This book articulates a new theory that shows that hierarchical decision making can in fact lead to a near optimization of system goals. The material in the book cuts across disciplines. It will appeal to graduate students and researchers in applied mathematics, operations management, operations research, and system and control theory.

Average-Cost Control of Stochastic Manufacturing Systems (Stochastic Modelling and Applied Probability, 54)

Sethi, Suresh P.

张青

Zhang, Han-Qin

Reference

Springer

Optimal Control Theory: Applications to Management Science and Economics

Optimal control methods are used to determine optimal ways to control a dynamic system. The theoretical work in this field serves as a foundation for the book, which the authors have applied to business management problems developed from their research and classroom instruction. Sethi and Thompson have provided management science and economics communities with a thoroughly revised edition of their classic text on Optimal Control Theory. The new edition has been completely refined with careful attention to the text and graphic material presentation. Chapters cover a range of topics including finance, production and inventory problems, marketing problems, machine maintenance and replacement, problems of optimal consumption of natural resources, and applications of control theory to economics. The book contains new results that were not available when the first edition was published, as well as an expansion of the material on stochastic optimal control theory.

Over the past decades, health care delivery - once characterized by clearly defined relationships between medical professionals, regulators, and industr- has become much more complex. Traditional roles and relationships are changing and new decision-makers are entering the scene. They bring into the discussion the need for cost containment and the changing perceptions of the public with regard to the risks and benefits of health care outcomes. As benefits of medical care are increasingly questioned and as scepticism and fear of advanced technologies are demonstrated by a growing body of people, decision making must move beyond medical concerns to encompass economic, political, and sociological considera­ tions. This evolution has also affected the pharmaceutical industry. Public percep­ tion of responsibility for product safety is no longer limited to regulators and physi­ cians. Manufacturers, previously unencumbered by social policies, governmental preoccupations, or public expectations, are now held accountable for product safety. As a certain amount of risk acceptance is, however, a prerequisite for the further development of health care goods, it has to be carefully balanced against society'S demand for safety. To weigh risks against benefits, to define acceptable risk, and to exclude what no longer falls within this category remain perpetual challenges. Accordingly, health care outcomes have to be assessed not only from a clinical point of view, but also from an economic and societal perspective. The assessment of drug risks and benefits has become an important political issue world-wide.

Inventory and Supply Chain Management with Forecast Updates

Science & Math

Yan, Houmin

Zhang, Hanqin

Biological Sciences

Throughput Optimization In Robotic Cells provides practitioners, researchers, and students with up-to-date algorithmic results on sequencing of robot moves and scheduling of parts in robotic cells. It brings together the structural results developed over the last 25 years for the various realistic models of robotic cells. This book is ideally suited for use in a graduate course or a research seminar on robotic cells.

Throughput Optimization in Robotic Cells (International Series in Operations Research & Management Science, 101)

Computers & Technology

Dawande, Milind W.

Geismar, H. N.

Computer Science

Springer, Berlin

Markovian Demand Inventory Models (International Series in Operations Research & Management Science, 108)

This text provides a superbly researched insight into Markovian demand inventory models. The result of ten years of research, this work covers all aspects of demand inventory where they are modeled by Markov processes. Inventory management is concerned with matching supply with demand and is a central problem in Operations Management. The central problem is to find the amount to be produced or purchased in order to maximize the total expected profit, or minimize the total expected cost.

Business & Money

Beyer, Dirk

Cheng, Feng

Taksar, Michael

Management & Leadership

Real problems are formulated into tractable mathematical models, which allow for an analysis of various approaches. Attention is focused on solutions. Provides a unified treatment of the models discussed , presents a critique of the existing results, and points out potential research directions.

Inventory and Supply Chain Management with Forecast Updates (International Series in Operations Research & Management Science, 81)

Economics

Inventory management is concerned with matching supply with demand and a central problem in Operations Management. The problem is to find the amount to be produced or purchased in order to maximize the total expected profit or minimize the total expected cost. Over the past two decades, several variations of the formula appeared, mostly in trade journals written by and for inventory managers. A critical assumption in the inventory literature is that the demands in different periods are independent and identically distributed. However, in real life, demands may depend on environmental considerations or the events in the world such as the weather, the state of economy, etc. Moreover, these events are represented by stochastic processes - exogenous or controlled. In Markovian Demand Inventory Models, the authors are concerned with inventory models where these world events are modeled by Markov processes. Their research on Markovian demand inventory models was carried out over a period of ten years beginning in the early nineties.

From the Back Cover\nThis new 4th edition offers an introduction to optimal control theory and its diverse applications in management science and economics. It introduces students to the concept of the maximum principle in continuous (as well as discrete) time by combining dynamic programming and Kuhn-Tucker theory. While some mathematical background is needed, the emphasis of the book is not on mathematical rigor, but on modeling realistic situations encountered in business and economics. It applies optimal control theory to the functional areas of management including finance, production and marketing, as well as the economics of growth and of natural resources. In addition, it features material on stochastic Nash and Stackelberg differential games and an adverse selection model in the principal-agent framework. 
Exercises are included in each chapter, while the answers to selected exercises help deepen readers’ understanding of the material covered. Also included are appendices of supplementary material on the solution of differential equations, the calculus of variations and its ties to the maximum principle, and special topics including the Kalman filter, certainty equivalence, singular control, a global saddle point theorem, Sethi-Skiba points, and distributed parameter systems. 
Optimal control methods are used to determine optimal ways to control a dynamic system. The theoretical work in this field serves as the foundation for the book, in which the author applies it to business management problems developed from his own research and classroom instruction. The new edition has been refined and updated, making it a valuable resource for graduate courses on applied optimal control theory, but also for financial and industrial engineers, economists, and operational researchers interested in applying dynamic optimization in their fields.\nThis new 4th edition offers an introduction to optimal control theory and its diverse applications in management science and economics. It introduces students to the concept of the maximum principle in continuous (as well as discrete) time by combining dynamic programming and Kuhn-Tucker theory. While some mathematical background is needed, the emphasis of the book is not on mathematical rigor, but on modeling realistic situations encountered in business and economics. It applies optimal control theory to the functional areas of management including finance, production and marketing, as well as the economics of growth and of natural resources. In addition, it features material on stochastic Nash and Stackelberg differential games and an adverse selection model in the principal-agent framework. 
Exercises are included in each chapter, while the answers to selected exercises help deepen readers’ understanding of the material covered. Also included are appendices of supplementary material on the solution of differential equations, the calculus of variations and its ties to the maximum principle, and special topics including the Kalman filter, certainty equivalence, singular control, a global saddle point theorem, Sethi-Skiba points, and distributed parameter systems. 
Optimal control methods are used to determine optimal ways to control a dynamic system. The theoretical work in this field serves as the foundation for the book, in which the author applies it to business management problems developed from his own research and classroom instruction. The new edition has been refined and updated, making it a valuable resource for graduate courses on applied optimal control theory, but also for financial and industrial engineers, economists, and operational researchers interested in applying dynamic optimization in their fields.

Optimal Control Theory: Applications to Management Science and Economics (Springer Texts in Business and Economics)

See the best price to sell, buy, or rent books by Sethi, Suresh P.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Sethi, Suresh P.

See the best price to sell, buy, or rent books by Sethi, Suresh P.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Average-Cost Control of Stochastic Manufacturing Systems (Stochastic Modelling and Applied Probability, 54)

Optimal Control Theory: Applications to Management Science and Economics

Inventory and Supply Chain Management with Forecast Updates

Throughput Optimization in Robotic Cells (International Series in Operations Research & Management Science, 101)

Markovian Demand Inventory Models (International Series in Operations Research & Management Science, 108)

Inventory and Supply Chain Management with Forecast Updates (International Series in Operations Research & Management Science, 81)

Markovian Demand Inventory Models (International Series in Operations Research & Management Science, 108)

Optimal Control Theory: Applications to Management Science and Economics (Springer Texts in Business and Economics)