Discrete Event Simulations

Supply chain management (SCM) is the process of integrating suppliers, manufacturers, warehouses, and retailers in the supply chain, so that goods are produced and delivered in the right quantities, and at the right time, while minimizing costs as well as satisfying customer's requirements (Cooper et al., 1997). Managing the entire supply chain is a key factor for a successful business. World-class organizations now realize that non-integrated manufacturing processes, non-integrated distribution processes and poor relationships with suppliers and customers are inadequate for their success (Chang & Makatsoris, 2001). A typical supply chain consists of a number of organizations; it starts with suppliers, who provide raw materials to manufacturers, which manufacture products and keep the manufactured goods in the warehouses. Then, products are sent to distribution centres and shipped to retailers. Due to the complexities of supply chain and to the numerous players involved in the product flow, supply chain design is a relevant topic for developing an optimal platform for an effective SCM (Yan et al., 2003). The proper design of a supply chain encompasses a set of decisions, embracing both strategic and tactical levels. Examples of such decisions concern number of echelons required and number of facilities per echelon, reorder policy to be adopted by echelons, assignment of each market region to one or more locations, selection of suppliers for sub-assemblies, components and materials (Chopra & Meindl, 2004; Hammami et al., 2008). At the same time, there are several expected benefits from a proper supply chain configuration, which include better coordination of material and capacity, reduced order cycle time, decrease in inventory cost and bullwhip effect (Lee et al., 2004), transport optimization, and increased customer responsiveness (Chang & Makatsoris, 2001). An analysis of the recent literature shows that the problem of optimizing supply chain design is approached by researchers either with linear programming (operational research) models or through simulation models. Linear programming models are exploited with several aims, which encompass determining the number, location, and capacity of DCs, minimizing the total cost, or maximizing the profit of the supply chain (e.g.,

This paper presents a tool for dynamic simulation of radio resources in WiMAX IEEE802.16e-2005 networks. The developed tool provides an integrated environment for defining user scenario characteristics, BS planning, simulation and analysis. For implementing the different mechanisms of WiMAX radio technology, the simulation tool kernel is defined on a system-level, dynamic, stochastic and a discrete event driven simulation model. With the purpose of validating the simulation model, two phases of testing were considered: coverage and capacity. The results confirm the developed tool validity and its good functionality in what scenario definition, simulation and analysis are concerned.

The need for information security is self-evident. The pervasiveness of this critical topic requires primarily risk assessment and management through quantitative means. To do an assessment, repeated security probes, surveys, and input data measurements must be taken and verified toward the goal of risk mitigation. One can evaluate risk using a probabilistically accurate statistical estimation scheme in a quantitative security meter (SM) model that mimics the events of the breach of security. An empirical study is presented and verified by discrete-event and Monte Carlo simulations. The design improves as more data are collected and updated. Practical aspects of the SM are presented with a realworld example and a risk-management scenario.

Paranormality is an observation property of a language, in which the occurrence of unobservable events never exits the closure of the language. In this paper, a synthesis method is proposed to construct a paranormal supervisor. We propose a method to construct a controllable language such that the occurrence of unobservable events does not exit the closure of the controllable language. Moreover, a new observation property, that is, Quasi Output Control Consistency (QOCC) is defined to construct the optimal (least restrictive) non-blocking decentralized supervisory control in the presence of unobservable controllable events. Using QOCC and natural observer properties, we propose a method to construct a normal supervisor such that an arbitrary pair of lookalike strings are initiated and terminated with identical observable and uncontrollable events. It is assumed that one of these strings has unobservable controllable events. An OCC property is defined in the literature as a special case of QOCC property, where none of the lookalike strings has unobservable controllable events.

proposed and investigated. These models can be classified as untimed DES models and timed DES models. In an untimed model, when considering the state evolution, only the sequence of states visited is of concern. That is, only the logical behavior is of interest. In a timed model, both logical behavior and timing information are considered. (Brandin & Wonham, 1994) adjoined to the structure of untimed DES (Ramadge & Wonham, 1989) the timing features of timed transition models. The BW framework, which is used in this chapter, retains the concept of maximally permissive supervision introduced in (Brandin & Wonham, 1994), allows the timed modeling of DES, admits subsystem composition, and admits forcing and disablement as means of control. Different synthesis methods have been developed and implemented as the software TCT (for untimed models) and TTCT (for timed models) (Wonham, 2009) to compute controllers that are optimal in the sense that the controlled system not only satisfies the specifications but is also as permissive as possible. TCT and TTCT are used in this study for synthesizing the supervisory controllers. There are good reasons for organizing the control of large systems in a distributed hierarchy structure. Among these are: deeper understanding facilitated by the hierarchical structure, reduction in complexity of communication and computation, modularity and adaptability to change, robustness, and generalization. The supervisory control of discrete-event systems can be designed to be hierarchically structured. In the present chapter, implementation of this approach to a control problem in electrical power systems is also discussed. A power system, in its simplest representation, comprises a set of lines intersecting at nodes (buses). Energy is injected at buses by generators, and loads can be considered as negative injections. The flow of power along lines to and from buses is a phenomenon of primary interest in power system operation and control. Transformers with tap-changing facilities constitute an important means of controlling voltage throughout electrical power systems at all voltage levels. Transformers with off-load tap-changing facilities can help to maintain satisfactory voltage profiles. Under-load tap-changing transformers (ULTC) can be used to take care of daily, hourly, and minute-by-minute variations in system conditions. ULTC may be controlled either automatically or manually (Kundur, 1994). Many dynamic subsystems in a power system exhibit discrete-event behavior. Typically, the continuous dynamics relate to components that obey physical laws. Event-driven discrete behavior results from logical rules that govern the system. The continuous trajectory of the system state can be interrupted by discrete control actions and uncontrolled disturbances, which may be frequent or infrequent. The time scale for these events changes from milliseconds, through seconds and minutes, to hours, days, and weeks or longer (Fink, 1999). Discrete-event systems theory has been applied to problems in electrical power systems

In this paper some new reliability importance measures are suggested, These measures, which have been developed from ideas presented in Bergman (1985) and by Natvig (1985a), are suitable for the evaluation of component reliability importance of a time dependent system.

We consider a special case of the M/G/∞ traffic process, named as Poisson Lomax Burst Process (PLBP), where the burst lengths follow the Lomax distribution. We illustrate its advantage in modelling Internet traffic flow sizes, particularly, its ability to capture a large number of small flows. We provide two approximations based on analytical and fast simulation methods for the overflow probability of a single server queue fed by PLBP, and illustrate their accuracy by discrete-event simulations.

The optimized placements of mobile antennas (MA) in an existing high traffic urban area are identified using evolutionary multiobjective optimization algorithms (EMOA). The design parameters such as (x, y) coordinates, transmitting power, height, and tilt are considered. The objectives are coverage and cost where as the inequality constraints are handover, traffic demand, and overlap. Modified NSGA‐II (MNSGA‐II) is preferred to improve the non dominated solutions. A detailed study is performed about physical and geometrical structure of propagation environment. Four different case studies are performed. The simulation results reveal that Hata–Okumura path loss model is more suited. MNSGA‐II is able to determine optimal MA parameters for multiobjective case studies. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:737–743, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26627

Discrete-event simulation represents modeling, simulating, and analyzing systems utilizing the computational and mathematical techniques, while creating a model construct of a conceptual framework that describes a system. The system is father simulates by performing experiment(s) using computer implementation of the model and analyzed to draw conclusions from output that assist in decision making process. Discrete event simulation technologies have been extensively used by industry and academia to deal with various industrial problems. By late 1990s, the discrete event simulation was in doldrums as global manufacturing industries went through radical changes. The simulation software industry also went through consolidation. The changes have created new problems, challenges and opportunities to the discrete event simulation. This chapter reviews the discrete event simulation technologies; discusses challenges and opportunities presented by both global manufacturing and the knowledge economy. The authors believe that discrete event simulation remains one of the most effective decision support tools but much need to be done in order to address new challenges. To this end, the chapter calls for development of a new generation of discrete event simulation software.

In this study, we implement a controller, based on Supervisory Control Theory on a pneumatic assembly system. We use local modular approach, which exploits modular structure of the plant and the specifications. An implementation methodology using a hierarchical control structure where PLCs are utilized is introduced. Local modular approach, together with the implementation methodology presented in this study provides an effective way for synthesizing and realizing supervisors for DES control problems. The resulting PLC program is also modular in structure, making it easy to modify and detect errors.

The modeling of large-scale stochastic systems of heterogeneous individuals and their interactions, where multiple behaviors exist, requires a large number of scenarios and repetitions of simulation experiments. In these areas, the agent-based simulation (ABM) is the common tool and the High-Performance Computing can provide an adequate infrastructure for this type of simulations. The present work shows the methodology and the tools developed to allow the execution of multiple simulation scenarios based on ABM Netlogo model simulation in an HPC environment. The goal is to provide a user-friendly environment for ABM simulation to non-technological users.

This paper deals with the design of measurement-based correlation models for shadow fading. Based on the correlation model, we design a simulation model using the sum-of-sinusoids (SOS) method to enable the simulation of spatial lognormal processes characterizing real-world shadow fading scenarios. The model parameters of the simulation model are computed by applying the Lp-norm method (LPNM). This method facilitates an excellent fitting of the simulation model’s autocorrelation function (ACF) to that of measured channels. Our study includes an evaluation of all important statistical quantities of the proposed measurement-based simulation model, such as the probability density function (PDF), spatial ACF, decorrelation and coherence distance, as well as the level-crossing rate (LCR) and the average duration of fades (ADF). A comparison with the Gudmundson correlation model shows that the developed measurement-based correlation model outperforms the former one by far in terms of the ...

This thesis discusses a new synthesis approach to the supervisory control of Timed Discrete Event Systems (TDES). The new approach is much more efficient than the existing synthesis approaches. Using this method, many practical systems can be synthesized using a personal computer. Besides, it is shown that the number of nodes in the Binary Decision Diagram (BDD) representing a TDES can be a better measurement of the complexity of the TDES than the number of states and transitions. The structural information of the timers in a given TDES together with the reduction properties of BDDs are exploited to help this new method achieve more efficient performance. The algorithm is based on the fact that each flat structure can be divided into smaller structures. The success of our new approach is illustrated with very large versions of existing examples taken from the literature. I am very grateful for the support of all my friends in Toronto and specially my colleagues in Systems Control Group. I would like to acknowledge Maziar Khatir, who was highly supportive and Farid Fadaie for his help and patience. Special thanks to Ma Chuan for his complementary guidance throughout the development of this work.

Although different modeling techniques have been proposed during the last 300 years, the differential equation formalism proposed by Newton and Leibniz has been the tool of choice for modeling and problem solving Taylor (1996); Wainer (2009). Differential equations provide a formal mathematical method (sometimes also called an analytical method) for studying the entity of interest. Computational methods based on differential equations could not be easily applied in studying human-made dynamic systems (e.g., traffic controllers, robotic arms, automated factories, production plants, computer networks, VLSI circuits). These systems are usually referred to as discrete event systems because their states do not change continuously but, rather, because of the occurrence of events. This makes them asynchronous, inherently concurrent, and highly nonlinear, rendering their modeling and simulation different from that used in traditional approaches. In order to improve the model definition for this class of systems, a number of techniques were introduced, including Petri Nets, Finite State Machines, min-max algebra,

Although different modeling techniques have been proposed during the last 300 years, the differential equation formalism proposed by Newton and Leibniz has been the tool of choice for modeling and problem solving Taylor (1996); Wainer (2009). Differential equations provide a formal mathematical method (sometimes also called an analytical method) for studying the entity of interest. Computational methods based on differential equations could not be easily applied in studying human-made dynamic systems (e.g., traffic controllers, robotic arms, automated factories, production plants, computer networks, VLSI circuits). These systems are usually referred to as discrete event systems because their states do not change continuously but, rather, because of the occurrence of events. This makes them asynchronous, inherently concurrent, and highly nonlinear, rendering their modeling and simulation different from that used in traditional approaches. In order to improve the model definition for this class of systems, a number of techniques were introduced, including Petri Nets, Finite State Machines, min-max algebra,

In the present paper the Natvig measures of component importance for repairable systems, and its extended version are analysed for two three component systems and a bridge system. The measures are also applied to an offshore oil and gas production system. According to the extended version of the Natvig measure a component is important if both by failing it strongly reduces the expected system uptime and by being repaired it strongly reduces the expected system downtime. The results include a study of how different distributions affect the ranking of the components. All numerical results are computed using discrete event simulation. In a companion paper (Huseby, Eide, Isaksen, Natvig and Gåsemyr 2008) the advanced simulation methods needed in these calculations are described

In the present paper the Natvig measures of component importance for repairable systems, and its extended version are applied to an offshore oil and gas production system. According to the extended version of the Natvig measure a component is important if both by failing it strongly reduces the expected system uptime and by being repaired it strongly reduces the expected system downtime. The results include a study of how different distributions affect the ranking of the components. All numerical results are computed using discrete event simulation. In a companion paper (Huseby, Eide, Isaksen, Natvig, and Gåsemyr 2008) the advanced simulation methods needed in these calculations are decribed.

In the present paper the Natvig measures of component importance for repairable systems, and its extended version are analysed for two three component systems and a bridge system. The measures are also applied to an offshore oil and gas production system. According to the extended version of the Natvig measure a component is important if both by failing it strongly reduces the expected system uptime and by being repaired it strongly reduces the expected system downtime. The results include a study of how different distributions affect the ranking of the components. All numerical results are computed using discrete event simulation. In a companion paper [4] the advanced simulation methods needed in these calculations are described.

Tourism researchers together with their counterparts in urban planning and economics have grappled with the complexities of urban destination management and have been on a search for an integrative methodological framework ever since. This paper explores the prospects of cellular automata (CA) models and their use as modeling environments for urban tourism research. The discussion begins by way of an introduction to past applications in the natural and computer sciences. Certain speculations are made with regards to their utility and relevance to the modeling of urban tourism. The economic paradigm features largely together with discussions on the influence of complexity economics and its roots in complex adaptive systems.

The modeling of large-scale stochastic systems of heterogeneous individuals and their interactions, where multiple behaviors exist, requires a large number of scenarios and repetitions of simulation experiments. In these areas, the agent-based simulation (ABM) is the common tool and the High-Performance Computing can provide an adequate infrastructure for this type of simulations. The present work shows the methodology and the tools developed to allow the execution of multiple simulation scenarios based on ABM Netlogo model simulation in an HPC environment. The goal is to provide a user-friendly environment for ABM simulation to non-technological users.

We propose a new linear-regression model for the estimation of the path-loss exponent and the parameters of the shadowing from the propagation-loss data collected by the mobiles with respect to their serving base stations. The difficulty consists in deriving the parameters of the distribution of the propagation loss with respect to an arbitrary base station from these regarding the strongest one. The proposed solution is based on a simple, explicit relation between the two distributions in the case of infinite Poisson network and on the convergence of an arbitrary regular (in particular hexagonal) network to the Poisson one with increasing variance of the shadowing. The new approach complements existing methods, in particular the one based on COST Walfisch-Ikegami model, which does not allow for the shadowing estimation and is not suited for indoor scenario.

Discrete-event simulation represents modeling, simulating, and analyzing systems utilizing the computational and mathematical techniques, while creating a model construct of a conceptual framework that describes a system. The system is father simulates by performing experiment(s) using computer implementation of the model and analyzed to draw conclusions from output that assist in decision making process. Discrete event simulation technologies have been extensively used by industry and academia to deal with various industrial problems. By late 1990s, the discrete event simulation was in doldrums as global manufacturing industries went through radical changes. The simulation software industry also went through consolidation. The changes have created new problems, challenges and opportunities to the discrete event simulation. This chapter reviews the discrete event simulation technologies; discusses challenges and opportunities presented by both global manufacturing and the knowledge economy. The authors believe that discrete event simulation remains one of the most effective decision support tools but much need to be done in order to address new challenges. To this end, the chapter calls for development of a new generation of discrete event simulation software.

The modeling of large-scale stochastic systems of heterogeneous individuals and their interactions, where multiple behaviors exist, requires a large number of scenarios and repetitions of simulation experiments. In these areas, the agent-based simulation (ABM) is the common tool and the High-Performance Computing can provide an adequate infrastructure for this type of simulations. The present work shows the methodology and the tools developed to allow the execution of multiple simulation scenarios based on ABM Netlogo model simulation in an HPC environment. The goal is to provide a user-friendly environment for ABM simulation to non-technological users.

In the present paper the Natvig measures of component importance for repairable systems, and its extended version are analysed for two three component systems and a bridge system. The measures are also applied to an offshore oil and gas production system. According to the extended version of the Natvig measure a component is important if both by failing it strongly reduces the expected system uptime and by being repaired it strongly reduces the expected system downtime. The results include a study of how different distributions affect the ranking of the components. All numerical results are computed using discrete event simulation. In a companion paper [4] the advanced simulation methods needed in these calculations are described.

The modeling of large-scale stochastic systems of heterogeneous individuals and their interactions, where multiple behaviors exist, requires a large number of scenarios and repetitions of simulation experiments. In these areas, the agent-based simulation (ABM) is the common tool and the High-Performance Computing can provide an adequate infrastructure for this type of simulations. The present work shows the methodology and the tools developed to allow the execution of multiple simulation scenarios based on ABM Netlogo model simulation in an HPC environment. The goal is to provide a user-friendly environment for ABM simulation to non-technological users.

Emergency plan preparation is an important problem in building design to evacuate people as fast as possible. Simulation exercises as fire drills are not a realistic situation to understand people behaviour. In the case of crowd evacuations the complexity and uncertainty of the systems increases. Computer simulation allows us to run crowd dynamics models and extract information from emergency situations. Several models solve the emergency evacuation problem. Individual oriented modelling allows to give behaviour rules to the individual and simulate interactions between them. Due to variation on the emergency situations results have to be statistically reliable. This reliability increases the computing demand. Distributed and parallel paradigms solve the performance problem. In the present work we present a crowd evacuations distributed simulator. We implemented two versions of the model. One using Netlogo and another using C with MPI. We chose a real environment to test the simulator: pavilion 2 of Fira de Barcelona building, able to hold thousands of persons. The distributed simulator was tested with 62,820 runs in a distributed environment with 15,000 individuals. In this work we show how the distributed simulator has a linear speedup and scales efficiently.

In this paper, tuning of COST-231 Hata prediction method has been carried out based on experiments at 2.3 GHz using WiMAX transmissions conducted in western India. Fine tuning of the model is carried out using large number of measurement records to increase the accuracy of the prediction. The parameters of this model are tuned based on a linear iterative tuning method. The path loss predictions of tuned model are compared with the original COST-231 Hata model. Mean errors, standard deviation of error of tuned COST-231 Hata model has been deduced and compared with original COST-231Hata model. Mean error calculated by tuned model reduces by 14.3 dB and reduction of 3.5 dB is achieved in standard deviation of error. Root mean square error and coefficient of variation of tuned model are used to validate the tuning method. Root mean square error of tuned model reduces by 14.4 dB compared with the original COST-231 Hata model.

This paper intends to study outdoor RF attenuation path loss behavior under certain restrictions. The study has been conducted in Nablus city to develop and optimize a suitable propagation model based on one of the existing propagation models based on outdoor measurements for 900 MHz, where a local GSM system is operating under sever geographical terrains and frequency limitations. The optimized model has been chosen such that certain error parameters are minimized. Some of the proposed models are; Bertoni-Walfish, Hat, Walfisch-Ikegami and the standard macrocell. In this paper a Tuned Bertoni-Walfisch model has outperformed the other models and has proven, to be the best suited for propagation analysis involving such terrain. This is achieved by varying the range dependence using Least Mean Square Error

This paper intends to study outdoor RF attenuation path loss behavior under certain restrictions. The study has been conducted in Nablus city to develop and optimize a suitable propagation model based on one of the existing propagation models based on outdoor measurements for 900 MHz, where a local GSM system is operating under sever geographical terrains and frequency limitations. The optimized model has been chosen such that certain error parameters are minimized. Some of the proposed models are; Bertoni-Walfish, Hat, Walfisch-Ikegami and the standard macrocell. In this paper a Tuned Bertoni-Walfisch model has outperformed the other models and has proven, to be the best suited for propagation analysis involving such terrain. This is achieved by varying the range dependence using Least Mean Square Error

Simulation has become an indispensable tool for researchers to explore systems without having recourse to real experiments. Depending on the characteristics of the modeled system, methods used to represent the system may vary. Multi-agent systems are, thus, often used to model and simulate complex systems. Whatever modeling type used, increasing the size and the precision of the model increases the amount of computation, requiring the use of parallel systems when it becomes too large. In this paper, we focus on parallel platforms that support multi-agent simulations. Our contribution is a survey on existing platforms and their evaluation in the context of high performance computing. We present a qualitative analysis, mainly based on platform properties, then a performance comparison using the same agent model implemented on each platform.

In the present paper the Natvig measures of component importance for repairable systems, and its extended version are applied to an offshore oil and gas production system. According to the extended version of the Natvig measure a component is important if both by failing it strongly reduces the expected system uptime and by being repaired it strongly reduces the expected system downtime. The results include a study of how different distributions affect the ranking of the components. All numerical results are computed using discrete event simulation. In a companion paper (Huseby, Eide, Isaksen, Natvig, and Gåsemyr 2008) the advanced simulation methods needed in these calculations are decribed.

The tremendous demand is pushing the development of wireless mobile communications faster than ever before. Handoff management has widely been recognized as one of the most important and challenging problems for a seamless access to wireless network and mobile services. Mobility Models plays an important role in handoff management. In this paper, the effect of handoff procedure on the performance of random mobile nodes in wireless networks was investigated. Mobility of node is defined by various mobility models. Evaluating mobility models within an infrastructured network gives solution to performance measures like blocking probability, dropping probability to evaluate the performance of handoff algorithm. Handoff algorithm based on Absolute and Relative Measurement was used to examine the effect of Random Walk and Gauss Markov mobility models on performance of Infrastructure based Wireless Network.

For the past few years, the scope of optimization techniques has been widened at a rapid rate in order to unravel a variety of design problems in cellular networks, with a clear vision to save cost as well as to improve the quality of the overall network. In real life scenario, we have varying geographic locations to set up a cellular network that include sparse as well as dense populated areas. WiMAX (Worldwide Interoperability for Microwave Access) is a cell-based technology specified by the IEEE 802.16 standard that supports quality of service (QoS) mechanisms and enables highquality levels of applications such as VoIP. In this research paper, we have highlighted the results obtained through simulation carried out on OPNET modeler to deduce that IEEE 802.16 networks perform differently for different network traffic, number of mobile nodes, distance from base station and mobile speed. The measured performance metrics like throughput, delay etc will positively lend us a hand while ...

In recent years, malware has evolved by using different obfuscation techniques; due to this evolution, the detection of malware has become problematic. Signature-based and traditional behavior-based malware detectors cannot effectively detect this new generation of malware. This paper proposes a subtractive center behavior model (SCBM) to create a malware dataset that captures semantically related behaviors from sample programs. In the proposed model, system paths, where malware behaviors are performed, and malware behaviors themselves are taken into consideration. This way malicious behavior patterns are differentiated from benign behavior patterns. Features that could not exceed the specified score are removed from the dataset. The datasets created using the proposed model contain far fewer features than the datasets created by n-gram and other models that have been used in other studies. The proposed model can handle both known and unknown malware, and the obtained detection rate...

A Query-Driven Simulation systein C a s s n~~d r n S is deqcrihed that allows end-users to ask queries about outcomes of runnillg siinttlation models written in various simulation languages. The architecture of Cassandra+, its rlttery language SZIZIQL, and the implementation of the system are described. It is argued that Query-Dri\ic>it Simulations provide a more declarative, flexible, and interactive approacll to asltiitg cluestions about siitlulatioit outcomes than the traditional approaches of letting the end-ubers run simulations and gather statistics on the trace files being produced.

This paper studies conditions where messages endlessly circulate in a system of IEEE 1588-2008 clocks. The study is based on two independent analysis techniques. One uses a discrete event simulation environment for describing the operation of the best master clock (BMC) algorithm in IEEE 1588-2008. The second uses a model checking tool. We discuss several cases illustrating conditions under which circulating messages occur and the effectiveness of measures to squelch these messages. This paper demonstrates that one or more of the squelching mechanisms must be implemented. 1

This paper intends to study outdoor RF attenuation path loss behavior under certain restrictions. The study has been conducted in Nablus city to develop and optimize a suitable propagation model based on one of the existing propagation models based on outdoor measurements for 900 MHz, where a local GSM system is operating under sever geographical terrains and frequency limitations. The optimized model has been chosen such that certain error parameters are minimized. Some of the proposed models are; Bertoni-Walfish, Hat, Walfisch-Ikegami and the standard macrocell. In this paper a Tuned Bertoni-Walfisch model has outperformed the other models and has proven, to be the best suited for propagation analysis involving such terrain. This is achieved by varying the range dependence using Least Mean Square Error

One of the important issues in the design of future generation high-speed networks is the provision of real-time services to different types of traf®c with various time constraints. In this paper we study the problem of providing real-time service to hard and soft real-time messages in Wavelength-Division-Multiplexing (WDM) optical networks. We propose a set of scheduling algorithms which prioritize and manage message transmissions in single-hop WDM passive star networks based on speci®c message time constraints. In particular, we develop time-based priority schemes for scheduling message transmissions in order to increase the real-time performance of a WDM network topology. We formulated an analytical model and conducted extensive discrete-event simulations to evaluate the performance of the proposed algorithms. We compared their performances with that of the state-of-the-art WDM scheduling algorithms which typically do not consider the time constraint of the transmitted messages. This study suggests that when scheduling real-time messages in WDM networks, one has to consider not only the problem of resources allocation in the network but also the problem of sequencing messages based on their time constraints.

In this paper we present a hierarchical loss network model for estimating the end-to-end blocking probabilities for large networks. As networks grow in size, nodes tend to form clusters geographically and hierarchical routing schemes are more commonly used. Loss network and reduced load models are often used to approximate end-to-end call blocking probabilities and hence throughput. However so far all work being done in this area is for flat networks with flat routing schemes. We aim at developing a more efficient approximation method for networks that have a natural hierarchy and/or when some form of hierarchical routing policy is used. We present two hierarchical models in detail for fixed hierarchical routing and dynamic hierarchical routing policies, respectively, via the notion of network abstraction, route segmentation, traffic segregation and aggregation. Computation is done separately within each cluster (local) and among clusters (global), and the fixed point is obtained by iteration between local and global computations. We also present numerical results for the first case.

proposed and investigated. These models can be classified as untimed DES models and timed DES models. In an untimed model, when considering the state evolution, only the sequence of states visited is of concern. That is, only the logical behavior is of interest. In a timed model, both logical behavior and timing information are considered. (Brandin & Wonham, 1994) adjoined to the structure of untimed DES (Ramadge & Wonham, 1989) the timing features of timed transition models. The BW framework, which is used in this chapter, retains the concept of maximally permissive supervision introduced in (Brandin & Wonham, 1994), allows the timed modeling of DES, admits subsystem composition, and admits forcing and disablement as means of control. Different synthesis methods have been developed and implemented as the software TCT (for untimed models) and TTCT (for timed models) (Wonham, 2009) to compute controllers that are optimal in the sense that the controlled system not only satisfies the specifications but is also as permissive as possible. TCT and TTCT are used in this study for synthesizing the supervisory controllers. There are good reasons for organizing the control of large systems in a distributed hierarchy structure. Among these are: deeper understanding facilitated by the hierarchical structure, reduction in complexity of communication and computation, modularity and adaptability to change, robustness, and generalization. The supervisory control of discrete-event systems can be designed to be hierarchically structured. In the present chapter, implementation of this approach to a control problem in electrical power systems is also discussed. A power system, in its simplest representation, comprises a set of lines intersecting at nodes (buses). Energy is injected at buses by generators, and loads can be considered as negative injections. The flow of power along lines to and from buses is a phenomenon of primary interest in power system operation and control. Transformers with tap-changing facilities constitute an important means of controlling voltage throughout electrical power systems at all voltage levels. Transformers with off-load tap-changing facilities can help to maintain satisfactory voltage profiles. Under-load tap-changing transformers (ULTC) can be used to take care of daily, hourly, and minute-by-minute variations in system conditions. ULTC may be controlled either automatically or manually (Kundur, 1994). Many dynamic subsystems in a power system exhibit discrete-event behavior. Typically, the continuous dynamics relate to components that obey physical laws. Event-driven discrete behavior results from logical rules that govern the system. The continuous trajectory of the system state can be interrupted by discrete control actions and uncontrolled disturbances, which may be frequent or infrequent. The time scale for these events changes from milliseconds, through seconds and minutes, to hours, days, and weeks or longer (Fink, 1999). Discrete-event systems theory has been applied to problems in electrical power systems

This paper first highlights common evaluation techniques used in wireless ad hoc networks. Specifically, it focuses on the discrete-event simulations. The paper further reviews methods that are used during the analysis of the generated results from discrete-event simulations. Steady-state detection, simulation time, and confidence intervals are considered.

This paper intends to study outdoor RF attenuation path loss behavior under certain restrictions. The study has been conducted in Nablus city to develop and optimize a suitable propagation model based on one of the existing propagation models based on outdoor measurements for 900 MHz, where a local GSM system is operating under sever geographical terrains and frequency limitations. The optimized model has been chosen such that certain error parameters are minimized. Some of the proposed models are; Bertoni-Walfish, Hat, Walfisch-Ikegami and the standard macrocell. In this paper a Tuned Bertoni-Walfisch model has outperformed the other models and has proven, to be the best suited for propagation analysis involving such terrain. This is achieved by varying the range dependence using Least Mean Square Error (LMSE) method

Discrete-event simulation represents modeling, simulating, and analyzing systems utilizing the computational and mathematical techniques, while creating a model construct of a conceptual framework that describes a system. The system is father simulates by performing experiment(s) using computer implementation of the model and analyzed to draw conclusions from output that assist in decision making process. Discrete event simulation technologies have been extensively used by industry and academia to deal with various industrial problems. By late 1990s, the discrete event simulation was in doldrums as global manufacturing industries went through radical changes. The simulation software industry also went through consolidation. The changes have created new problems, challenges and opportunities to the discrete event simulation. This chapter reviews the discrete event simulation technologies; discusses challenges and opportunities presented by both global manufacturing and the knowledge economy. The authors believe that discrete event simulation remains one of the most effective decision support tools but much need to be done in order to address new challenges. To this end, the chapter calls for development of a new generation of discrete event simulation software.

Discrete-event simulation represents modeling, simulating, and analyzing systems utilizing the computational and mathematical techniques, while creating a model construct of a conceptual framework that describes a system. The system is father simulates by performing experiment(s) using computer implementation of the model and analyzed to draw conclusions from output that assist in decision making process. Discrete event simulation technologies have been extensively used by industry and academia to deal with various industrial problems. By late 1990s, the discrete event simulation was in doldrums as global manufacturing industries went through radical changes. The simulation software industry also went through consolidation. The changes have created new problems, challenges and opportunities to the discrete event simulation. This chapter reviews the discrete event simulation technologies; discusses challenges and opportunities presented by both global manufacturing and the knowledge economy. The authors believe that discrete event simulation remains one of the most effective decision support tools but much need to be done in order to address new challenges. To this end, the chapter calls for development of a new generation of discrete event simulation software.

The large-scale simulation of complex cognitive agent based models simulated on realistic physical space require special computer processing considerations. In this paper, we explore the up-scaling of an agent based model to city-scale, by considering details about how to implement the models in a cellular automata (CA) in a parallel-distributed simulation (PDS) framework.