Evaluating Fast And Efficient Modeling Methods For Simulation Based Optimization
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Simulation-Based Optimization
| Author | : Abhijit Gosavi |
| Publisher | : Springer |
| Total Pages | : 530 |
| Release | : 2014-10-30 |
| Genre | : Business & Economics |
| ISBN | : 1489974911 |
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.
Simulation-driven Design Optimization And Modeling For Microwave Engineering
| Author | : Qi-jun Zhang |
| Publisher | : World Scientific |
| Total Pages | : 526 |
| Release | : 2013-03-14 |
| Genre | : Technology & Engineering |
| ISBN | : 1848169221 |
Computer-aided full-wave electromagnetic (EM) analysis has been used in microwave engineering for the past decade. Initially, its main application area was design verification. Today, EM-simulation-driven optimization and design closure become increasingly important due to the complexity of microwave structures and increasing demands for accuracy. In many situations, theoretical models of microwave structures can only be used to yield the initial designs that need to be further fine-tuned to meet given performance requirements. In addition, EM-based design is a must for a growing number of microwave devices such as ultra-wideband (UWB) antennas, dielectric resonator antennas and substrate-integrated circuits. For circuits like these, no design-ready theoretical models are available, so design improvement can only be obtained through geometry adjustments based on repetitive, time-consuming simulations. On the other hand, various interactions between microwave devices and their environment, such as feeding structures and housing, must be taken into account, and this is only possible through full-wave EM analysis.Electromagnetic simulations can be highly accurate, but they tend to be computationally expensive. Therefore, practical design optimization methods have to be computationally efficient, so that the number of CPU-intensive high-fidelity EM simulations is reduced as much as possible during the design process. For the same reasons, techniques for creating fast yet accurate models of microwave structures become crucially important.In this edited book, the authors strive to review the state-of-the-art simulation-driven microwave design optimization and modeling. A group of international experts specialized in various aspects of microwave computer-aided design summarize and review a wide range of the latest developments and real-world applications. Topics include conventional and surrogate-based design optimization techniques, methods exploiting adjoint sensitivity, simulation-based tuning, space mapping, and several modeling methodologies, such as artificial neural networks and kriging. Applications and case studies include microwave filters, antennas, substrate integrated structures and various active components and circuits. The book also contains a few introductory chapters highlighting the fundamentals of optimization and modeling, gradient-based and derivative-free algorithms, metaheuristics, and surrogate-based optimization techniques, as well as finite difference and finite element methods./a
High-Performance Simulation-Based Optimization
| Author | : Thomas Bartz-Beielstein |
| Publisher | : Springer |
| Total Pages | : 291 |
| Release | : 2019-06-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3030187640 |
This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
Handbook of Simulation Optimization
| Author | : Michael C Fu |
| Publisher | : Springer |
| Total Pages | : 400 |
| Release | : 2014-11-13 |
| Genre | : Business & Economics |
| ISBN | : 1493913840 |
The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology. Leading contributors cover such topics as discrete optimization via simulation, ranking and selection, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, variance reduction techniques, model-based stochastic search methods and Markov decision processes. This single volume should serve as a reference for those already in the field and as a means for those new to the field for understanding and applying the main approaches. The intended audience includes researchers, practitioners and graduate students in the business/engineering fields of operations research, management science, operations management and stochastic control, as well as in economics/finance and computer science.
Reduced-Order Modeling (ROM) for Simulation and Optimization
| Author | : Winfried Keiper |
| Publisher | : Springer |
| Total Pages | : 184 |
| Release | : 2018-04-11 |
| Genre | : Mathematics |
| ISBN | : 3319753193 |
This edited monograph collects research contributions and addresses the advancement of efficient numerical procedures in the area of model order reduction (MOR) for simulation, optimization and control. The topical scope includes, but is not limited to, new out-of-the-box algorithmic solutions for scientific computing, e.g. reduced basis methods for industrial problems and MOR approaches for electrochemical processes. The target audience comprises research experts and practitioners in the field of simulation, optimization and control, but the book may also be beneficial for graduate students alike.
Modern Simulation and Modeling
| Author | : Reuven Y. Rubinstein |
| Publisher | : Wiley-Interscience |
| Total Pages | : 392 |
| Release | : 1998-03-09 |
| Genre | : Mathematics |
| ISBN | : |
A step-by-step guide for today's modeling and simulation practices This new guide for modeling and simulation of discrete-event systems (DES) demonstrates why simulation is fast becoming the method of choice for the evaluation of system performance in science, engineering, and management. The book begins with the basics of conventional simulation, then proceeds to modern simulation-treating sensitivity analysis and optimization in a wide range of systems that exhibit complex interaction of discrete events. These include communications networks, flexible manufacturing systems, PERT (project evaluation and review techniques) networks, queueing systems, and more. Less focused on theory than on presenting a clear approach to practical applications, Modern Simulation and Modeling: * Emphasizes concepts rather than mathematical completeness * Integrates references and explanations of complex topics into the body of the text * Provides an innovative chapter on rare-event probability estimation * Describes the implementation of the score function (SF) method using the NSO simulation package * Features 40 illustrations and numerous algorithms * Offers extensive, end-of-chapter exercise sets * Includes chapter bibliographies for further reading Modern Simulation and Modeling is an essential text for graduate students of DES and stochastic processes and for undergraduate students in simulation. It is also an excellent reference for professionals in statistics and probability, mathematics, and management science.
Exploration and Exploitation Techniques for High-dimensional Simulation-based Optimization Problems in Urban Transportation
| Author | : Timothy Tay |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
Stochastic traffic and mobility simulation models are popular tools for modeling urban transportation networks. However, their use for optimizing urban transportation networks can be challenging due to their computationally intensive nature. This thesis focuses on high-dimensional simulation-based (SO) optimization problems. To find solutions with good performance efficiently, we need to balance exploration and exploitation. We propose techniques for achieving a better balance between exploration and exploitation when tackling high-dimensional SO problems in urban transportation. The first part of the thesis considers a general-purpose exploration mechanism and introduces exploitation components to it. We propose an inverse cumulative distribution function (cdf) sampling mechanism that makes use of problem-specific prior information in the form of an analytical model to efficiently sample for points with good performance. The inverse cdf sampling mechanism can be used in conjunction with any optimization algorithm. We study whether problem-specific prior information should be used in the exploration (i.e., sampling) mechanism and/or the exploitation (i.e., optimization) algorithm when tackling a high-dimensional traffic signal control problem in Midtown Manhattan. The results show that the use of inverse cdf sampling mechanism as part of an optimization framework can help to quickly and efficiently identify solutions with good performance. The second and third parts of the thesis focus on developing a framework to enable high-dimensional Bayesian optimization (BO) for stationary and dynamic transportation SO problems respectively. BO naturally combines exploration and exploitation. In the second part, we consider stationary problems and propose approaches to incorporate problem-specific prior information in the BO prior functions such as to jointly enhance both exploration and exploitation. This is done through the use of a stationary analytical surrogate traffic model. In the third part, we extend the BO framework to tackle dynamic problems by formulating and embedding a computation ally efficient dynamic analytical surrogate traffic model. For both parts, we evaluate their performance with a traffic signal control problems for a congested Midtown Manhattan (New York City) network. The proposed methods enhance the ability of BO to tackle high-dimensional urban transportation SO problems.
Tools and Methods for Analysis, Debugging, and Performance Improvement of Equation-Based Models
| Author | : Martin Sjölund |
| Publisher | : Linköping University Electronic Press |
| Total Pages | : 243 |
| Release | : 2015-05-11 |
| Genre | : Debugging in computer science |
| ISBN | : 9175190710 |
Equation-based object-oriented (EOO) modeling languages such as Modelica provide a convenient, declarative method for describing models of cyber-physical systems. Because of the ease of use of EOO languages, large and complex models can be built with limited effort. However, current state-of-the-art tools do not provide the user with enough information when errors appear or simulation results are wrong. It is of paramount importance that such tools should give the user enough information to correct errors or understand where the problems that lead to wrong simulation results are located. However, understanding the model translation process of an EOO compiler is a daunting task that not only requires knowledge of the numerical algorithms that the tool executes during simulation, but also the complex symbolic transformations being performed. As part of this work, methods have been developed and explored where the EOO tool, an enhanced Modelica compiler, records the transformations during the translation process in order to provide better diagnostics, explanations, and analysis. This information is used to generate better error-messages during translation. It is also used to provide better debugging for a simulation that produces unexpected results or where numerical methods fail. Meeting deadlines is particularly important for real-time applications. It is usually essential to identify possible bottlenecks and either simplify the model or give hints to the compiler that enable it to generate faster code. When profiling and measuring execution times of parts of the model the recorded information can also be used to find out why a particular system model executes slowly. Combined with debugging information, it is possible to find out why this system of equations is slow to solve, which helps understanding what can be done to simplify the model. A tool with a graphical user interface has been developed to make debugging and performance profiling easier. Both debugging and profiling have been combined into a single view so that performance metrics are mapped to equations, which are mapped to debugging information. The algorithmic part of Modelica was extended with meta-modeling constructs (MetaModelica) for language modeling. In this context a quite general approach to debugging and compilation from (extended) Modelica to C code was developed. That makes it possible to use the same executable format for simulation executables as for compiler bootstrapping when the compiler written in MetaModelica compiles itself. Finally, a method and tool prototype suitable for speeding up simulations has been developed. It works by partitioning the model at appropriate places and compiling a simulation executable for a suitable parallel platform.
Simulation-based Optimization of Energy Efficiency in Production
| Author | : Anna Carina Römer |
| Publisher | : Springer Nature |
| Total Pages | : 221 |
| Release | : 2021-02-11 |
| Genre | : Business & Economics |
| ISBN | : 3658329718 |
The importance of the energy and commodity markets has steadily increased since the first oil crisis. The sustained use of energy and other resources has become a basic requirement for a company to competitively perform on the market. The modeling, analysis and assessment of dynamic production processes is often performed using simulation software. While existing approaches mainly focus on the consideration of resource consumption variables based on metrologically collected data on operating states, the aim of this work is to depict the energy consumption of production plants through the utilization of a continuous simulation approach in combination with a discrete approach for the modeling of material flows and supporting logistic processes. The complex interactions between the material flow and the energy usage in production can thus be simulated closer to reality, especially the depiction of energy consumption peaks becomes possible. An essential step towards reducing energy consumption in production is the optimization of the energy use of non-value-adding production phases.
