大数据驱动的大型自行车共享系统及其站点车辆再平衡研究

近年来,大数据、云计算与物联网为复杂系统的组织与管理提供了有力的新型信息化技术,并引起了企业的组织架构与运营机制的多方面变化.基于此,本文首先针对大数据驱动的大型自行车共享系统构建了一个新的随机模型,既表达了大数据的重要作用,又描述了大型自行车共享系统的运营过程,特别是使用卡车对各个站点自行车的再平衡.其次,本文提出了一种研究大数据驱动的大型自行车共享系统的平均场极限理论,包括利用平均场理论建立非时齐的排队系统,由非时齐的排队系统建立系统的平均场方程组;给出了经验测度过程(empirical measure process)的非线性生灭过程,提出了分段结构下生灭过程的固定点的有效算法,由此能够计算每个站点稳态平均自行车数;用数值算例分析了每个站点稳态平均自行车数是如何依赖于自行车共享系统中的一些关键参数的.基于此,本文对大数据在大型自行车共享系统中所引起的物理效应进行了建模分析,从而为大型自行车共享系统的随机分析提供了一个极有研究潜力的重要发展方向.     

平均场倒向重随机微分方程及其应用

研究了平均场倒向随重机微分方程, 得到了平均场倒向重随机微分方程解的存在唯一性.基于平均场倒向重随机微分方程的解, 给出了一类非局部随机偏微分方程解的概率解释.讨论了平均场倒向重随机系统的最优控制问题, 建立了庞特利亚金型的最大值原理.最后讨论了一个平均场倒向重随机线性二次最优控制问题, 展示了上述最大值原理的应用.     

单自由度摩擦系统离散模型

发展了两种随机离散数学模型:导出了一个以二维平均映射描述的随机模型,并建立了一个概率预报模型.通过实例对不同模型进行了比较,对于平均映射模型,分岔图指出了外噪声对系统性质的影响,通过符号动力学方法分析指出概率预报模型的随机性质.     

动力学系统参数辨识问题最优控制解的理论与方法（II）—随机？…

基于（Ｉ）（《应用数学和力学》,１９９８,２０（２））的内容和随机最优控制理论,本首先介绍了随机动力学系统参数辨识问题最优控制解的概念。然后讨论了建立参数辨识问题ＨＪＢ方程的过程以及参数辨识的算法,最后给出了一个应用实例：解决动力学系统局部非线性参数辨识问题的方法。     

供应链中的MQ(t)/Gj/∞排队群

本文研究由一个非时齐Poisson到达过程驱动的多个MQ(t)／Gj／∞型排队系统．当一个K型到达发生时(K(?){1,…,m}),对每一个j∈K,顾客按批量QjK进入排队系统j,(Q1K,…,QmK)是相依的随机向量．我们导出了联合队长过程和联合输出过程的多变量母函数,并给出各排队系统之间的相关性分析．这些结果为非时齐供应链分析提供了理论依据     

随机激励下四自由度车辆-道路耦合系统动力分析

采用四自由度车辆模型,以 Gauss平稳随机过程模拟路面的不平整度,编制程序得到不同路面等级下的不平整度序列;并将车辆和道路看作一个相互作用的整体系统,建立了车辆 道路耦合系统的动力平衡方程.在对车辆施加随机激励时,为了简化分析过程,避开以往研究中使用随机振动理论求解动轮胎力的复杂性,将得到的路面不平整度序列,直接以向量的形式输入到所建立的动力平衡方程中.基于增量形式的Newmark-β法开发了一个MATLAB程序对该方程进行求解.并对所提出的理论模型进行了试验验证,证明了模型的可靠性.随后,通过一个实例,分析了车速变化、路面等级变化对车辆动荷载系数和车体垂向加速度的影响.最后,对不同路基刚度对车辆振动特性的影响规律进行了探讨.     

Theory and Algorithm of Optimal Control Solution to Dynamic Systme Parameters Identification (

基于文(Ⅰ)(《应用数学和力学》,1998,20(2))的内容和随机最优控制理论,本文首先介绍了随机动力学系统参数辨识问题最优控制解的概念。然后讨论了建立参数辨识问题HJB方程的过程以及参数辨识的算法。最后给出了一个应用实例解决动力学系统局部非线性参数辨识问题的方法。     

动力学系统参数辨识问题最优控制解的理论与方法(Ⅱ)——随机系统参数辨识及应用实例

基于文(Ⅰ)(《应用数学和力学》,1998,20(2))的内容和随机最优控制理论,本文首先介绍了随机动力学系统参数辨识问题最优控制解的概念.然后讨论了建立参数辨识问题HJB方程的过程以及参数辨识的算法.最后给出了一个应用实例:解决动力学系统局部非线性参数辨识问题的方法.     

模型不确定非线性随机系统的鲁棒性能准则设计

对于一类模型不确定非线性随机系统,用耗散性的观点发展了鲁棒性能准则理论.特别地,将确定性非线性系统理论中的耗散性概念引入到模型不确定随机非线性系统中,并以此作为基础来发展H∞理论.在精确模型随机非线性系统H∞基础上,建立了模型不确定系统L2增益和HJI不等式的可解性的关系.由于HJI偏微分方程难于求解,考虑模型参数满足某种适当匹配条件的系统的鲁棒性能准则问题,我们不需要通过求解HJI方程就可以得到此类系统的H∞控制律.     

国民经济第三产业发展预测的等维新息模型

国民经济系统是一个典型的灰色系统,利用灰色系统理论的GM(1 ,1 )模型建立了我国第三产业产值的预测模型,并对2 0 0 1年第三产业增加值进行了预测,进而建立了等维新息模型,得到2 0 0 1—2 0 0 5年第三产业产值的预测值,取得了良好的分析效果.     

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??Recently, big data, could computing and internet of things provide some new information technologies for organization and management of complex systems, and they have caused multifaceted changes on organization framework and operations mechanism of enterprises. Based on this, we first construct a new stochastic model for a big data driven large-scale bike-sharing system, which expresses the important role played by big data, and describes the operations mechanism of the large-scale bike-sharing system, and specifically, the rebalancing of bikes in various stations in terms of trucks. Then, we present a mean-field limit theory, which is applied to analyzing the big data driven large-scale bike-sharing system, including establishing a time-inhomogeneous queueing system by means of the mean field theory, and setting up the mean-field equations through the time-inhomogeneous queueing system; providing an empirical measure process by means of a nonlinear birth-death process, giving algorithms for computing the fixed point in terms of a segmented structural birth-death processes, and computing the average number of bikes in each station; and providing numerical examples to analyze how the steady average number of bikes in each station depends on some key parameters of the bike-sharing system. Using these results, this paper analyzes physical effect of big data on performance of the large-scale bike-sharing. Therefore, this paper gives a promising research direction of stochastic model in the study of large-scale bike-sharing systems.     

On approximating the expected behavior of stochastic epidemiological models applicable to small populations

Many biological systems can be characterized by stochastic multivariate birth-death processes. Such models are often non-linear making the estimation of their mean behavior difficult. Several techniques for approximating the moments of such stochastic systems are proposed. Their use is illustrated by a study of the mean behavior of an epidemiological model applicable to small populations. Computer calculations using the theoretical methods are also given.     

Use of an orthogonal projector for accelerating a queuing problem solver

Overflow queuing models are often analyzed by explicitly solving a large sparse singular linear systems arising from Kolmogorov balance equations. The system is often converted into an eigenvalue problem the dominant eigenvector of which is the desired null vector. In this paper, we convert an overflow queuing problem into an eigen-value problem of size 1/2 of the original. Then we devise an orthogonal projector that enhances its convergence by removing unwanted eigen-components effectively. Numerical result with some suggestion is given at the end.     

Analysis of flexible manufacturing systems with priority scheduling: PMVA

A new methodology for performance analysis of flexible manufacturing systems (FMSs) with priority scheduling is presented. The analytic model developed extends the mean value analysis of closed networks of queues with multiple product types, various non-preemptive priority service disciplines, and with parallel machine stations. Performance measures derived include the expected throughput per product and per station, utilization of machines and transporters, queuing times and queue length measures for various configurations. Extensive numerical calculations have shown that the algorithm used for solving the problem converges rapidly and retains numerical stability for large models. The paper also illustrates the application of the model to a system with a mixture of FCFS and HOL disciplines which gives insights into various priority assignment policies in FMSs. Special attention was given to the problem of scheduling the robot carriers (transporters).     

Partial mean field limits in heterogeneous networks

We investigate systems of interacting stochastic differential equations with two kinds of heterogeneity: one originating from different weights of the linkages, and one concerning their asymptotic relevance when the system becomes large. To capture these effects, we define a partial mean field system, and prove a law of large numbers with explicit bounds on the mean squared error. Furthermore, a large deviation result is established under reasonable assumptions. The theory will be illustrated by several examples: on the one hand, we recover the classical results of chaos propagation for homogeneous systems, and on the other hand, we demonstrate the validity of our assumptions for quite general heterogeneous networks including those arising from preferential attachment random graph models.     

GI/G/1排队系统的几种收敛速度

对随机模型,可以从不同角度研究其稳定性,一种是研究其转移概率函数趋向于平稳分布的速度,即各种遍历性;另一种是研究平稳分布的尾部衰减速度.本文从这两个方面着手,找它们之间的关系,对GI/G/1排队系统,给出等待时间列几何遍历、平稳分布轻尾与服务时间分布轻尾三者等价,l-遍历、平稳分布的尾部(l-1)-阶衰减与服务时间分布的尾部l-阶衰减三者等价,最后证明出等待时间列不是强遍历.     

Minimising average passenger waiting time in personal rapid transit systems

Personal Rapid Transit (PRT) is an emerging urban transport mode. A PRT system operates much like a conventional hackney taxi system, except that the vehicles are driven by computer (no human driver) between stations in a dedicated network of guideways. The world’s first two PRT systems began operating in 2010 and 2011. In both PRT and taxi systems, passengers request immediate service; they do not book ahead. Perfect information about future requests is therefore not available, but statistical information about future requests is available from historical data. If the system does not use this statistical information to position empty vehicles in anticipation of future requests, long passenger waiting times result, which makes the system less attractive to passengers, but using it gives rise to a difficult stochastic optimisation problem. This paper develops three lower bounds on achievable mean passenger waiting time, one based on queuing theory, one based on the static problem, in which it is assumed that perfect information is available, and one based on a Markov Decision Process model. An evaluation of these lower bounds, together with a practical heuristic developed previously, in simulation shows that these lower bounds can often be nearly attained, particularly when the fleet size is large. The results also show that low waiting times and high utilisation can be simultaneously obtained when the fleet size is large, which suggests important economies of scale.     

多服务台并联排队系统的随机模拟

针对多服务台并联排队系统,提出了计算机随机模拟的方法.分别研究了基本和非基本排队系统中,诸如排队人数、排队时间、平稳分布、忙期及其忙期服务人数等各项排队指标的模拟求解方法.通过模拟例子和实际数据分析,显示了本文所提模拟方法的有效性、灵活性和实用性.     

随机能源Logistic反馈控制系统的分岔研究

建立了非线性随机动力模型—带噪声的能源Logistic反馈控制模型,应用随机平均法对随机动力模型进行了简化,得到了一个二维的扩散过程.二维过程满足Ito型随机微分方程,应用不变测度理论研究了该模型的随机分岔.最后,给出了数值实验验证了相应的结论.     

Using mixture of Gamma distributions for Bayesian analysis in an M/G/1 queue with optional second service

The paper proposes Bayesian framework in an M/G/1 queuing system with optional second service. The semi-parametric model based on a finite mixture of Gamma distributions is considered to approximate both the general service and re-service times densities in this queuing system. A Bayesian procedure based on birth-death MCMC methodology is proposed to estimate system parameters, predictive densities and some performance measures related to this queuing system such as stationary system size and waiting time. The approach is illustrated with several numerical examples based on various simulation studies.