A reinforcement learning algorithm for scheduling parallel processors with identical speedup functions

Ziaei, Farid; Ranjbar, Mohammad

doi:10.1016/j.mlwa.2023.100485

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Ref. [23] solves the parallel processor scheduling problem with identical acceleration functions by introducing a new state variable into the reinforcement learning algorithm and by using a set of virtual boxes to classify jobs according to remaining processing time, generating reasonable scheduling rules. Ref.…”

Section: Reinforcement Learning Scheduling Methodsmentioning

confidence: 99%

Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction

Li,

Duan,

Xiong

et al. 2024

Sustainability

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The scheduling of harbor tugboats is a crucial task in port operations, aiming to optimize resource allocation and reduce operational costs, including fuel consumption of tugboats and the time cost of vessels waiting for operations. Due to the complexity of the port environment, traditional scheduling methods, often based on experience and practice, lack scientific and systematic decision support, making it difficult to cope with real-time changes in vessel dynamics and environmental factors. This often leads to scheduling delays and resource waste. To address this issue, this study proposes a mathematical model based on fuzzy programming, accounting for the uncertainty of the arrival time of target vessels. Additionally, we introduce the NRPER-DDPG algorithm (DDPG Algorithm with Prioritized Experience Replay and Noise Reduction), which combines a prioritized replay mechanism with a decaying noise strategy based on the DDPG algorithm. This approach optimizes the time for tugboats to reach the task location as a continuous action space, aiming to minimize the total system cost and improve scheduling efficiency. To verify the effectiveness of the mathematical model and algorithm, this study conducted experimental validation. Firstly, the optimal algorithm hyperparameter combinations were adjusted through random examples to ensure the stability and reliability of the algorithm. Subsequently, large-scale examples and actual port cases were used to further verify the performance advantages of the algorithm in practical applications. Experimental results demonstrate that the proposed mathematical model and algorithm significantly reduce system costs and improve scheduling efficiency, providing new insights and methods for the sustainable development of port operations.

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Section: Reinforcement Learning Scheduling Methodsmentioning

confidence: 99%

Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction

Li,

Duan,

Xiong

et al. 2024

Sustainability

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“…Reinforcement learning algorithms may learn from prior experiences and determine the best actions to take in an unknown environment to accomplish the optimal state transition for achieving the objective (Aytaç and Khayet, 2023b;Aytaç et al 2023c;Aytaç et al 2023d). Some of the research using these three branches of ML include robust fall detection in video surveillance (Lian et al, 2023), fire recognition (Sun et al, 2022), diagnosing Alzheimer's disease using brain magnetic resonance imaging scans (Liu et al, 2023), temporal dynamic vehicle-to-vehicle interactions (Guha et al, 2022), to control chaos synchronization between two identical chaotic systems (Cheng et al, 2023), scheduling parallel processors with identical speedup functions (Ziaei and Ranjbar, 2023), controller for dynamic positioning of an unmanned surface vehicle (Yuan and Rui, 2023). Machine learning approaches still require development to be able to create high-quality predictive models, especially in situations where causal relationships between variables are difficult to fully resolve (Aytaç, 2021b, Aytaç 2022b.…”

Section: Introductionmentioning

confidence: 99%

Object Detection and Regression Based Visible Spectrophotometric Analysis: A Demonstration Using Methylene Blue Solution

Aytaç

2023

ADCAIJ

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This study investigates the estimation of the concentration of methylene blue solutions to understand if visible spectrophotometry could be performed using a smartphone and machine learning. The presented procedure consists of taking photos, detecting test tubes and sampling region of interest (ROI) with YOLOv5, finding the hue, saturation, value (HSV) code of the dominant color in the ROI, and regression. 257 photos were taken for the procedure. The YOLOv5 object detection architecture was trained on 928 images and the highest mAP@05 values were detected as 0.915 in 300 epochs. For automatic ROI sampling, the YOLOv5 detect.py file was edited. The trained YOLOv5 detected 254 out of 257 test tubes and extracted ROIs. The HSV code of the dominant color in the exported ROI images was determined and stored in a csv file together with the concentration values. Subsequently, 25 different regression algorithms were applied to the generated data set. The extra trees regressor was the most generalizing model with 99.5% training and 99.4% validation R2 values. A hyperparameter tuning process was performed on the extra trees regressor and a mixed model was created using the best 3 regression algorithms to improve the R2 value. Finally, all three models were tested on unseen data and the lowest MSE value was found in the untuned extra trees regressor and blended model with values of 0.10564 and 0.16586, respectively. These results prove that visible spectrophotometric analysis can be performed using the presented procedure and that a mobile application can be developed for this purpose.

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Optimizing Energy Consumption through Scheduling in Low-resource Edge Clusters using Multi-agent PPO

Verninas,

Linguaglossa

2024

Proceedings of the CoNEXT on Student Workshop 2024

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A reinforcement learning algorithm for scheduling parallel processors with identical speedup functions

Cited by 3 publications

References 20 publications

Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction

Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction

Object Detection and Regression Based Visible Spectrophotometric Analysis: A Demonstration Using Methylene Blue Solution

Optimizing Energy Consumption through Scheduling in Low-resource Edge Clusters using Multi-agent PPO

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