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...
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 S...
The Applied and Numerical Harmonic Analysis (ANHA) book series aims to provide the engineering, mathematical, and scienti?c communities with s- ni?cant developments in harmonic analysis, ranging from abstract harmonic analysis to basic applications. The title of the series re?ects the importance of applications and numerical implementation, but richness and relevance of applications and implementation depend fundamentally on the structure and depth of theoretical underpinnings. Thus, from our point of view, the int- leaving of theory and applications and their creative symbiotic evolution is...
The Applied and Numerical Harmonic Analysis (ANHA) book series aims to provide the engineering, mathematical, and scienti?c communities with s- ni?can...
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. This book brings the state-of-the-art research together for the first time. It provides practical modeling methods for many real-world problems with high dimensionality or complexity which have not hitherto been treatable with Markov decision processes.
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, compute...
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. The Encyclopedia of Operations Research and Management Science is the prologue to that story.
The editors,...
The goal of the Encyclopedia of Operations Research and Management Science is to provide decision makers and problem solvers in business, in...
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...
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 S...
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...
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer s...
Conditional Monte Carlo: Gradient Estimation and OptimizationApplications 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...
Conditional Monte Carlo: Gradient Estimation and OptimizationApplications deals with various gradient estimation techniques of pert...
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...
The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the m...
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...
The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the m...
