Deep Reinforcement Learning based Actor-Critic Framework for Decision-Making Actions in Production Scheduling

Elsayed, Asmaa K.; Elsayed, Eman K.; ElDahshan, Kamal A.

doi:10.1109/icicis52592.2021.9694207

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…We used the settings that were previously used in our research and they performed well in [31], shown in Tab. 1, the results indicated that our proposed model has more effective to solve the problems with big data set that has more than 100 jobs and 100 machines.…”

Section: Experimentally and Results Analysis 41 Configuration And Hyp...mentioning

confidence: 99%

Deep Reinforcement Learning-Based Job Shop Scheduling of燬mart燤anufacturing

Elsayed¹,

Elsayed²,

ElDahshan³

2022

Computers, Materials &Amp; Continua

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Industry 4.0 production environments and smart manufacturing systems integrate both the physical and decision-making aspects of manufacturing operations into autonomous and decentralized systems. One of the key aspects of these systems is a production planning, specifically, Scheduling operations on the machines. To cope with this problem, this paper proposed a Deep Reinforcement Learning with an Actor-Critic algorithm (DRLAC). We model the Job-Shop Scheduling Problem (JSSP) as a Markov Decision Process (MDP), represent the state of a JSSP as simple Graph Isomorphism Networks (GIN) to extract nodes features during scheduling, and derive the policy of optimal scheduling which guides the included node features to the best next action of schedule. In addition, we adopt the Actor-Critic (AC) network's training algorithm-based reinforcement learning for achieving the optimal policy of the scheduling. To prove the proposed model's effectiveness, first, we will present a case study that illustrated a conflict between two job scheduling, secondly, we will apply the proposed model to a known benchmark dataset and compare the results with the traditional scheduling methods and trending approaches. The numerical results indicate that the proposed model can be adaptive with real-time production scheduling, where the average percentage deviation (APD) of our model achieved values between 0.009 and 0.21 compared with heuristic methods and values between 0.014 and 0.18 compared with other trending approaches.

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Section: Experimentally and Results Analysis 41 Configuration And Hyp...mentioning

confidence: 99%

Deep Reinforcement Learning-Based Job Shop Scheduling of燬mart燤anufacturing

Elsayed¹,

Elsayed²,

ElDahshan³

2022

Computers, Materials &Amp; Continua

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Enabling the Evaluation of Production Scheduling Algorithms in Complex Production Environments Using Individually Deployable Scheduling Services

Groth

Dippel

Schümann

2023

Lecture Notes in Computer Science

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Solving Flexible Job-Shop Scheduling Problem with Heterogeneous Graph Neural Network Based on Relation and Deep Reinforcement Learning

Tang,

Dong

2024

Machines

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Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry faces significant challenges in adapting to flexible and efficient production methods. This study presents an innovative approach to solving the Flexible Job-Shop Scheduling Problem (FJSP) by integrating Heterogeneous Graph Neural Networks based on Relation (HGNNR) with Deep Reinforcement Learning (DRL). The proposed framework models the complex relationships in FJSP using heterogeneous graphs, where operations and machines are represented as nodes, with directed and undirected arcs indicating dependencies and compatibilities. The HGNNR framework comprises four key components: relation-specific subgraph decomposition, data preprocessing, feature extraction through graph convolution, and cross-relation feature fusion using a multi-head attention mechanism. For decision-making, we employ the Proximal Policy Optimization (PPO) algorithm, which iteratively updates policies to maximize cumulative rewards through continuous interaction with the environment. Experimental results on four public benchmark datasets demonstrate that our proposed method outperforms four state-of-the-art DRL-based techniques and three common rule-based heuristic algorithms, achieving superior scheduling efficiency and generalization capabilities. This framework offers a robust and scalable solution for complex industrial scheduling problems, enhancing production efficiency and adaptability.

show abstract

Deep Reinforcement Learning based Actor-Critic Framework for Decision-Making Actions in Production Scheduling

Cited by 3 publications

References 15 publications

Deep Reinforcement Learning-Based Job Shop Scheduling of燬mart燤anufacturing

Deep Reinforcement Learning-Based Job Shop Scheduling of燬mart燤anufacturing

Enabling the Evaluation of Production Scheduling Algorithms in Complex Production Environments Using Individually Deployable Scheduling Services

Solving Flexible Job-Shop Scheduling Problem with Heterogeneous Graph Neural Network Based on Relation and Deep Reinforcement Learning

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