Operations Research Algorithms And Applications
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Operations Research
The market-leading textbook for the course, Winston's OPERATIONS RESEARCH owes much of its success to its practical orientation and consistent emphasis on model formulation and model building. It moves beyond a mere study of algorithms without sacrificing the rigor that faculty desire. As in every edition, Winston reinforces the book's successful features and coverage with the most recent developments in the field. The Student Suite CD-ROM, which now accompanies every new copy of the text, contains the latest versions of commercial software for optimization, simulation, and decision analysis.
Operations Research: Algorithms And Applications
Author: Rathindra P. Sen
language: en
Publisher: PHI Learning Pvt. Ltd.
Release Date: 2010-01-30
It covers all the relevant topics along with the recent developments in the field. The book begins with an overview of operations research and then discusses the simplex method of optimization and duality concept along with the deterministic models such as post-optimality analysis, transportation and assignment models. While covering hybrid models of operations research, the book elaborates PERT (Programme Evaluation and Review Technique), CPM (Critical Path Method), dynamic programming, inventory control models, simulation techniques and their applications in mathematical modelling and computer programming. It explains the decision theory, game theory, queueing theory, sequencing models, replacement and reliability problems, information theory and Markov processes which are related to stochastic models. Finally, this well-organized book describes advanced deterministic models that include goal programming, integer programming and non-linear programming.
Monte Carlo
Author: George Fishman
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-03-09
This book provides an introduction to the Monte Carlo method suitable for a one-or two-semester course for graduate and advanced undergraduate students in the mathematical and engineering sciences. It also can serve as a reference for the professional analyst. In the past, my inability to provide students with a single source book on this topic for class and for later professional reference had left me repeatedly frustrated, and eventually motivated me to write this book. In addition to focused accounts of major topics, the book has two unifying themes: One concerns the effective use of information and the other concerns error control and reduction. The book describes how to incorporate information about a problem into a sampling plan in a way that reduces the cost of estimating its solution to within a specified error bound. Although exploiting special structures to reduce cost long has been a hallmark of the Monte Carlo method, the propen sity of users of the method to discard useful information because it does not fit traditional textbook models repeatedly has impressed me. The present account aims at reducing the impediments to integrating this information. Errors, both statistical and computational, abound in every Monte Carlo sam pling experiment, and a considerable methodology exists for controlling them.