Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities

Yan, Yimo; Chow, Ahf; Ho, Chin Pang; Kuo, Yong-Hong; Wu, Qihao; Ying, Chengshuo

doi:10.1016/j.tre.2022.102712

Cited by 76 publications

(8 citation statements)

References 124 publications

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“…Reinforcement learning (RL) is a field within machine learning that investigates how intelligent agents should make decisions in an environment to maximize their cumulative reward. With the success of Alpha Go [9], RL has gained considerable attention in many fields, including energy [10], civil engineering [11], network system [12], finance [13], logistics [14], and transportation [15]. For a brief survey with recent advances in reinforcement learning, please refer to Arulkumaran et al [16].…”

Section: Applications Of Reinforcement Learningmentioning

confidence: 99%

Traffic light control with reinforcement learning

Pan

2024

ACE

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Urban traffic signal optimization is important for alleviating congestion in urban transportation systems. This study proposes a real-time traffic light control algorithm based on deep Q learning with a reward function that accounts for queue lengths, delays, travel times, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed signal timing and an online stage using real-time traffic data. A deep Q network structure with aphase gate component is used to simplify the model's learning task under different phases. Amemory palace" mechanism is used to address sample imbalance during the training process. Both synthetic and real-world traffic flow data are used to validate our approach under an urban road intersection scenario in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal timing plans.

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Section: Applications Of Reinforcement Learningmentioning

confidence: 99%

Traffic light control with reinforcement learning

Pan

2024

ACE

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“…Modern supply chain management methodologies [22][23][24] have greatly optimized this process. New methods to ensure population mobility [25][26][27] have also significantly improved the organizational component of urban logistics.…”

Section: Literature Review and Defining The Problemmentioning

confidence: 99%

Generalising cases of insensitive queuing city traffic management systems and their impact on the delivery of goods by air and road

Kuchma,

Gabrielova,

Lytvynenko

et al. 2024

MATEC Web Conf.

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It is established that the urban traffic management system, as well as the aviation traffic management system, has a significant impact on the multimodal delivery of goods in the road-air transport, which requires the creation of tools for theoretical substantiation and practical implementation. The authors� method of determination the occurrence of vehicles queues in cities using mathematical modelling tools for three scenarios of crossing intersections was developed. The conceptual scheme for calculating the occurrence of the queue of vehicles was proposed. It was noted that vehicles in the queue often arrive randomly, the duration of service is constant, and the probability that before the queue disappears for the first time a certain number of cars cross the intersection has a Borel-Tanner distribution. The given mathematical modelling should become a part of tools for the implementation of the integrated intelligent information system of cities as a cluster of the city traffic management system.

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“…This approach significantly boosts profits across various stock indices, outperforming traditional systems in volatile markets. Moreover, in supply chain and logistics, the manuscript [ 53 ] reviewed the increasing deep reinforcement learning to address challenges stemming from evolving business operations and E-commerce growth, discussing methodologies, applications, and future research directions.…”

Section: Preliminariesmentioning

confidence: 99%

Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning

Olivares,

Salinas,

Ravelo

et al. 2024

Biomimetics

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In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms—namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm—with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning’s potential to boost cybersecurity measures in rapidly evolving threat environments.

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Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities

Cited by 76 publications

References 124 publications

Traffic light control with reinforcement learning

Traffic light control with reinforcement learning

Generalising cases of insensitive queuing city traffic management systems and their impact on the delivery of goods by air and road

Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning

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