Sticky States in Banyan Network Queues and Their Application to Analysis

A finite-buffered banyan network analysis technique designed to model networks at high traffic loads is presented. The technique specially models two states of the network queues: queue empty and queue congested (roughly, zero or one slots free). A congested queue tends to stay congested because packets bound for the queue accumulate in the previous stage. The expected duration of this state is computed using a probabilistic model of a switching module feeding the congested queue. A technique for finding a lower arrival rate to an empty queue is also described. The queues themselves are modeled using independent Markov chains with an additional congested state added. The new analysis technique is novel in its modeling the higher arrival rate to a congested queue and a lower arrival rate to an empty queue. Comparison of queue state distributions obtained with the analysis and simulation shows that an important feature of congestion is modeled.     

Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach

This article investigates metamodeling opportunities in buffer allocation and performance modeling in asynchronous assembly systems (AAS). Practical challenges to properly design these complex systems are emphasized. A critical review of various approaches in modeling and evaluation of assembly systems reported in the recently published literature, with a special emphasis on the buffer allocation problems, is given. Various applications of artificial intelligence techniques on manufacturing systems problems, particularly those related to artificial neural networks, are also reviewed. Advantages and the drawbacks of the metamodeling approach are discussed. In this context, a metamodeling application on AAS buffer design/performance modeling problems in an attempt to extend the application domain of metamodeling approach to manufacturing/assembly systems is presented. An artificial neural network (ANN) metamodel is developed for a simulation model of an AAS. The ANN and regression metamodels for each AAS are compared with respect to their deviations from the simulation results. The analysis shows that the ANN metamodels can successfully be used to model of AASs. Consequently, one concludes that practising engineers involved in assembly system design can potentially benefit from the advantages of the metamodeling approach.     

基于多模型的能源组件维修性建模与仿真研究

某激光装置能源组件是其关键组件,该组件具有随机劣化失效特点,其部件失效类型多,维修类型和维修策略也比较复杂,现有的基于单一模型和解析方法的系统维修性建模与分析方法难以解决其维修性设计与分析问题。本文采用多模型集成的维修性建模与仿真方法,解决能源组件维修性建模与分析问题。其中,采用故障树模型描述能源组件系统级可靠性,采用SPNAT模型描述各部件的失效过程和维修决策,并基于"Join/Replicate"操作进行模型集成,通过报酬结构定义组件的维修性指标。仿真结果表明,基于多模型集成的维修性建模与仿真方法,是解决能源组件这样的复杂产品维修方案设计与分析的有效途径。     

A new approach to the queue length and waiting time of BMAP/G/1 queues

We apply the factorization principle to derive the generating function of the queue length and the vector Laplace–Stieltjes transform of the waiting time of a BMAP/G/1 queue. The mean performance measures are provided with a computational experience.Scope and purposeThe classical method of obtaining the queue length and waiting time distributions of BMAP/G/1 queues starts with the analysis of the imbedded Markov renewal process at departure epochs. This method is intricate and time consuming when the idle period process is complicated. In this paper, we demonstrate that the factorization property can be applied efficiently and effectively to derive the queue length distributions of BMAP/G/1 queueing systems by avoiding the conventional standard procedures. The approach demonstrated in this paper can be applied to the analysis of many other BMAP/G/1 queueing systems with higher behavioral complexities.     

Computational Experiments for Complex Social Systems: Experiment Design and Generative Explanation

Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS). In this context, computational experiments method has emerged as a novel approach for the design, analysis, management, control, and integration of CPSS, which can realize the causal analysis of complex systems by means of “algorithmization” of “counterfactuals”. However,because CPSS involve human and social factors(e.g., autonomy,initiative, and so...     

Adaptive neural queue management for TCP networks

Active Queue Management (AQM) is a proven strategy to efficiently maintain queues and ensure high utilization of Transmission Control Protocol (TCP) network resources. The fundamental mechanism is to manage incoming packet rates at a router to prevent incipient network congestion. In this paper, we present an efficient neural network AQM system as a queue controller. The recurrent neural network has a Multi-layer Perceptron-Infinite Impulse Response (MLP-IIR) structure. Three distinct neural AQMs are trained under different network scenarios involving traffic levels. Selecting one of three neural AQMs is based on posterior probability history of traffic level. In addition, we investigate stochastic modeling of the network dynamics by a Dynamic Bayesian Network (DBN). This model allows implementation of a predictive AQM system in which queue dynamics are predicted and used for error prediction via online DBN estimation. Our AQM method is evaluated through simulation experiments both using an Ordinary Differential Equation (ODE) network model and using OPNET^©. The simulation results demonstrate that our adaptive neural AQM outperforms Random Early Detection (RED) and Proportional-Integral-Derivative (PID) based AQM.     

Decomposition of general tandem queueing networks with MMPP input

For tandem queueing networks with generally distributed service times, decomposition often is the only feasible solution method besides simulation. The network is partitioned into individual nodes which are analyzed in isolation. In most existing decomposition algorithms for continuous-time networks, the output of a queue is usually approximated as a renewal process, which becomes the arrival process to the next queue. In this paper, the internal traffic processes are described as semi-Markov processes (SMPs) and Markov modulated Poisson processes (MMPPs). Thus, correlations in the traffic streams, which are known to have a considerable impact on performance, are taken into account to some extent. A two-state MMPP, which arises frequently in communications modeling, serves as input to the first queue of the tandem network. Furthermore, the single nodes may have infinite or finite buffers. Customers who arrive at a full buffer will get lost.

In principle, the analysis of an individual queue as an MMPP/G/1(/K) system delivers a wide range of performance measures. For different examples of tandem networks, stationary mean queue lengths at arbitrary time are compared to simulation data. The relative errors of the results, which are computed promptly by the decomposition component of the tool TimeNET, remain within a reasonable range.     

Design of practical optimum JIT systems by integration of computer simulation and analysis of variance

This study introduces a framework for re-design of manufacturing systems into practical optimum just-in-time systems by integration of computer simulation and analysis of variance. The conventional JIT approach is mostly applicable to static production systems and the dynamic production systems require a more practical integrated JIT approach. In addition, the re-design of existing dynamic systems into just-in-time systems must follow a practical path, which can be a cumbersome task. This means, a unique practical optimum just-in-time system that considers system's limitations and its dynamic behavior must be designed. To achieve the objective of this study, first, the actual system must be totally modeled and simulated. Second, the integrated simulation model is tested and validated by analysis of variance. Third, the optimum (most fitted) JIT design is developed and tested by modeling actual system's limitations and its dynamic behavior. The framework is applied and tested for an auto production line and a heavy rolling mill workshop.     

An interface for hierarchical modeling in object-oriented simulation

A graphical interface for a new simulation development environment is presented. This interface is described in relation to information hiding and visual layers. In the simulation development environment distinct types of entities may easily be modeled. This results in simulation models which are good representations of real world systems. We introduce two nodes which facilitate discrete event simulation modeling. Sequences of tasks may be assigned to various entities in a queue/server node. A multi-queues/multi-servers node permits the modeler to formulate server selection rules and queue selection rules for each type of entity. Servers may also have a queue selection rule.     

Short-term traffic flow forecasting: parametric and nonparametric approaches via emotional temporal difference learning

Information signal from real case and natural complex dynamical systems such as traffic flow are usually specified by irregular motions. Chaotic nonlinear dynamics approach is now the most powerful tool for scientists to deal with complexities in real cases, and neural networks and neuro-fuzzy models are widely used for their capabilities in nonlinear modeling of chaotic systems more than the traditional methods. As mentioned, the traffic flow conditions caused the forecasting values of traffic flow to lack robustness and accuracy. In this paper, the traffic flow forecasting is analyzed with emotional concepts and multi-agent systems (MASs) points of view as a new method in this field. The findings enabled the researchers to develop a newly object-oriented method of forecasting traffic flow. Its architecture is based on a temporal difference (TD) Q-learning with a neuro-fuzzy structure, which is the nonparametric approach. The performance of TD Q-learning is improved by emotional learning. The proposed method on the present conditions and the action of the system according to the criteria could forecast traffic signals so that the objectives are reached in minimum time. The ability of presented learning algorithm to prospect gains from future actions and obtain rewards from its past experiences allows emotional TD Q-learning algorithm to improve its decisions for the best possible actions. In addition, to study in a more practical situation, the neuro-fuzzy behaviors could be modeled by MAS. The proposed method (intelligent/nonparametric approach) is compared by parametric approach, autoregressive integrated moving average (ARIMA) method, which is implemented by multi-layer perceptron neural networks and called ARIMANN. Here, the ARIMANN is updated by backpropagation and temporal difference backpropagation for the first time. The simulation results revealed that the studied forecaster could discover the optimal forecasting by means of the Q-learning algorithm. Difficult to handle through parametric and classic methods, the real traffic flow signals used for fitting the algorithms is obtained from a two-lane street I-494 in Minnesota City.     

An EM‐circuit‐ANN co‐modeling approach for RF module design

A new co‐modeling technique dedicated to RF module design and optimization is described. This approach is based on combined EM, circuit, and artificial neural network simulations. Generic segments are parameterized in geometry, physical properties and frequency by analytical models and are integrated into commercial circuit software. This co‐modeling combines speed and flexibility of circuit simulators and accuracy of EM‐based simulators. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.     

Intelligent modeling and control for nonlinear systems with rate-dependent hysteresis

A new modeling approach for nonlinear systems with rate-dependent hysteresis is proposed. The approach is used for the modeling of the giant magnetostrictive actuator, which has the rate-dependent nonlinear property. The models built are simpler than the existed approaches. Compared with the experiment result, the model built can well describe the hysteresis nonlinear of the actuator for input signals with complex frequency. An adaptive direct inverse control approach is proposed based on the fuzzy tree model and inverse learning and special learning that are used in neural network broadly. In this approach, the inverse model of the plant is identified to be the initial controller firstly. Then, the inverse model is connected with the plant in series and the linear parameters of the controller are adjusted using the least mean square algorithm by on-line manner. The direct inverse control approach based on the fuzzy tree model is applied on the tracing control of the actuator by simulation. The simulation results show the correctness of the approach. Supported by the National Natural Science Foundation of China (Grant No. 60534020), the National Basic Research Program of China (Grant No. G2002cb312205-04), the Research Fund for the Doctoral Program of Higher Education (Grant No. 20070006060), and the Key Subject Foundation of Beijing (Grant Nos. XK100060526, XK100060422)     

Distributed Computation Multi-agent System

This article addresses a formal model of a distributed computation multi-agent system. This model has evolved from the experimental research on using multi-agent systems as a ground for developing fuzzy cognitive maps. The main paper contribution is a distributed computation multi-agent system definition and mathematical formalization based on automata theory. This mathematical formalization is tested by developing distributed computation multi-agent systems for fuzzy cognitive maps and artificial neural networks – two typical distributed computation systems. Fuzzy cognitive maps are distributed computation systems used for qualitative modeling and behavior simulation, while artificial neural networks are used for modeling and simulating complex systems by creating a non-linear statistical data model. An artificial neural network encapsulates in its structure data patterns that are hidden in the data used to create the network. Both of these systems are well suited for formal model testing. We have used evolutionary incremental development as an agent design method which has shown to be a good approach to develop multi-agent systems according to the formal model of a distributed computation multi-agent system.     

Simulation optimization of an emergency department by modeling human errors

Emergency department (ED) is considered as one of the most critical elements in healthcare systems. This paper describes a simulation optimization of an emergency department in a general hospital in Iran by modeling human error. Human error is defined as combination of committed errors by nurses and technicians. In this study three types of errors are evaluated including repeated venipuncture, unsafe transportation and sampling errors. These errors are considered in simulation model. Seventy appropriate scenarios approved by experts in ED are defined to assess various alternatives. Scenarios are examined and evaluated by stochastic data envelopment analysis (SDEA). Then, the optimum scenarios are identified. In this study, expense cost, number of nurses and physicians are considered as inputs whilst patient duration, queue length, and number of three different committed errors are considered as outputs of SDEA and DEA model. The results show that addition of nurse and physician in ED would reduce the human errors, patient duration and queue length. Then, proposed approach is validated and verified by statistical test. Finally, queue length is identified as the most important indicator through sensitivity analysis. To the best of our knowledge this is the first paper that examines human errors in a general hospital by simulation, DEA, and SDEA approaches. Moreover, human errors are classified based on skill, rule, and knowledge (SRK) based behavior to help decision makers in order to improve the quality of care for this particular ED.     

基于可视化计算的图形建模及参数寻优环境的设计与实现

研究了复杂非线性系统参数优化环境的可视化建模技术及软件实现问题．应用先进的仿真技术,采用面向对象和结构化设计方法,通过通用软件实现了复杂系统参数自动寻优环境的高度可视化的一体化;与基于常规高级和谐设计语言的参数寻优方法相比,该方法不仅不用传统程序代码对算法编程,而且可方便地对系统进行多参数自动迭代寻优试验及进行智能化分析．     

Violation Probability in Processor-Sharing Queues

Processor-sharing queues are often used to model file transmission in networks. While sojourn time is a common performance metric in the queueing literature, average transmission rate is the more commonly discussed metric in the networking literature. Whereas much is known about sojourn times, there is little known about the average service rate experienced by jobs in processor-sharing queues. We focus here upon performance requirements in the form of an upper bound on the probability of failing to achieve a specified minimum transmission rate or a specified minimum average rate. For an M/G/l processor-sharing queue, we give a closed-form expression for this violation probability. We derive closed-form expressions for the marginal service rate with respect to the violation probability and to the minimum transmission rate, and characterize when each is binding. We then consider the effect of using connection access control by modeling an M/G/l/K processor-sharing queue, and discuss the relationship between queue service rate, queue limit, violation probability, and blocking probability. Finally, we consider a two-class discriminatory processor-sharing queue, and discuss what combinations of class weighting and service rate can be used to achieve specified minimum rate violation probabilities for both classes.     

基于WebGIS的海上溢油应急信息系统的构建

传统的基于GIS的溢油信息系统只能在单机或者局域网内使用，应用范围小且平台独立性不强。为了在溢油应急反应决策中提供更便捷、易于获取的GIS应用和应急信息，该文提出了构建基于网络地理信息系统(WebGIS)的海上溢油应急信息系统。在设计系统的体系结构和功能基础上，利用数值建模技术开发了溢油动态模拟模块，建立了应急信息数据库，运用GIS、动态Web及数据库技术开发了系统服务器端和客户端应用程序，并探讨了系统集成以及在WebGIS上实现数值模拟结果动态可视化表达的方法。初步研究结果表明，结合WebGIS、数据库、数值建模的综合方法提供了溢油事故应急所需的有效解决方案。     

Development of efficient and cost-effective distributed hydrological modeling tool MWEasyDHM based on open-source MapWindow GIS

Many regions are still threatened with frequent floods and water resource shortage problems in China. Consequently, the task of reproducing and predicting the hydrological process in watersheds is hard and unavoidable for reducing the risks of damage and loss. Thus, it is necessary to develop an efficient and cost-effective hydrological tool in China as many areas should be modeled. Currently, developed hydrological tools such as Mike SHE and ArcSWAT (soil and water assessment tool based on ArcGIS) show significant power in improving the precision of hydrological modeling in China by considering spatial variability both in land cover and in soil type. However, adopting developed commercial tools in such a large developing country comes at a high cost. Commercial modeling tools usually contain large numbers of formulas, complicated data formats, and many preprocessing or postprocessing steps that may make it difficult for the user to carry out simulation, thus lowering the efficiency of the modeling process. Besides, commercial hydrological models usually cannot be modified or improved to be suitable for some special hydrological conditions in China. Some other hydrological models are open source, but integrated into commercial GIS systems. Therefore, by integrating hydrological simulation code EasyDHM, a hydrological simulation tool named MWEasyDHM was developed based on open-source MapWindow GIS, the purpose of which is to establish the first open-source GIS-based distributed hydrological model tool in China by integrating modules of preprocessing, model computation, parameter estimation, result display, and analysis. MWEasyDHM provides users with a friendly manipulating MapWindow GIS interface, selectable multifunctional hydrological processing modules, and, more importantly, an efficient and cost-effective hydrological simulation tool. The general construction of MWEasyDHM consists of four major parts: (1) a general GIS module for hydrological analysis, (2) a preprocessing module for modeling inputs, (3) a model calibration module, and (4) a postprocessing module. The general GIS module for hydrological analysis is developed on the basis of totally open-source GIS software, MapWindow, which contains basic GIS functions. The preprocessing module is made up of three submodules including a DEM-based submodule for hydrological analysis, a submodule for default parameter calculation, and a submodule for the spatial interpolation of meteorological data. The calibration module contains parallel computation, real-time computation, and visualization. The postprocessing module includes model calibration and model results spatial visualization using tabular form and spatial grids. MWEasyDHM makes it possible for efficient modeling and calibration of EasyDHM, and promises further development of cost-effective applications in various watersheds.     

A polynomial factorization approach to the discrete time GI/G/1/(N) queue size distribution

The queue of a single server is considered with independent and identically distributed interarrivai and service times and an infinite (GI/G/1) or finite (GI/G/1/N) waiting room. The queue discipline is non-preemptive and independent of the service times.

A discrete time version of the system is analyzed, using a two-component state model at the arrival and departure instants of customers. The equilibrium equations are solved by a polynomial factorization method. The steady state distribution of the queue size is then represented as a linear combination of geometrical series, whose parameters are evaluated by closed formulae depending on the roots of a characteristic polynomial.

Considering modified boundary constraints, systems with finite waiting room or with an exceptional first service in each busy period are included.     

基于图形建模的可视化群仿真技术研究

本文研究基于图形建模技术的可视化群仿真环境的软件实现问题,针对复杂动态参数摄动系统,应用先进仿真技术,用通用软件实现了复杂系统在参数摄动条件下的整个群仿真软件环境的高度可视化和一体化。