Reinforcement Learning With Composite Rewards for Production Scheduling in a Smart Factory

Zhou, Tong; Tang, Dunbing; Zhu, Haihua; Wang, Liping

doi:10.1109/access.2020.3046784

Cited by 36 publications

(12 citation statements)

References 30 publications

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“…In equation ( 3), Percentage_of_errors can be obtained by calculating the percentage of the difference of the predicted value and the answer value, divided by the answer value as shown in equation (4).…”

Section: Resultsmentioning

confidence: 99%

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Deep Neural Network Structure Design for Equipment Failure Prediction in Smart Factory

Jang¹

2021

APJCRI

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A smart factory is a system in which all equipment is connected through the Internet of Things (IoT) and all facilities are operated through a virtual physical system. In the factory, failure prediction for facilities maintenance combined with deep learning has recently attracted much attention to researchers. To perform deep learning-based prediction, a neural network model is built and trained using the learning dataset. The training performance varies depending on the structure of the constructed neural network design. In this study, an efficient design of a neural network is explored that is expected to have the best training performance using a dataset of vibration and temperature. To do this, different neural network models are implemented, and their performance was compared by evaluating loss, accuracy, and execution time. In the experiment, the neural network model was configured to have one input layer, two or three hidden layers, and one output layer, and performance metrics values were measured according to the internal node numbers in every hidden layer. Experimental results show that losses do not increase in spite of that node number becomes large. In general, as the structure of a deep neural network model is complex and the number of nodes inside it increases, it is expected that the loss value will be smaller. For accuracy, there was almost no significant difference between all models. In the case of execution time, it took more time as the number of internal nodes of the model increased.

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Section: Resultsmentioning

confidence: 99%

“…Recently, the manufacturing fields interest in smart factories has increased in response to these changes. Various techniques have been applied in an attempt to reduce the time and cost invested in facility management [4][5][6]. Earlier, facility management relied mainly on the experience and intuition of the person in charge.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Structure Design for Equipment Failure Prediction in Smart Factory

Jang¹

2021

APJCRI

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“…For the dynamic multi-objective job shop scheduling problem with just-in-time constraint, Hong and Prabhu [70] modelled the problem as SMDP and introduced a novel scheduling algorithm by using Qlearning. The performance of the algorithm was significantly better than other scheduling rules.…”

Section: Rl For Other Scheduling Problemsmentioning

confidence: 99%

A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling

Wang

Pan

Wang

2021

Complex Syst. Model. Simul.

136

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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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“…We can highlight state space design [12,25,33,107,144,179,193,208,217,220,222,224,227,266,267] and action space design [109,220,246,268], reward construction [14,76,110,199,220,226,246,[269][270][271][272][273], and exploration strategy planning [86,274] which can be determinants from the whole application point of view. [11,13,17,20,21,24,38,43,61,62,66,69,82,89,93], Allocation, assignment, resource management [20,22,…”

Section: Complexitymentioning

confidence: 99%

“…Referred publications Markov decision process [12,23,24,37,64,70,75,84,96,100,101,104,127,130,133,138,144,153,165,167,170,177,188,191,199], [203, 207, 211, 212, 214, 217, 220, 231, 252, 256-259, 263, 264, 272, 274, 281, 291, 309, 313, 320, 340, 343, 346], [369][370][371][372][373][374][375][376] Multiarmed bandit [61,66,102,198,351,377,378] Dynamic programming [16,19,27,52,68,70,84,90,93,…”

Section: Approachmentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

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Reinforcement Learning With Composite Rewards for Production Scheduling in a Smart Factory

Abstract: His research interest includes machine learning, internet of things, and scheduling of manufacturing systems.

Cited by 36 publications

References 30 publications

Deep Neural Network Structure Design for Equipment Failure Prediction in Smart Factory

Deep Neural Network Structure Design for Equipment Failure Prediction in Smart Factory

A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

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