Based on the study of visitors' individual spatial behaviors, a tourists'spatial behavior simulator (TSBS) to assess the carrying capacity of tourist resorts was developed,TSBS employs GIS (Geographic Informat...Based on the study of visitors' individual spatial behaviors, a tourists'spatial behavior simulator (TSBS) to assess the carrying capacity of tourist resorts was developed,TSBS employs GIS (Geographic Information System) to manage the spatial data,and Multi-Agent systemto simulate the actions of individual visitors. By utilizing TSBS, visitors' travel patterns such aslocation, cost, and state can be analyzed and predicted. Based on this analysis and prediction, themodel of assessing the carrying capacity of resorts is built. Our results show that TSBS will be aneffective tool to accurately assess the carrying capacity of tourist resorts.展开更多
This work introduces the Queen's University Agent-Based Outbreak Outcome Model(QUABOOM).This tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health policy.We illustrate t...This work introduces the Queen's University Agent-Based Outbreak Outcome Model(QUABOOM).This tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health policy.We illustrate the use of the model by examining capacity restrictions during a lockdown.We find that public health measures should focus on the few locations where many people interact,such as grocery stores,rather than the many locations where few people interact,such as small businesses.We also discuss a case where the results of the simulation can be scaled to larger population sizes,thereby improving computational efficiency.展开更多
Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning...Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning(MARL)for real-world DCB problems is proposed.The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management(ATFM)region to quickly obtain a satisfactory solution.In this method,agents of all flights in a scenario form a multi-agent decision-making system based on partial observation.The trained agent with the customised neural network can be deployed directly on the corresponding flight,allowing it to solve the DCB problem jointly.A cooperation coefficient is introduced in the reward function,which is used to adjust the agent’s cooperation preference in a multi-agent system,thereby controlling the distribution of flight delay time allocation.A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated.Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method.From a statis-tical point of view,it is proven that the proposed method is generalised within the scope of the flights and sectors of interest,and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods.The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.展开更多
文摘Based on the study of visitors' individual spatial behaviors, a tourists'spatial behavior simulator (TSBS) to assess the carrying capacity of tourist resorts was developed,TSBS employs GIS (Geographic Information System) to manage the spatial data,and Multi-Agent systemto simulate the actions of individual visitors. By utilizing TSBS, visitors' travel patterns such aslocation, cost, and state can be analyzed and predicted. Based on this analysis and prediction, themodel of assessing the carrying capacity of resorts is built. Our results show that TSBS will be aneffective tool to accurately assess the carrying capacity of tourist resorts.
基金support of the Department of Physics,Engineering Physics&Astronomy at Queen's University through a research initiation grant,the Queen's University Arts and Science Research Fundthe Queen's University Bartlett Student Initiatives Fundthe Natural Sciences and Engineering Research Council of Canada,funding reference number SAPIN-2017-00023.
文摘This work introduces the Queen's University Agent-Based Outbreak Outcome Model(QUABOOM).This tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health policy.We illustrate the use of the model by examining capacity restrictions during a lockdown.We find that public health measures should focus on the few locations where many people interact,such as grocery stores,rather than the many locations where few people interact,such as small businesses.We also discuss a case where the results of the simulation can be scaled to larger population sizes,thereby improving computational efficiency.
基金co-funded by the National Natural Science Foundation of China(No.61903187)the National Key R&D Program of China(No.2021YFB1600500)+2 种基金the China Scholarship Council(No.202006830095)the Natural Science Foundation of Jiangsu Province(No.BK20190414)the Jiangsu Province Postgraduate Innovation Fund(No.KYCX20_0213).
文摘Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning(MARL)for real-world DCB problems is proposed.The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management(ATFM)region to quickly obtain a satisfactory solution.In this method,agents of all flights in a scenario form a multi-agent decision-making system based on partial observation.The trained agent with the customised neural network can be deployed directly on the corresponding flight,allowing it to solve the DCB problem jointly.A cooperation coefficient is introduced in the reward function,which is used to adjust the agent’s cooperation preference in a multi-agent system,thereby controlling the distribution of flight delay time allocation.A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated.Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method.From a statis-tical point of view,it is proven that the proposed method is generalised within the scope of the flights and sectors of interest,and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods.The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.
文摘货位分配(storage location assignment problem,SLAP),即在存储区域为物料分配货位的过程。当仓库布局、拣货路径、订单组合等其他因素确定时,货位分配策略对订单拣货效率有很大影响。本文研究实际生产型仓库中的关联物料区位分配问题。生产中使用的相对稳定的BOM(bill of material)使得仓库中的物料具有稳定的相关性,因此,本文考虑将具有需求关联的物料存储在同一区域,以尽可能地减少在拣选物料时所需要的区域访问次数。此外,该仓库还存在两个重要特征,即存在两类不同尺寸货架构成的两类不同容量的区域及采用严格的重物下置原则。本文建立了以最小化区域访问次数为目标的数学规划模型,给出了求解该问题的一种聚类启发式方法与自适应大邻域搜索算法(adaptive large neighborhood search,ALNS),并设计了能够反映物料关联特征的小规模和大规模算例用于测试两种算法的性能。将两个算法结果与随机策略、CPLEX求解结果对比,结果显示聚类启发式方法与ALNS在大规模算例中表现明显优于随机策略和CPLEX的求解结果。
