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Multi-echelon inventory optimization using deep reinforcement learning
This paper studies the applicability of a deep reinforcement learning approach to three different multi-echelon inventory systems, with the objective...
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Deep reinforcement learning for dynamic scheduling of energy-efficient automated guided vehicles
Automated guided vehicle (AGV) scheduling has become a hot topic in recent years as manufacturing systems become flexible and intelligent. However,...
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Deep reinforcement learning imbalanced credit risk of SMEs in supply chain finance
It is crucial to predict the credit risk of small and medium-sized enterprises (SMEs) accurately for the success of supply chain finance (SCF)....
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A generalisable tool path planning strategy for free-form sheet metal stam** through deep reinforcement and supervised learning
Due to the high cost of specially customised presses and dies and the advance of machine learning technology, there is some emerging research...
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The third party logistics provider freight management problem: a framework and deep reinforcement learning approach
In many large manufacturing companies, freight management is handled by a third-party logistics (3PL) provider, thus allowing manufacturers and their...
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Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection
The cybersecurity threat landscape has lately become overly complex. Threat actors leverage weaknesses in the network and endpoint security in a very...
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Promoting sustainable tourism by recommending sequences of attractions with deep reinforcement learning
Develo** Recommender Systems (RSs) is particularly interesting in the tourist domain, where one or more attractions have to be suggested to users...
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Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection
In the context of distributed machine learning, the concept of federated learning (FL) has emerged as a solution to the privacy concerns that users...
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Enterprise and service−level scheduling of robot production services in cloud manufacturing with deep reinforcement learning
Cloud manufacturing is a manufacturing paradigm that integrates wide-area distributed manufacturing resources for distributed services over the...
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A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization
In this paper, we address the controversies of epidemic control planning by develo** a novel Simulation-Deep Reinforcement Learning (SiRL) model....
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Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning
The Internet of Things (IoT) application scenarios is becoming extensive due to the quick evolution of smart devices with fifth-generation (5G)...
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Smart scheduling of dynamic job shop based on discrete event simulation and deep reinforcement learning
In the era of Industry 4.0, production scheduling as a critical part of manufacturing system should be smarter. Smart scheduling agent is required to...
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Optimizing inland container ship** through reinforcement learning
In this study, we investigate the container delivery problem and explore ways to optimize the complex and nuanced system of inland container...
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Instant flow distribution network optimization in liquid composite molding using deep reinforcement learning
Carbon fibre reinforced plastic (CFRP) manufacturing cycle time is a major driver of production rate and cost for aerospace manufacturers. In vacuum...
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A dynamic spectrum access algorithm based on deep reinforcement learning with novel multi-vehicle reward functions in cognitive vehicular networks
As a revolution in the field of transportation, the demand for communication of vehicles is increasing. Therefore, how to improve the success rate of...
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Reinforcement learning-based rate adaptation in dynamic video streaming
Video streaming stands out as the most significant traffic type consumed by mobile devices. This increased demand has been a major driver for...
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A goal-oriented reinforcement learning for optimal drug dosage control
The dosage control of therapeutic drugs is a concern for clinicians. Whether the clinician’s dosing decision is correct and efficient determines...
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Robust-stable scheduling in dynamic flow shops based on deep reinforcement learning
This proof-of-concept study provides a novel method for robust-stable scheduling in dynamic flow shops based on deep reinforcement learning (DRL)...
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Reinforcement learning for freight booking control problems
Booking control focuses on the problem of deciding whether to accept or reject bookings to maximize revenue while considering limited capacity. For...
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A deep reinforcement learning assisted simulated annealing algorithm for a maintenance planning problem
Maintenance planning aims to improve the reliability of assets, prevent the occurrence of asset failures, and reduce maintenance costs associated...
