User-Based Discrete-Time Queuing Analysis for Opportunistic Spectrum Access in Cognitive Radio Networks

In cognitive radio networks, the spectrum utilization can be improved by cognitive users opportunistically using the idle channels licensed to the primary users. However, the new arrived cognitive users may not be able to use the channel immediately since the channel usage state is random. This will impose additional time delay for the cognitive users. Excessive waiting delay can make cognitive users miss the spectrum access chances. In this paper, a discrete-time Markov queuing model from a macro point of view is provided. Through the matrix-geometric solution theory, the average sojourn time for cognitive users in the steady state before accessing the spectrum is obtained. Given the tolerant delay of cognitive users, the macro-based throughput is derived and an access control mechanism is proposed. The numerical results show the effects of service completion probability on average sojourn time and throughput. It is confirmed that the throughput can be obviously improved by using the proposed access control mechanism. Finally, the performance evaluations based on users are compared to that based on data packets.

基于BIM-Queuing Theory的超高层施工电梯配置方法研究

随着中国经济的高速发展,相对发达的城市都在建设超高层建筑并将其作为自己的地标性建筑。由于超高层建筑施工过程中工作面有限,投入资金量大,加快施工进度从而降低施工成本显得尤为重要。目前施工单位的施工管理水平有限,进度与成本两个目标的权衡变得很困难,施工单位在布置施工电梯时多是根据经验进行粗略估算,这可能会造成巨大的资源浪费。通过搜集和研究当前施工电梯在超高层建筑中的应用现状,应用BIM技术与排队论,结合施工电梯的实际应用状况,对施工电梯布置方法进行研究,力求为施工单位在配置施工电梯时提供一种新的思路,加强施工单位在施工过程中的管理水平,从而提高经济效益。

基于Queuing Theory的收费服务系统模型优化及仿真

针对目前诸多形态的收费服务系统运行效率不高的现状,通过对收费服务系统排队规定和措施的研究分析,基于排队论对收费服务系统数学模型进行优化,并以超市为实证对象,对优化模型进行仿真。根据仿真结果,所提出的收费服务系统优化模型能在一定程度上减少排队时间或者是在排队时间既定的情况下,减少收费资源的投入。

Application of the Queuing Theory in Characterizing and Optimizing the Passenger Flow at the Airport Security

This paper presents mathematics models that describe and optimize the passenger flow at the airport security checkpoints by applying the queuing theory. Firstly, a Poisson process is used to estimate the flow of passengers waiting for going through the security. Then, the Poisson distribution is combined with a multiple M/M/s model. Following that, an arrival model (passengers’ arriving at the checkpoints preparing for security examination and departure) with Gumbel extreme value estimation is described that predicts the busiest time in the busiest airport. Real case data collected from several major airports worldwide is used for creating a hybrid Poisson model to generate the simulation of passenger volume. At last, Markov Chain theory is applied to the analysis to randomly simulate the flow of enplaned passengers again, and the results of these two simulations are compared and discussed, revealing that the hybrid Poisson model is the more accurate one. After successfully characterizing the passenger flow mathematically, two methods for optimizing the passenger flow are then provided in two different respects: one is bypassing passengers and creating an express pass;while the other one promotes Pre-Check service application.

Using Poisson Modeling and Queuing Theory to Optimize Staffing and Decrease Patient Wait Time in the Emergency Department

Introduction: Studies have shown Emergency Department (ED) crowding contributes to reduced quality of patient care, delays in starting treatments, and increased number of patients leaving without being seen. This analysis shows how to theoretically and optimally align staffing to demand. Methods: The ED value stream was identified and mapped. Patients were stratified into three resource-driven care flow cells based on the severity indices. Time observations were conducted for each of the key care team members and the manual cycle times and service rate were calculated and stratified by severity indices. Using X32 Healthcare’s Online Staffing Optimization (OSO) tool, staffing inefficiencies were identified and an optimal schedule was created for each provider group. Results: Lower Severity Indices (higher acuity patient) led to longer times for providers, nurses, patient care assistants, and clerks. The patient length of stay varied from under one hour to over five hours. The flow of patients varied considerably over the 24 hours’ period but was similar by day of the week. Using flow data, we showed that we needed more nurses, more care team members during peak times of patient flow. Eight hour shifts would allow better flexibility. We showed that the additional salary hours added to the budget would be made up for by increased revenue recognized by decreasing the number of patients who leave without being seen. Conclusion: If implemented, these changes will improve ED flow by using lean tools and principles, ultimately leading to timeliness of care, reduced waits, and improved patient experience.

Hospital Bed Allocation Strategy Based on Queuing Theory during the COVID-19 Epidemic

During the current epidemic,it is necessary to ensure the rehabilitation treatment of children with serious illness.At the same time,however,it is essential to effectively prevent cross-infection and prevent infections from occurring within the hospital setting.To resolve this contradiction,the rehabilitation department of Nanjing Children’s Hospital adjusted its bed allocation based on the queuing model,with reference to the regional source and classification of the children’s conditions in the rehabilitation department ward.The original triple rooms were transformed into a double room to enable the treatment of severely sick children coming from other places.A M/G/2 queuing model with priority was also applied to analyze the state of patient admissions.Moreover,patients in Nanjing were also classified into mild and severe cases.The M/M/1 queuing model with priority was used for analysis of this situation,so that severely ill children could be treated in time while patients with mild symptoms could be treated at home.This approach not only eases the bed tension in the ward,but also provides suitable conditions for controlling cross-infection.

Performance analysis of blockchain for civil aviation business data based on M/G/1 queuing theory

An Ethereum blockchain based on proof of stake ( PoS) consensus mechanism is used to achieve the data sharing within the civil aviation service platform for both airport group management and passengers. Considering the Gas consumption of Ethereum, the dynamic batch-service capacity constraint by the Block Gas Limit and the priority mechanism depending on the different Gas Price of transactions, M/ G/1 queuing theory with batch-service is used to construct the service model of transactions confirmation process in the proposed blockchain system, where the effects of transactions arrival rate, block capacity, service rate and number of nodes on the average confirmation time of transactions with different priority are analyzed, and eventually a performance analysis model of blockchain for civil aviation business data is proposed. The simulation results prove the usability and accuracy of the model, which can provide both theoretical basis for data sharing of civil aviation using Ethereum blockchain and the further optimization of transactions confirmation time.

Optimal gateway deployment under different queuing mechanisms in smart grid

By optimizing the network topology, this paper proposes a newmethod of queuing theory clustering algorithm based on dynamic programming in a home energy management system（ HEMS）. First, the total cost of the HEMS system is divided into two parts, the gateway installation cost and the data transmission cost. Secondly, through comparing two kinds of different queuing theories, the cost problem of the HEMS is converted into the problem of gateway deployment. Finally, a machine-to-machine（ M2M） gateway configuration scheme is designed to minimize the cost of the system. Simulation results showthat the cost of the HEMS system mainly comes from the installation cost of the gateways when the gateway buffer space is large enough. If the gateway buffer space is limited, the proposed queue algorithm can effectively achieve optimal gateway setting while maintaining the minimal cost of the HEMS at desired levels through marginal analyses and the properties of cost minimization.

Queuing Management in Wireless Sensor Networks for QoS Measurement

Data transmission in multimedia WSNs are required high bandwidth and reliable transfer because of large amount of data size. However, some applications of WSNs are required high quality. In this context, main factor in quality of services (QoS) metrics in WSNs becomes longevity of the network and high quality. In sensor nodes, choosing relevant transceiver and microcontroller components plays important role in assembling sensor devices, in which data controls should be designed so that packet loss is minimized. Available QoS metrics based on queuing/buffer management in wired and other wireless networks don’t applicable in WSNs because of its unique characteristics. In this paper the simplest model of QoS model-bufferless system were proposed. In the proposed model, measurement of the probability of blocking of the arrival packets was suggested by using queuing theory in order to increase QoS. Given probability of blocking (PB) illustrates clear picture how system specification should be chosen so that blocking state would be minimized.

Coordinated scheduling model for intermodal transit hubs based on GI/M^K/1 queuing system

Coordinated scheduling of multimode plays a pivotal role in the rapid gathering and dissipating of passengers in transport hubs. Based on the survey data, the whole-day reaching time distribution at transfer points of passengers from the dominant mode to the connecting mode was achieved. A GI/M K/1 bulk service queuing system was constituted by putting the passengers' reaching time distribution as the input and the connecting mode as the service institution. Through queuing theory, the relationship between average queuing length under steady-state and headway of the connecting mode was achieved. By putting the minimum total cost of system as optimization objective, the headway as decision variable, a coordinated scheduling model of multimode in intermodal transit hubs was established. At last, a dynamic scheduling strategy was generated to cope with the unexpected changes of the dominant mode. The instance analysis indicates that this model can significantly reduce passengers' queuing time by approximately 17% with no apparently increase in departure frequency, which provides a useful solution for the coordinated scheduling of different transport modes in hubs.

Performance Analysis of Two Priority Queuing Systems in Tandem

In this paper, we consider a tandem of two head-of-line (HOL) non-preemptive priority queuing systems, each with a single server and a deterministic service-time. Two classes of traffic are considered, namely high priority and low priority traffic. By means of a generating function approach, we present a technique to derive closed-form expressions for the mean buffer occupancy at each node and mean delay. Finally, we illustrate our solution technique with some numerical examples, whereby we illustrate the starvation impact of the HOL priority scheduling discipline on the performance of the low-priority traffic stream. Our research highlights the important fact that the unfairness of the HOL priority scheduling becomes even more noticeable at the network level. Thus this priority mechanism should be used with caution.

Research and Simulate the Queuing Systems Based on the Markov Chain

This paper shows the researches of the queuing theory,especially the Markov Chainand the Poison Process,and implements the efficient way of designing the queuing system.Then simulating the queuing systems based on the“Simply package”in the“Python”programming language.And the paper would discuss and analyze the differences between different queuing systems.

新能源汽车电池回收网点竞争选址模型及算法

针对考虑排队论的新能源汽车电池回收网点竞争设施选址问题,提出一种改进的人类学习优化(IHLO)算法。首先,构建包含排队时间约束、容量约束和门槛约束等条件的新能源汽车电池回收网点竞争设施选址模型;然后,考虑到该问题属于NP-hard问题,针对人类学习优化(HLO)算法前期收敛速度较慢、寻优精度不够高、求解稳定性不够高的不足,通过引入精英种群反向学习策略、团队互助学习算子和调和参数自适应策略提出IHLO算法;最后,以上海市和长江三角洲为例进行数值实验,并将IHLO算法和改进二进制灰狼(IBGWO)算法、改进二进制粒子群(IBPSO)算法、HLO算法和融合学习心理学的人类学习优化(LPHLO)算法进行比较。大、中、小三种不同规模的实验结果表明,IHLO算法在15个指标中的14个指标上表现最优,IHLO算法比IBGWO算法求解精度至少提高了0.13%,求解稳定性至少提高了10.05%,求解速度至少提高了17.48%。所提算法具有较高的计算精度和优化速度,可有效解决竞争设施选址问题。

拥堵收费下考虑船主心理期望成本的过坝策略研究

本文针对内河航运中水利枢纽通航拥堵问题,在拥堵收费下引入心理期望成本,以探寻面向双过坝模式(通航建筑物过坝和翻坝转运)的优化策略。首先,应用非抢占式M/M/1排队系统,构建了以综合过坝成本为参考点的决策模型,提出了三种情形(无心理期望成本、通航建筑物过坝的心理期望成本、双过坝模式的心理期望成本)下的船舶过坝优化策略,并对比分析了通航建筑物过坝比率、船舶平均等待时间和过坝系统总成本;然后,引入对翻坝转运的补贴策略,通过调整心理期望成本水平,来影响船舶过坝策略选择;最后,以三峡工程为例,验证了上述模型的有效性。结果表明:在拥堵收费下,考虑船主心理期望成本,可以促进过坝方式的转移,降低船舶过坝平均等待时间,且双过坝模式中均考虑该因素更有利于提升通航效率;同时,实施翻坝转运补贴策略,能充分发挥心理期望成本的调节作用,从而有效缓解水利枢纽通航拥堵问题。

卫星物联网中面向多类型任务的计算卸载策略

边缘计算与卫星物联网相融合,可以将本地物联网设备产生的任务在靠近边缘端进行处理,极大地缓解了本地物联网设备的计算压力。然而,鉴于卫星物联网中计算任务具有多样性,由于任务特性不同需求也不同,针对每一种任务类型部署一套计算卸载策略会导致计算资源的浪费。此外,由于卫星物联网中计算任务产生具有随机性,如果仅考虑系统短期优化会导致计算设备的计算资源利用不足,进一步导致任务处理时延的增加。为解决上述问题,提出了一种基于DQN(deep Q-network)多类型任务计算卸载策略,该策略引入排队论以最小化长期系统任务处理平均时延,并且为提升模型训练结果,在训练阶段重新设计了探索机制。最后,大量仿真结果表明,与其他卸载策略相比,所提策略能有效降低系统任务处理的平均时延。

基于DHMM-ESUM的露天矿运输系统车铲比优化研究

在露天矿生产中,如何利用现有的资源条件对电铲与卡车进行合理配比,充分发挥设备的效率,是提高矿山企业生产效益的关键。以安家岭露天矿生产系统收集的数据为基础,首先运用排队理论构建以电铲为采掘中心,卡车为运输工具的闭合排队网络模型,将运输系统分为4个排队子系统,分析各项服务时间的概率分布;基于离散隐马尔可夫模型(discrete hidden Markov model,DHMM)与拓展求和(extended summation,ESUM)算法分析系统作业性能,获取电铲设备的运行状态和班次生产能力。然后,应用蒙特卡洛法建立随机动态规划模型,以产量期望值最大化为目标对露天矿的车铲比进行优化。最后,对生产系统进行仿真建模分析,结果验证了基于DHMM-ESUM对车铲比进行优化的有效性和准确性。

城市轨道交通预约进站配额优化研究

通勤时段客流激增,城市轨道交通车站过度拥挤,安全风险较大.除站外限流外,采用线上预约、快速进站制度可以使乘客合理规划出行时间,提升系统效能.作为1种新解决方案,预约进站制度中,预约时段选择以及预约配额设置仍缺乏科学合理的依据.为此,本研究基于智能卡数据分析通勤高峰时期乘客出行特征,确定预约时段;通过实地调研获取进站时间特征,利用排队论以最小化非预约乘客站外排队时间为目标的预约配额优化模型.以北京地铁沙河站为例,分别采用数值算法以及仿真模型实现并验证了提出模型的适用性,为城市轨道交通预约进站制度的推广应用提供科学的理论指导.

基于排队论的某食品零售店收银系统优化研究

对于某食品零售店高峰期排队结账时间较长的问题,提出两种针对性的解决方案。通过选取节假日客流高峰时间段的数据进行现场采集,基于运筹学中的排队理论对该食品零售门店高峰期两小时的实际客流进行统计,运用python建立M/G/2模型,利用真实数据进行模拟仿真,根据仿真结果提出两种可行的优化方案,并对优化方案进行仿真计算。计算结果表明,不论是收银系统的改变还是排队模式的改变,都能够更加充分地利用环境资源、硬件资源和人力资源,能帮助该类门店缓解高峰时期客流拥堵的现象,由此见得,优化后的系统相较于优化前更加高效合理。这样门店就能够将更好的服务提供给消费者,从而提高消费者的满意程度,为企业带来更大的效益,门店可以根据实际情况安排合适的优化方式。

自动化立体车库的库位布局和配置优化

为提高自动化立体车库的服务效率,以太原市某一平面移动式自动化立体车库为例,基于排队论模型,对其系统的相关配置进行优化。首先,基于M/M/1的排队模型,分析了顾客平均逗留时间与出入口数量的关系;然后基于M/M/C的排队模型,结合Matlab数值仿真结果,对四点同步提升机构、搬运台车的数量匹配进行研究,并对库位布局进行优化。实现对系统内相关设备的统一调度、管理和协调。