Introduction to Practical Linear Programming - Softcover

Pannell, David J.

 
9780471517894: Introduction to Practical Linear Programming

Synopsis

This book discusses linear programming, an accessible technique widely used by non–mathematicians such as business managers, consultants, social scientists, and applied economists. This comprehensive guide to applying the linear programming model in real–world situations emphasizes practical applications of linear programming in a simplified, non–theoretical manner without requiring knowledge of the underlying mathematics.

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About the Author

DAVID J. PANNELL, PhD, is Senior Lecturer at the University of Western Australia. The winner of numerous academic awards, he is the coeditor of MIDAS, A Bioeconomic Model of a Dryland Farm

From the Back Cover

A comprehensive, nonmathematical guide to the practical application of linear programming models for students and professionals in any field

From finding the least–cost method for manufacturing a given product to determining the most profitable use for a given resource, there are countless practical applications for linear programming models. This self–contained book and disk set provides everything you need to know to apply linear programming to real–world situations how to prepare input, how to interpret output, what to do if the model will not solve, and how to make your results useful and usable while entrusting the hard–core arithmetic to the user–friendly computer package on disk. Written in clear prose that stays away from the complex mathematics underlying the technique, Introduction to Practical Linear Programming contains:

  • A complete introduction to problem structure, assumptions, applications, and other core concepts
  • A detailed, step–by–step guide to model construction (from a problem description to a useful model) and interpretation of output
  • Linear programming examples and exercises from a range of real–life areas, including agriculture, manufacturing, finance, and advertising
  • Important techniques for troubleshooting and error identification
  • Procedures for testing how good your model is how robust are the results? and more
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