随机相位信号检测模型及其在移动通信保密系统中的应用

通过对随机相位信号检测模型的分析 ,介绍了一种基于该检测模型的对于经FSK调制的随机相位信号的检测方法 ,并阐述了该方法在一种蜂窝式移动通信保密系统中的应用     

一类具有吸收效应的HIV-1病毒动力学模型的全局稳定性

研究一类具有吸收效应的HIV-1病毒感染动力学模型．通过构造适当的Lyapunov函数证明当基本再生数〈1时,未感染平衡点是全局渐近稳定的;当基本再生数〉1时,给出病毒感染平衡点全局渐近稳定的充分条件．     

一类具有饱和发生率的SEIS传染病模型全局稳定性研究

研究一类易感者和潜伏者都有新增常数输入,疾病具有饱和发生率的SEIS传染病模型．经计算得到模型的基本再生数,证明当基本再生数〉1时,模型只存在惟一的地方病平衡点的结论,并利用特征方程和Hurwitz判据分析地方病平衡点的局部稳定性,通过采用第二加性复合矩阵理论证明地方病平衡点的全局渐近稳定性．     

Pickands型推广估计量的渐近正态性

极值指数在许多实际领域广泛应用:如组合投资、风险值的计算、预报地震等, 估计极值指数γ以及它的性质的研究近年来成为极值统计理论的基本问题。主要研究极值指数γ的一个估计量:Pickands型推广估计量的渐近正态性质。首先研究由Pickands型推广估计量构成的一随机过程,得到了该过程的渐近分布;然后利用研究结果证明了Pickands 型推广估计量的渐近正态性,得到其渐近方差;最后对提出的Pickands型推广估计的平滑估计量进行了相应研究。     

随机-非概率模型下的船体结构屈服强度可靠性分析

针对在役舰船船体载荷、材料属性的随机特性和实船勘验得出的腐蚀损伤下船体抗力区间特性,提出一种基于随机概率理论和区间非概率理论的随机-非概率可靠性分析模型,分别从区间变量随机化和随机变量区间化两个角度给出该模型的可靠性指标求解方法。基于该模型对某舰船船体结构屈服强度进行可靠性分析,结果表明:该模型既体现了结构变量的客观不确定性又减弱了评估结果的盲目保守性,对于既含有随机变量又含有区间变量的在役舰船的可靠性评估具有很强的适用性。     

采用随机几何工具的终端直通通信接入控制方法

针对现有具备终端直通（Device-to-Device, D2D）功能的蜂窝网络的干扰管理问题，提出一种新型的采用随机几何工具的D2D通信接入控制方法。利用随机过程理论以及随机几何工具建立模型分析邻近基站和D2D通信对蜂窝通信的影响，并推导蜂窝业务接入失败概率表达式。基于该表达式能够计算网络允许的最大D2D用户密度，辅助D2D通信接入控制实现干扰管理。仿真证明基于所提数值计算方法获得的估计结果与蒙特卡洛仿真结果相符，且通过合理限制D2D用户密度和D2D用户发射功率可满足指定的蜂窝业务接入失败概率要求。     

极值指数之推广矩估计量的渐近正态性

首先将推广矩估计量代换为一种新的估计量,然后研究由该估计量引起的一种与尾经验过程有关的函数的弱收敛问题,最后得到与推广矩估计量有关的一随机过程的弱收敛函数,同时也就得到了推广矩估计量的渐近分布,因而证明了推广估计量的渐近正态性.     

一类具有饱和发生率的离散型SIS传染病模型

研究一类具有饱和发生率的离散型SIS传染病模型,得到了模型的基本再生数.通过线性化的方法,运用LaSalle-Lyapunov定理,证明当基本再生数R01时,无病平衡点是全局渐近稳定的;通过迭代的方法,证明当基本再生数R01时,地方病平衡点是全局渐近稳定的.     

一类随机生态-流行病系统的分析

对一类食饵染病的随机食饵-捕食者系统,应用伊藤公式,给出系统均衡解的全局随机渐近稳定的条件,并通过数值模拟对理论结果进行论证.     

教材印量决策模型研究

根据院校教材的需求特点及自印教材的费用发生特点,研究提出了３种适合于院校自印教材印量决策的随机库存模型．所提出的模型实用性强,对节省院校教材保障经费将起到直接的作用．     

The (s,Q) inventory model with Erlang lead time and deterministic demand

We develop a simple, approximately optimal solution to a model with Erlang lead time and deterministic demand. The method is robust to misspecification of the lead time and has good accuracy. We compare our approximate solution to the optimal for the case where we have prior information on the lead‐time distribution, and another where we have no information, except for computer‐generated sample data. It turns out that our solution is as easy as the EOQ's, with an accuracy rate of 99.41% when prior information on the lead‐time distribution is available and 97.54–99.09% when only computer‐generated sample information is available. Apart from supplying the inventory practitioner with an easy heuristic, we gain insights into the efficacy of stochastic lead time models and how these could be used to find the cost and a near‐optimal policy for the general model, where both demand rate and lead time are stochastic. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

Satellite constellation design for forest fire monitoring via a stochastic programing approach

There is significant value in the data collected by satellites during and after a natural disaster. The current operating paradigm in practice is for satellites to passively collect data when they happen to fly over a disaster location. Conversely, this article considers the alternative approach of actively maneuvering satellites to fly directly overhead of the disaster site on a routine basis. Toward this end, we seek to compute a satellite constellation design that minimizes the expected maneuver costs for monitoring an unknown forest fire. In this article, we present a 2‐stage stochastic programing model for this problem as well as a accelerated L‐shaped decomposition approach. A comparison between our approach and the current operating paradigm indicates that our solution provides longer duration data collections and a greater number of data collections. Analysis also shows that our proposed solution is robust over a wide array of scenarios.     

Robust tower location for code division multiple access networks

Designing Code Division Multiple Access networks includes determining optimal locations of radio towers and assigning customer markets to the towers. In this paper, we describe a deterministic model for tower location and a stochastic model to optimize revenue given a set of constructed towers. We integrate these models in a stochastic integer programming problem with simple recourse that optimizes the location of towers under demand uncertainty. We develop algorithms using Benders' reformulation, and we provide computational results. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.     

A stochastic programming approach for chemotherapy appointment scheduling

Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in premedication and infusion durations. In this paper, we formulate a two‐stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real‐life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty.     

Stochastic call center staffing with uncertain arrival,service and abandonment rates: A Bayesian perspective

In this article, we introduce staffing strategies for the Erlang‐A queuing system in call center operations with uncertain arrival, service, and abandonment rates. In doing so, we model the system rates using gamma distributions that create randomness in operating characteristics used in the optimization formulation. We divide the day into discrete time intervals where a simulation based stochastic programming method is used to determine staffing levels. More specifically, we develop a model to select the optimal number of agents required for a given time interval by minimizing an expected cost function, which consists of agent and abandonment (opportunity) costs, while considering the service quality requirements such as the delay probability. The objective function as well as the constraints in our formulation are random variables. The novelty of our approach is to introduce a solution method for the staffing of an operation where all three system rates (arrival, service, and abandonment) are random variables. We illustrate the use of the proposed model using both real and simulated call center data. In addition, we provide solution comparisons across different formulations, consider a dynamic extension, and discuss sensitivity implications of changing constraint upper bounds as well as prior hyper‐parameters. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 460–478, 2016     

A stochastic approximation algorithm to compute bid prices for joint capacity allocation and overbooking over an airline network

In this article, we develop a stochastic approximation algorithm to find good bid price policies for the joint capacity allocation and overbooking problem over an airline network. Our approach is based on visualizing the total expected profit as a function of the bid prices and searching for a good set of bid prices by using the stochastic gradients of the total expected profit function. We show that the total expected profit function that we use is differentiable with respect to the bid prices and derive a simple expression that can be used to compute its stochastic gradients. We show that the iterates of our stochastic approximation algorithm converge to a stationary point of the total expected profit function with probability 1. Our computational experiments indicate that the bid prices computed by our approach perform significantly better than those computed by standard benchmark strategies and the performance of our approach is relatively insensitive to the frequency with which we recompute the bid prices over the planning horizon. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

A stochastic gradual cover location problem

Covering models assume that a point is covered if it is within a certain distance from a facility and not covered beyond that distance. In gradual cover models it is assumed that a point is fully covered within a given distance from a facility, then cover gradually declines, and the point is not covered beyond a larger distance. Gradual cover models address the discontinuity in cover which may not be the correct approach in many situations. In the stochastic gradual cover model presented in this article it is assumed that the short and long distances employed in gradual cover models are random variables. This refinement of gradual cover models provides yet a more realistic depiction of actual behavior in many situations. The maximal cover model based on the new concept is analyzed and the single facility location cover problem in the plane is solved. Computational results illustrating the effectiveness of the solution procedures are presented. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Appointment scheduling at a multidisciplinary outpatient clinic using stochastic programming

The purpose of this paper is to investigate the problem of constructing an appointment template for scheduling patients at a specific type of multidisciplinary outpatient clinic called an integrated practice unit (IPU). The focus is on developing and solving a stochastic optimization model for a back pain IPU in the face of random arrivals, an uncertain patient mix, and variable service times. The deterministic version of the problem is modeled as a mixed integer program with the objective of minimizing a weighted combination of clinic closing time (duration) and total patient waiting time (length of stay). A two‐stage stochastic program is then derived to account for the randomness and the sequential nature of the decisions. Although it was not possible to solve the two‐stage problem for even a limited number of scenarios, the wait‐and‐see (WS) problem was sufficiently tractable to provide a lower bound on the stochastic solution. The introduction of valid inequalities, limiting indices, and the use of special ordered sets helped to speed up the computations. A greedy heuristic was also developed to obtain solutions much more quickly. Out of practical considerations, it was necessary to develop appointment templates with time slots at fixed intervals, which are not available from the WS solution. The first to be derived was the expected value (EV) template that is used to find the expected value of the EV solution (EEV). This solution provides an upper bound on the objective function value of the two‐stage stochastic program. The average gap between the EEV and WS solutions was 18%. Results from extensive computational testing are presented for the EV template and for our adaptation of three other templates found in the literature. Depending on the relative importance of the two objective function metrics, the results demonstrate the trade‐off that exists between them. For the templates investigated, the “closing time” ranged from an average of 235 to 275 minutes for a 300‐minute session, while the corresponding “total patient time in clinic” ranged from 80 to 71 minutes.     

Fashion products with asymmetric sales horizons

In this study, we explore an inventory model for a wholesaler who sells a fashion product through two channels with asymmetric sales horizons. The wholesaler can improve profitability by employing joint procurement and inventory reallocation as a recourse action in response to the dynamics of sales. In this research, a simple stochastic programming model is analyzed to specify the properties of the optimal inventory decisions. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011