Real-time scheduling for distributed permutation flowshops with dynamic job arrivals using deep reinforcement learning

Yang, Shengluo; Wang, Junyi; Xu, Zhigang

doi:10.1016/j.aei.2022.101776

Cited by 41 publications

(2 citation statements)

References 56 publications

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“…DRL-based methods are more promising than traditional methods in solving complex scheduling problems due to their low reliance on human experience and fast solving speed (after training). Recently, DRL has been employed to solve various scheduling problems such as permutation flow-shop [23], hybrid flow-shop [24] and flexible job-shop [25]. In particular, JSP has drawn much attention in DRL research as a well-known and standardized scheduling problem.…”

Section: Drl Methods For Scheduling Problemsmentioning

confidence: 99%

Deep Reinforcement Learning for Dynamic Twin Automated Stacking Cranes Scheduling Problem

Jin,

Mi,

Song

et al. 2023

Electronics

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Effective dynamic scheduling of twin Automated Stacking Cranes (ASCs) is essential for improving the efficiency of automated storage yards. While Deep Reinforcement Learning (DRL) has shown promise in a variety of scheduling problems, the dynamic twin ASCs scheduling problem is challenging owing to its unique attributes, including the dynamic arrival of containers, sequence-dependent setup and potential ASC interference. A novel DRL method is proposed in this paper to minimize the ASC run time and traffic congestion in the yard. Considering the information interference from ineligible containers, dynamic masked self-attention (DMA) is designed to capture the location-related relationship between containers. Additionally, we propose local information complementary attention (LICA) to supplement congestion-related information for decision making. The embeddings grasped by the LICA-DMA neural architecture can effectively represent the system state. Extensive experiments show that the agent can learn high-quality scheduling policies. Compared with rule-based heuristics, the learned policies have significantly better performance with reasonable time costs. The policies also exhibit impressive generalization ability in unseen scenarios with various scales or distributions.

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Section: Drl Methods For Scheduling Problemsmentioning

confidence: 99%

Deep Reinforcement Learning for Dynamic Twin Automated Stacking Cranes Scheduling Problem

Jin,

Mi,

Song

et al. 2023

Electronics

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show abstract

“…Further, the action space was categorized as going straight, turning right, or turning left. Learning systems are earlier to develop and require less troubleshooting by applying Q-learning to developing a path planning algorithm [3], we can improve safety while considering ecological forces as well as numerous port services, direction finding conveniences, and nearby ships while developing the algorithm. Experiment a huge-measure was deployed in a natural marine environment to introduce a collision avoidance algorithm.…”

Section: Introductionmentioning

confidence: 99%

Identify the Dynamic Position of Moving Vessels using Big Data and Deep Learning Approaches

Mistry¹,

Suaibia²,

Bin³

2023

EJECE

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Recently, various purposes such as computer vision, self-driving vehicles, self-directed shipping, and so on. Therefore, machine learning and deep learning algorithms have demonstrated strong routine. Using machine learning applications, we can implement sophisticated functionality without relying on complex code constructions. An alternative is to train a learning system on a collected training dataset and ensure that it performs as expected. Two main benefits of a deep-learning-based system over a hard-coded one exist. A Reinforcement Learning, and deep learning-based system does not require such complex hard-coded algorithms, which makes it less prone to error and easier to implement. In this study we proposed a deep learning-driven systems are able to adapt to different situations by retraining their algorithms on collected data. Proposed method influenced varying obtain leaning to another example of changing conditions. Systems can adapt even when input distributions change over time.

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Unrelated Parallel-Machine Scheduling Problem with Time-Changing Effects and Dynamic Job Arrivals

Guan,

Zhang,

Chen

2024

J. Shanghai Jiaotong Univ. (Sci.)

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Real-time scheduling for distributed permutation flowshops with dynamic job arrivals using deep reinforcement learning

Cited by 41 publications

References 56 publications

Deep Reinforcement Learning for Dynamic Twin Automated Stacking Cranes Scheduling Problem

Deep Reinforcement Learning for Dynamic Twin Automated Stacking Cranes Scheduling Problem

Identify the Dynamic Position of Moving Vessels using Big Data and Deep Learning Approaches

Unrelated Parallel-Machine Scheduling Problem with Time-Changing Effects and Dynamic Job Arrivals

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