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This volume contains a selection of papers referring to lectures presented at the symposium Operations Research 2006 held at the University of Karlsruhe. The symposium presented the state of the art in Operations Research and related areas in Economics, Mathematics, and Computer Science and demonstrated the broad applicability of its core themes, placing particular emphasis on Basel II, one of the most topical challenges of Operations Research.
This book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book’s closing section, several case studies are presented, helping students apply the theory covered to practical problems. The book is based on lecture notes developed for an Econometrics and Operations Research course for master students at the University of Groningen, the Netherlands - the longest-standing Stochastic Programming course worldwide.
Up to now, demand fulfillment in make-to-stock manufacturing is usually handled by advanced planning systems. Orders are fulfilled on the basis of simple rules or deterministic planning approaches not taking into account demand fluctuations. The consideration of different customer classes as it is often done today requires more sophisticated approaches explicitly considering stochastic influences. This book reviews current literature, presents a framework that addresses revenue management and demand fulfillment at once and introduces new stochastic approaches for demand fulfillment in make-to-stock manufacturing based on the ideas of the revenue management literature.
This book constitutes the joint refereed proceedings of the 8th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2005 and the 9th International Workshop on Randomization and Computation, RANDOM 2005, held in Berkeley, CA, USA in August 2005. The volume contains 41 carefully reviewed papers, selected by the two program committees from a total of 101 submissions. Among the issues addressed are design and analysis of approximation algorithms, hardness of approximation, small space and data streaming algorithms, sub-linear time algorithms, embeddings and metric space methods, mathematical programming methods, coloring and partitioning, cuts and c...
Inventory Management in Multi-Echelon Networks presents methods to plan inventory in distribution networks. By holistically looking at the supply chain, it shows how safety stocks across all echelons can be optimized if inventory of all levels is taken into consideration. The gap between the existence of advanced inventory planning methods and their low penetration in the industry was the motivation for this book. Christopher Grob develops essential algorithms that companies can use for network inventory planning and highlights achievable implementation benefits. The work of the author was inspired by the needs of an after sales supply chain of a large automotive company. This company suppli...
No detailed description available for "Proceedings of the Seventh Conference on Probability Theory".
With 1901/1910-1956/1960 Repertoium is bound: Brinkman's Titel-catalohus van de gedurende 1901/1910-1956/1960 (Title varies slightly).
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Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering.