 20 The variety of applications and the concept of learning from experience, give rise to the study and design of reinforcement learning techniques for complex and large-scale problems.…”

Section: Introductionmentioning

confidence: 99%

A cooperative learning method based on cellular learning automata and its application in optimization problems

Mozafari

Shiri

Beigy

2015

Journal of Computational Science

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A reinforcement learning based approach for a multiple-load carrier scheduling problem

Cited by 25 publications

References 33 publications

Modular production control using deep reinforcement learning: proximal policy optimization

Modular production control using deep reinforcement learning: proximal policy optimization

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

A cooperative learning method based on cellular learning automata and its application in optimization problems

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