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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Articles 1 - 4 of 4
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
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 …
Joint Optimization Control Of Energy Storage System Management And Demand Response, Xueying Gao, Tang Hao, Gangzhong Miao, Zhaowu Ping
Joint Optimization Control Of Energy Storage System Management And Demand Response, Xueying Gao, Tang Hao, Gangzhong Miao, Zhaowu Ping
Journal of System Simulation
Abstract: The joint optimization problem of energy management and demand response were studied in order to reduce the long-run cost of electricity users equipped with energy storage unit and smart applications, and to increase their benefits meanwhile. The goals were achieved by controlling both the energy storage unit (charging, discharging, or idle) and the load service (access or delay). Based on the random nature of solar photovoltaic, load demand electricity and electricity price, the joint optimization problem was modeled as infinite-horizon Markov decision process model, and Q-learning algorithm was proposed to find the optimal solution. Simulation results show that the …
Analysis And Optimization Of The Action Chain Mechanism In Agent2d Underlying In Robocup2d Soccer League, Chen Bing, Feifan Xu, Hanyan Xu, Zekai Cheng, Liu Cheng
Analysis And Optimization Of The Action Chain Mechanism In Agent2d Underlying In Robocup2d Soccer League, Chen Bing, Feifan Xu, Hanyan Xu, Zekai Cheng, Liu Cheng
Journal of System Simulation
Abstract: In the RoboCup2D soccer league, Agent2D is one of the most widely used underlying team in China. Data transmission noise and the incomplete action chain mechanism make the underlying teams using Agent2D be lack of flexibility. This paper introduces an action correcting parameter and optimizes the operation of the action chain by reinforcement learning mechanism. The performance of the Agent2D underlying team is improved in the game and the adaptability of the team is enhanced. Simulation experiment results show that this method has a certain effect.
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 …