Search

This 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.

This fully revised 3rd edition offers an introduction to optimal control theory and its diverse applications in management science and economics. It brings to students the concept of the maximum principle in continuous, as well as discrete, time by using 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 faced in business and economics. The book exploits optimal control theory to the functional areas of management including finance, production and marketing and to economics of growth and of natural resources. In addition, this new edition features materials on stochastic Nash and Stackelberg differential games and an adverse selection model in the principal-agent framework. The book provides exercises for each chapter and answers to selected exercises to help deepen the understanding of the material presented. Also included are appendices comprised of supplementary material on the solution of differential equations, the calculus of variations and its relationships 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 a foundation for the book, which the author has applied to business management problems developed from his research and classroom instruction. The new edition has been completely refined and brought up to date. Ultimately this should continue to be a valuable resource for graduate courses on applied optimal control theory, but also for financial and industrial engineers, economists, and operational researchers concerned with the application of dynamic optimization in their fields.

The Spanish Conference of Industrial Engineering /Ingeniería de Organización Industrial (CIO) is an annual meeting promoted by Asociación para el Desarrollo de la Ingeniería de Organización/ Industrial Engineers Association (ADINGOR). The aim of CIO is to establish a forum for the open and free exchange of ideas, opinions and academic experiences about research, technology transfer or successful business experiences in the field of Industrial Engineering. The Scientific Committee is composed by 68 international referees and we foresee the attendance of some 200 people from more than 15 countries and following the rotation of venue and organization between various Spanish universities, the 2011 Conference will be the fifteenth National Conference and the fifth International Conference in Cartagena. During three days the 2011 Conference will include the participation of European and other foreign countries researchers and practitioners that will presenting communications, reproduced in this volume, on a range of topics including:Production and Operations Business Management Supply Chain Management Economic environment Technological and Organizational Innovation andManagement and Innovation in Education The Conference on Industrial Engineering (CIO) and its proceedings are an excellent platform for the dissemination of the outputs of the scientific projects developed in the frame of the European, national or regional Research and Development plans.

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.

Intense global competition in manufacturing has compelled manufacturers to incorporate repetitive processing and automation for improving productivity. Modern manufacturing systems use robotic cells – a particular type of computer-controlled system in cellular manufacturing. 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. After describing industrial applications of robotic cells and presenting fundamental results about cyclic production, several advanced features, such as dual-grippers, parallel machines, multi-part-type production, and multiple robots, are treated. Important open problems in the area are also identified. This book is an excellent text for use in a graduate course or a research seminar on robotic cells.

Inventory and Supply Chain Management with Forecast Updates is concerned with the problems of inventory and supply chain decision making with information updating over time. The models considered include inventory decisions with multiple sources and delivery modes, supply-contract design and evaluation, contracts with exercise price, volume-flexible contracts allowing for spot-market purchase decisions, and competitive supply chains. Real problems are formulated into tractable mathematical models, which allow for an analysis of various approaches, and provide insights for better supply chain management. The book provides a unified treatment of these models, presents a critique of the existing results, and points out potential research directions. Attention is focused on solutions – that is, inventory decisions prior and subsequent to information updates and the impact of the quality of information on these decisions.