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Full-Text Articles in Physical Sciences and Mathematics
Deep Reinforcement Learning Approach To Solve Dynamic Vehicle Routing Problem With Stochastic Customers, Waldy Joe, Hoong Chuin Lau
Deep Reinforcement Learning Approach To Solve Dynamic Vehicle Routing Problem With Stochastic Customers, Waldy Joe, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In real-world urban logistics operations, changes to the routes and tasks occur in response to dynamic events. To ensure customers’ demands are met, planners need to make these changes quickly (sometimes instantaneously). This paper proposes the formulation of a dynamic vehicle routing problem with time windows and both known and stochastic customers as a route-based Markov Decision Process. We propose a solution approach that combines Deep Reinforcement Learning (specifically neural networks-based TemporalDifference learning with experience replay) to approximate the value function and a routing heuristic based on Simulated Annealing, called DRLSA. Our approach enables optimized re-routing decision to be generated …
Hierarchical Multiagent Reinforcement Learning For Maritime Traffic Management, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Hierarchical Multiagent Reinforcement Learning For Maritime Traffic Management, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Increasing global maritime traffic coupled with rapid digitization and automation in shipping mandate developing next generation maritime traffic management systems to mitigate congestion, increase safety of navigation, and avoid collisions in busy and geographically constrained ports (such as Singapore's). To achieve these objectives, we model the maritime traffic as a large multiagent system with individual vessels as agents, and VTS (Vessel Traffic Service) authority as a regulatory agent. We develop a hierarchical reinforcement learning approach where vessels first select a high level action based on the underlying traffic flow, and then select the low level action that determines their future …
Using Reinforcement Learning To Minimize The Probability Of Delay Occurrence In Transportation, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Zhengghua Chen, Le Zhang, Xuexi Zhang
Using Reinforcement Learning To Minimize The Probability Of Delay Occurrence In Transportation, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Zhengghua Chen, Le Zhang, Xuexi Zhang
Research Collection School Of Computing and Information Systems
Reducing traffic delay is of crucial importance for the development of sustainable transportation systems, which is a challenging task in the studies of stochastic shortest path (SSP) problem. Existing methods based on the probability tail model to solve the SSP problem, seek for the path that minimizes the probability of delay occurrence, which is equal to maximizing the probability of reaching the destination before a deadline (i.e., arriving on time). However, they suffer from low accuracy or high computational cost. Therefore, we design a novel and practical Q-learning approach where the converged Q-values have the practical meaning as the actual …