An online algorithm balancing the efficiency and equity principles is proposed for the kidney resource assignment when only the current patient and resource information is known to the assignment network. In the algor...An online algorithm balancing the efficiency and equity principles is proposed for the kidney resource assignment when only the current patient and resource information is known to the assignment network. In the algorithm, the assignment is made according to the priority, which is calculated according to the efficiency principle and the equity principle. The efficiency principle is concerned with the post-transplantation immunity spending caused by the possible post-operation immunity rejection and patient’s mental depression due to the HLA mismatch. The equity principle is concerned with three other factors, namely the treatment spending incurred starting from the day of registering with the kidney assignment network, the post-operation immunity spending and the negative effects of waiting for kidney resources on the clinical efficiency. The competitive analysis conducted through computer simulation indicates that the efficiency competitive ratio is between 6.29 and 10.43 and the equity competitive ratio is between 1.31 and 5.21, demonstrating that the online algorithm is of great significance in application.展开更多
The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques,such as the internet of things(IoT)and mobile crowdsensing(MCS).The core concept behind MCS is to use the ...The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques,such as the internet of things(IoT)and mobile crowdsensing(MCS).The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively,with each mobile user completing much simpler micro-tasks.This paper discusses the task assignment problem in mobile crowdsensing,which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals.The goal is to minimize aggregate sensing time for mobile users,which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task quality.This paper introduces a two-phase task assignment framework called location time-based algorithm(LTBA).LTBA is a framework that enhances task assignment in MCS,whereas assigning tasks requires overlapping time intervals between tasks and mobile users’tasks and the location of tasks and mobile users’paths.The process of assigning the nearest task to the mobile user’s current path depends on the ant colony optimization algorithm(ACO)and Euclidean distance.LTBA combines two algorithms:(1)greedy online allocation algorithm and(2)bio-inspired traveldistance-balance-based algorithm(B-DBA).The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user.B-DBA was location-based and worked on maximizing total task quality.The results demonstrate that the average task quality is 0.8158,0.7093,and 0.7733 for LTBA,B-DBA,and greedy,respectively.The sensing time was reduced to 644,1782,and 685 time units for LTBA,B-DBA,and greedy,respectively.Combining the algorithms improves task assignment in MCS for both total task quality and sensing time.The results demonstrate that combining the two algorithms in LTBA is the best performance for total task quality and total sensing time,and the greedy algorithm follows it then B-DBA.展开更多
Assignments are an important tool to evaluate learners’learning effectiveness in online courses.Clarifying assignment design strategies is of great significance for promoting the quality construction of online educat...Assignments are an important tool to evaluate learners’learning effectiveness in online courses.Clarifying assignment design strategies is of great significance for promoting the quality construction of online education courses.This paper uses Bloom’s taxonomy framework revised by Anderson and Krathwohl(2001)as a reference to label the knowledge types and cognitive dimensions in the assignment context of eight courses.Combined with a literature review,a discipline–objective–schedule(DOS)three-dimensional analysis framework based on the achievement of curriculum objectives,the design of chapter schedule,and the heterogeneity of disciplines is constructed to conduct an in-depth analysis of online course assignment design strategies.The research findings show that the online course assignment design strategy has an obvious curriculum objective orientation,follows the gradual learning rule,and presents typical disciplinary differences.The study finds that the current assignment design of online courses has three issues:first,a mismatch between assignment design and curriculum objectives;second,a lack of diversity in assignment formats;and third,insufficient comprehensiveness of some subject assignments.Based on the above discussions,corresponding suggestions are provided.展开更多
新兴技术(大数据/人工智能/移动互联网等)的发展和本地生活服务O2O(Online to Offline)商业模式兴起,催生了即时配送新兴物流形态,而外卖配送平台线上强履约要求成为即时配送业务痛点之一.考虑了实时外卖订单和动态变化的骑手等因素,将...新兴技术(大数据/人工智能/移动互联网等)的发展和本地生活服务O2O(Online to Offline)商业模式兴起,催生了即时配送新兴物流形态,而外卖配送平台线上强履约要求成为即时配送业务痛点之一.考虑了实时外卖订单和动态变化的骑手等因素,将问题建模为带取送约束和时间约束的实时车辆调度优化问题.基于滚动时域机制将连续时间的动态问题划分为一系列离散静态子问题,设计了邻域搜索启发式算法进行求解.最后,基于大连市某外卖平台的订单业务数据对算法进行了验证,与已有文献中的方法相比,算法能有效降低平均配送时间及超时订单数量,在大规模问题场景下求解算法对平台履约影响更大,高效的调度优化算法有利于外卖平台降本增效.展开更多
针对新型时空众包平台出现的3类对象在线任务匹配问题,现有工作往往假设工人拥有最大可匹配任务数量,将多个任务一次性分配给一个工人,忽略了工人的工作时间,可能会导致后匹配到的任务等待时间过长。因此,本文考虑了工作时长的在线3类...针对新型时空众包平台出现的3类对象在线任务匹配问题,现有工作往往假设工人拥有最大可匹配任务数量,将多个任务一次性分配给一个工人,忽略了工人的工作时间,可能会导致后匹配到的任务等待时间过长。因此,本文考虑了工作时长的在线3类对象动态匹配(online dynamic assginment for three types of objects,ODAT)问题,结合遗传算法(genetic algorithm,GA)提出一种延迟匹配算法来解决该问题。通过构造任务森林结构,借鉴蒙特卡罗树搜索思想随机模拟生成初始解,采用双重变异算子、局部最优算子融合贪心算法实现定向最优进化,使用随机部分重启机制跳出局部最优解;同时还提出一种延迟阈值策略来进一步提升效用。最终在真实数据集和合成数据集上进行大量实验,验证了算法的有效性和可行性。展开更多
机会群智感知网络中,不同节点间的相遇间隔各异,任务由不同节点执行时的时间成本有较大差异性。为最小化任务平均完成时间,设计并实现了一种基于预测的多任务在线分配算法(online multi-task assignment based on prediction,OTAP)。基...机会群智感知网络中,不同节点间的相遇间隔各异,任务由不同节点执行时的时间成本有较大差异性。为最小化任务平均完成时间,设计并实现了一种基于预测的多任务在线分配算法(online multi-task assignment based on prediction,OTAP)。基于真实移动轨迹数据集,分析了节点间相遇间隔分布,设计了节点相遇规律发现子算法;利用对节点间的相遇间隔的预测,每次给执行节点分配在与任务分发者下次相遇间隔内能完成的最大任务量。针对4个不同的真实移动轨迹数据集,利用ONE模拟器,对OTAP算法性能进行了验证与分析。结果显示,相比于已有的NTA算法,OTAP在4个不同数据集中平均任务完成时间分别缩短了50.49%、45.34%、32.71%、32.23%,任务完成率在其中两个移动轨迹数据集中也有所提高。展开更多
基金supported by the National Natural Science Foundation of China (No.70702030)the National Under-graduate Innovation Experimental Project of China (No.610762)
文摘An online algorithm balancing the efficiency and equity principles is proposed for the kidney resource assignment when only the current patient and resource information is known to the assignment network. In the algorithm, the assignment is made according to the priority, which is calculated according to the efficiency principle and the equity principle. The efficiency principle is concerned with the post-transplantation immunity spending caused by the possible post-operation immunity rejection and patient’s mental depression due to the HLA mismatch. The equity principle is concerned with three other factors, namely the treatment spending incurred starting from the day of registering with the kidney assignment network, the post-operation immunity spending and the negative effects of waiting for kidney resources on the clinical efficiency. The competitive analysis conducted through computer simulation indicates that the efficiency competitive ratio is between 6.29 and 10.43 and the equity competitive ratio is between 1.31 and 5.21, demonstrating that the online algorithm is of great significance in application.
文摘The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques,such as the internet of things(IoT)and mobile crowdsensing(MCS).The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively,with each mobile user completing much simpler micro-tasks.This paper discusses the task assignment problem in mobile crowdsensing,which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals.The goal is to minimize aggregate sensing time for mobile users,which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task quality.This paper introduces a two-phase task assignment framework called location time-based algorithm(LTBA).LTBA is a framework that enhances task assignment in MCS,whereas assigning tasks requires overlapping time intervals between tasks and mobile users’tasks and the location of tasks and mobile users’paths.The process of assigning the nearest task to the mobile user’s current path depends on the ant colony optimization algorithm(ACO)and Euclidean distance.LTBA combines two algorithms:(1)greedy online allocation algorithm and(2)bio-inspired traveldistance-balance-based algorithm(B-DBA).The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user.B-DBA was location-based and worked on maximizing total task quality.The results demonstrate that the average task quality is 0.8158,0.7093,and 0.7733 for LTBA,B-DBA,and greedy,respectively.The sensing time was reduced to 644,1782,and 685 time units for LTBA,B-DBA,and greedy,respectively.Combining the algorithms improves task assignment in MCS for both total task quality and sensing time.The results demonstrate that combining the two algorithms in LTBA is the best performance for total task quality and total sensing time,and the greedy algorithm follows it then B-DBA.
文摘Assignments are an important tool to evaluate learners’learning effectiveness in online courses.Clarifying assignment design strategies is of great significance for promoting the quality construction of online education courses.This paper uses Bloom’s taxonomy framework revised by Anderson and Krathwohl(2001)as a reference to label the knowledge types and cognitive dimensions in the assignment context of eight courses.Combined with a literature review,a discipline–objective–schedule(DOS)three-dimensional analysis framework based on the achievement of curriculum objectives,the design of chapter schedule,and the heterogeneity of disciplines is constructed to conduct an in-depth analysis of online course assignment design strategies.The research findings show that the online course assignment design strategy has an obvious curriculum objective orientation,follows the gradual learning rule,and presents typical disciplinary differences.The study finds that the current assignment design of online courses has three issues:first,a mismatch between assignment design and curriculum objectives;second,a lack of diversity in assignment formats;and third,insufficient comprehensiveness of some subject assignments.Based on the above discussions,corresponding suggestions are provided.
文摘新兴技术(大数据/人工智能/移动互联网等)的发展和本地生活服务O2O(Online to Offline)商业模式兴起,催生了即时配送新兴物流形态,而外卖配送平台线上强履约要求成为即时配送业务痛点之一.考虑了实时外卖订单和动态变化的骑手等因素,将问题建模为带取送约束和时间约束的实时车辆调度优化问题.基于滚动时域机制将连续时间的动态问题划分为一系列离散静态子问题,设计了邻域搜索启发式算法进行求解.最后,基于大连市某外卖平台的订单业务数据对算法进行了验证,与已有文献中的方法相比,算法能有效降低平均配送时间及超时订单数量,在大规模问题场景下求解算法对平台履约影响更大,高效的调度优化算法有利于外卖平台降本增效.
文摘针对新型时空众包平台出现的3类对象在线任务匹配问题,现有工作往往假设工人拥有最大可匹配任务数量,将多个任务一次性分配给一个工人,忽略了工人的工作时间,可能会导致后匹配到的任务等待时间过长。因此,本文考虑了工作时长的在线3类对象动态匹配(online dynamic assginment for three types of objects,ODAT)问题,结合遗传算法(genetic algorithm,GA)提出一种延迟匹配算法来解决该问题。通过构造任务森林结构,借鉴蒙特卡罗树搜索思想随机模拟生成初始解,采用双重变异算子、局部最优算子融合贪心算法实现定向最优进化,使用随机部分重启机制跳出局部最优解;同时还提出一种延迟阈值策略来进一步提升效用。最终在真实数据集和合成数据集上进行大量实验,验证了算法的有效性和可行性。
文摘机会群智感知网络中,不同节点间的相遇间隔各异,任务由不同节点执行时的时间成本有较大差异性。为最小化任务平均完成时间,设计并实现了一种基于预测的多任务在线分配算法(online multi-task assignment based on prediction,OTAP)。基于真实移动轨迹数据集,分析了节点间相遇间隔分布,设计了节点相遇规律发现子算法;利用对节点间的相遇间隔的预测,每次给执行节点分配在与任务分发者下次相遇间隔内能完成的最大任务量。针对4个不同的真实移动轨迹数据集,利用ONE模拟器,对OTAP算法性能进行了验证与分析。结果显示,相比于已有的NTA算法,OTAP在4个不同数据集中平均任务完成时间分别缩短了50.49%、45.34%、32.71%、32.23%,任务完成率在其中两个移动轨迹数据集中也有所提高。
