Michael C. Fu – författare

Saul Gass has been a leading contributor to the field of Operations Research for more than 50 years. He has been affiliated with the Robert H. Smith School of Business at the University of Maryland for more than 30 years. On February 25, 2006, "Operations Research in the 21st Century: A Symposium in Honor of Professor Saul Gass' 80th Birthday," was held on our campus. Opening remarks by Deans Howard Prank and Rudy Lamone were followed by talks by Alfred Blumstein, Karla Hoffman, Richard Larson, Christoph Witzgall, Thomas Magnanti, Rakesh Vohra, and Bruce Golden. The celebration continued into the evening with dinner in the Executive Din­ ing Room of Van Munching Hall, followed by numerous toasts to Saul. It was a special day for all of us who were in attendance, but it was especially memorable for Saul and his family. This Festschrift companion to the Symposium includes articles from each of the Symposium distinguished speakers plus 16 other articles from friends, colleagues, and several of Saul's former students. The book is divided into three sections. The first section comprises eight articles focusing on the field of Operations Research from a historical or professional perspective. The second section contains nine articles whose theme is optimization and heuristic search, while the third section includes six articles in the general area of modeling and decision making. Collectively, these articles pay tribute to Saul Gass' major interests in the field of Operations Research.

This work deals with various gradient estimation techniques of perturbation analysis based on the use of conditional expectation. The primary setting is discrete-event stochastic simulation. This book presents applications to queueing and inventory, and to other diverse areas such as financial derivatives, pricing and statistical quality control. To researchers already in the area, this book offers a unified perspective and adequately summarizes the state of the art. To researchers new to the area, this book offers a more systematic and accessible means of understanding the techniques without having to scour through the immense literature and learn a new set of notation with each paper. To practitioners, this book provides a number of diverse application areas that makes the intuition accessible without having to fully commit to understanding all the theoretical niceties.

Includes a collection of chapters by some of the most distinguished researchers and practitioners in the field of mathematical finance and financial engineering. Presenting the developments in theory and practice, this book offers applications to fixed income models, credit risk models, CDO pricing, tax rebates, tax arbitrage, and tax equilibrium.

The goal of the Encyclopedia of Operations Research and Management Science is to provide decision makers and problem solvers in business, industry, government, and academia a comprehensive overview of the wide range of ideas, methodologies, and synergistic forces that combine to form the preeminent decision-aiding fields of operations research and management science (OR/MS). The impact of OR/MS on the quality of life and economic well being of everyone is a story that deserves to be told in its full detail and glory. The Encyclopedia of Operations Research and Management Science is the prologue to that story.The editors, working with the Encyclopedia’s Editorial Advisory Board, surveyed and divided OR/MS into specific topics that collectively encompass the foundations, applications, and emerging elements of this ever-changing field. We also wanted to establish the close associations that OR/MS has maintained with other scientific endeavors, with special emphasis on its symbiotic relationships with computer science, information systems, and mathematics. Based on our broad view of OR/MS, we enlisted a distinguished international group of academics andpractitioners to contribute articles on subjects for which they are renowned. We commissioned over 200 major expository articles and complemented them by numerous descriptions, discussions, definitions, and abbreviations. The connections between topics are highlighted by an entry’s final “See” statement, as appropriate. Each article provides a background or history of the topic, describes relevant applications, overviews present and future trends, and lists seminal and current references. To allow for variety in exposition, the authors were instructed to present their material from their research and applied perspectives. In particular, the authors, each of whom is a leading authority on the particular subject, were allowed to use whatever mathematical notation they felt was standard fortheir topics. The Encyclopedia’s intended audience is technically diverse and wide; it includes anyone concerned with the science, techniques, and ideas of how one makes decisions. As this audience encompasses many professions, educational background and skills, we were attentive to the form, format, and scope of the articles. Thus, the articles are designed to serve as initial sources of information for all such readers, with special emphasis on the needs of students.

Saul Gass has been a leading contributor to the field of Operations Research for more than 50 years. He has been affiliated with the Robert H. Smith School of Business at the University of Maryland for more than 30 years. On February 25, 2006, "Operations Research in the 21st Century: A Symposium in Honor of Professor Saul Gass' 80th Birthday," was held on our campus. Opening remarks by Deans Howard Prank and Rudy Lamone were followed by talks by Alfred Blumstein, Karla Hoffman, Richard Larson, Christoph Witzgall, Thomas Magnanti, Rakesh Vohra, and Bruce Golden. The celebration continued into the evening with dinner in the Executive Din­ ing Room of Van Munching Hall, followed by numerous toasts to Saul. It was a special day for all of us who were in attendance, but it was especially memorable for Saul and his family. This Festschrift companion to the Symposium includes articles from each of the Symposium distinguished speakers plus 16 other articles from friends, colleagues, and several of Saul's former students. The book is divided into three sections. The first section comprises eight articles focusing on the field of Operations Research from a historical or professional perspective. The second section contains nine articles whose theme is optimization and heuristic search, while the third section includes six articles in the general area of modeling and decision making. Collectively, these articles pay tribute to Saul Gass' major interests in the field of Operations Research.

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.  Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable.  In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function.  Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.

Conditional Monte Carlo: Gradient Estimation and Optimization Applications deals with various gradient estimation techniques of perturbation analysis based on the use of conditional expectation. The primary setting is discrete-event stochastic simulation. This book presents applications to queueing and inventory, and to other diverse areas such as financial derivatives, pricing and statistical quality control. To researchers already in the area, this book offers a unified perspective and adequately summarizes the state of the art. To researchers new to the area, this book offers a more systematic and accessible means of understanding the techniques without having to scour through the immense literature and learn a new set of notation with each paper. To practitioners, this book provides a number of diverse application areas that makes the intuition accessible without having to fully commit to understanding all the theoretical niceties. In sum, the objectives of this monograph are two-fold: to bring together many of the interesting developments in perturbation analysis based on conditioning under a more unified framework, and to illustrate the diversity of applications to which these techniques can be applied. Conditional Monte Carlo: Gradient Estimation and Optimization Applications is suitable as a secondary text for graduate level courses on stochastic simulations, and as a reference for researchers and practitioners in industry.

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.

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.