Introduction to the Mathematics of Operations Research with Mathematica®

2nd Edition

Kevin J. Hastings

Chapman and Hall/CRC
Published May 30, 2006
Textbook - 592 Pages
ISBN 9781574446128 - CAT# DK3895


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  • Integrates suggestions and self-check questions throughout the text, making the book much more interactive
  • Employs a software package, Mathematica, in order to expose new issues and possibilities, enhance users' desire to experiment, and reduce the computational burden
  • Introduces the Traveling Salesman problem and other famous graph theory problems, and covers Brownian motion
  • Includes longer investigations that promote the development of problem-solving techniques
  • Deploys Mathematica tools to execute probabilistic computations regarding Poisson processes and queues
  • Applies Mathematica's symbolic algebra ability to solve dynamic programming problems, refocusing attention on the modeling aspect of these problems
  • Summary

    The breadth of information about operations research and the overwhelming size of previous sources on the subject make it a difficult topic for non-specialists to grasp. Fortunately, Introduction to the Mathematics of Operations Research with Mathematica®, Second Edition delivers a concise analysis that benefits professionals in operations research and related fields in statistics, management, applied mathematics, and finance.

    The second edition retains the character of the earlier version, while incorporating developments in the sphere of operations research, technology, and mathematics pedagogy. Covering the topics crucial to applied mathematics, it examines graph theory, linear programming, stochastic processes, and dynamic programming. This self-contained text includes an accompanying electronic version and a package of useful commands. The electronic version is in the form of Mathematica notebooks, enabling you to devise, edit, and execute/reexecute commands, increasing your level of comprehension and problem-solving.

    Mathematica sharpens the impact of this book by allowing you to conveniently carry out graph algorithms, experiment with large powers of adjacency matrices in order to check the path counting theorem and Markov chains, construct feasible regions of linear programming problems, and use the "dictionary" method to solve these problems. You can also create simulators for Markov chains, Poisson processes, and Brownian motions in Mathematica, increasing your understanding of the defining conditions of these processes. Among many other benefits, Mathematica also promotes recursive solutions for problems related to first passage times and absorption probabilities.