Improving distributed simulation in a workstation environment

Technological development brings more and more complex systems to the consumer markets. The time required for bringing a new product to market is crucial for the competitive edge of a company. Simulation is used as a tool to model these products and their operation before actual live systems are built. The complexity of these systems can easily require large amounts of memory and computing power. Distributed simulation can be used to meet these demands. Distributed simulation has its problems. Diworse, a distributed simulation environment, was used in this study to analyze the different factors that affect the time required for the simulation of a system. Examples of these factors are the simulation algorithm, communication protocols, partitioning of the problem, distributionof the problem, capabilities of the computing and communications equipment and the external load. Offices offer vast amounts of unused capabilities in the formof idle workstations. The use of this computing power for distributed simulation requires the simulation to adapt to a changing load situation. This requires all or part of the simulation work to be removed from a workstation when the owner wishes to use the workstation again. If load balancing is not performed, the simulation suffers from the workstation's reduced performance, which also hampers the owner's work. Operation of load balancing in Diworse is studied and it is shown to perform better than no load balancing, as well as which different approaches for load balancing are discussed.

Development a distributed simulation environment on a cluster of workstations

Simulation has traditionally been used for analyzing the behavior of complex real world problems. Even though only some features of the problems are considered, simulation time tends to become quite high even for common simulation problems. Parallel and distributed simulation is a viable technique for accelerating the simulations. The success of parallel simulation depends heavily on the combination of the simulation application, algorithm and message population in the simulation is sufficient, no additional delay is caused by this environment. In this thesis a conservative, parallel simulation algorithm is applied to the simulation of a cellular network application in a distributed workstation environment. This thesis presents a distributed simulation environment, Diworse, which is based on the use of networked workstations. The distributed environment is considered especially hard for conservative simulation algorithms due to the high cost of communication. In this thesis, however, the distributed environment is shown to be a viable alternative if the amount of communication is kept reasonable. Novel ideas of multiple message simulation and channel reduction enable efficient use of this environment for the simulation of a cellular network application. The distribution of the simulation is based on a modification of the well known Chandy-Misra deadlock avoidance algorithm with null messages. The basic Chandy Misra algorithm is modified by using the null message cancellation and multiple message simulation techniques. The modifications reduce the amount of null messages and the time required for their execution, thus reducing the simulation time required. The null message cancellation technique reduces the processing time of null messages as the arriving null message cancels other non processed null messages. The multiple message simulation forms groups of messages as it simulates several messages before it releases the new created messages. If the message population in the simulation is suffiecient, no additional delay is caused by this operation A new technique for considering the simulation application is also presented. The performance is improved by establishing a neighborhood for the simulation elements. The neighborhood concept is based on a channel reduction technique, where the properties of the application exclusively determine which connections are necessary when a certain accuracy for simulation results is required. Distributed simulation is also analyzed in order to find out the effect of the different elements in the implemented simulation environment. This analysis is performed by using critical path analysis. Critical path analysis allows determination of a lower bound for the simulation time. In this thesis critical times are computed for sequential and parallel traces. The analysis based on sequential traces reveals the parallel properties of the application whereas the analysis based on parallel traces reveals the properties of the environment and the distribution.

Numerical Simulation of Stochastic Di erential Equations

Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.

New directions in Stochastic Multicriteria Acceptability Analysis

Decisions taken in modern organizations are often multi-dimensional, involving multiple decision makers and several criteria measured on different scales. Multiple Criteria Decision Making (MCDM) methods are designed to analyze and to give recommendations in this kind of situations. Among the numerous MCDM methods, two large families of methods are the multi-attribute utility theory based methods and the outranking methods. Traditionally both method families require exact values for technical parameters and criteria measurements, as well as for preferences expressed as weights. Often it is hard, if not impossible, to obtain exact values. Stochastic Multicriteria Acceptability Analysis (SMAA) is a family of methods designed to help in this type of situations where exact values are not available. Different variants of SMAA allow handling all types of MCDM problems. They support defining the model through uncertain, imprecise, or completely missing values. The methods are based on simulation that is applied to obtain descriptive indices characterizing the problem. In this thesis we present new advances in the SMAA methodology. We present and analyze algorithms for the SMAA-2 method and its extension to handle ordinal preferences. We then present an application of SMAA-2 to an area where MCDM models have not been applied before: planning elevator groups for high-rise buildings. Following this, we introduce two new methods to the family: SMAA-TRI that extends ELECTRE TRI for sorting problems with uncertain parameter values, and SMAA-III that extends ELECTRE III in a similar way. An efficient software implementing these two methods has been developed in conjunction with this work, and is briefly presented in this thesis. The thesis is closed with a comprehensive survey of SMAA methodology including a definition of a unified framework.

Learning Robot Environment through Simulation

Traditionally simulators have been used extensively in robotics to develop robotic systems without the need to build expensive hardware. However, simulators can be also be used as a “memory”for a robot. This allows the robot to try out actions in simulation before executing them for real. The key obstacle to this approach is an uncertainty of knowledge about the environment. The goal of the Master’s Thesis work was to develop a method, which allows updating the simulation model based on actual measurements to achieve a success of the planned task. OpenRAVE was chosen as an experimental simulation environment on planning,trial and update stages. Steepest Descent algorithm in conjunction with Golden Section search procedure form the principle part of optimization process. During experiments, the properties of the proposed method, such as sensitivity to different parameters, including gradient and error function, were examined. The limitations of the approach were established, based on analyzing the regions of convergence.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

Stochastic variability of output levels in a pulp mill

Quite often, in the construction of a pulp mill involves establishing the size of tanks which will accommodate the material from the various processes in which case estimating the right tank size a priori would be vital. Hence, simulation of the whole production process would be worthwhile. Therefore, there is need to develop mathematical models that would mimic the behavior of the output from the various production units of the pulp mill to work as simulators. Markov chain models, Autoregressive moving average (ARMA) model, Mean reversion models with ensemble interaction together with Markov regime switching models are proposed for that purpose.

Stochastic approximation methods in stochastic optimization

Stochastic approximation methods for stochastic optimization are considered. Reviewed the main methods of stochastic approximation: stochastic quasi-gradient algorithm, Kiefer-Wolfowitz algorithm and adaptive rules for them, simultaneous perturbation stochastic approximation (SPSA) algorithm. Suggested the model and the solution of the retailer's profit optimization problem and considered an application of the SQG-algorithm for the optimization problems with objective functions given in the form of ordinary differential equation.

Mechanical design and optics simulation of the illuminated Volvo Iron mark

Utilization of light and illumination systems in automotive industry for different purposes has been increased significantly in recent years. Volvo as one of the leading companies in manufacturing of luxury cars has found the great capacity in this area. The performance of such an illumination systems is one of the challenges that engineers in this industry are facing with. In this study an effort has been made to design a system to make the iron mark of Volvo being illuminated and the system is being evaluated by optics simulation in software using Ray optics method. At the end, results are assessed and some optimizations are carried out. Different kind of light guides, front side of the iron mark and some possible arrangement for LED also evaluated and different materials tested. The best combination from uniformity, color and amount of luminance aspect selected as a possible solution for this special project which can be used as a base for further studies in Volvo.