Guides in the application of linear programming to firm decision making, with the goal of giving decision-makers a better understanding of methods at their disposal
Useful as a main resource or as a supplement in an economics or management science course, this comprehensive book addresses the deficiencies of other texts when it comes to covering linear programming theory―especially where data envelopment analysis (DEA) is concerned―and provides the foundation for the development of DEA.
Linear Programming and Resource Allocation Modeling begins by introducing primal and dual problems via an optimum product mix problem, and reviews the rudiments of vector and matrix operations. It then goes on to cover: the canonical and standard forms of a linear programming problem; the computational aspects of linear programming; variations of the standard simplex theme; duality theory; single- and multiple- process production functions; sensitivity analysis of the optimal solution; structural changes; and parametric programming. The primal and dual problems are then reformulated and re-examined in the context of Lagrangian saddle points, and a host of duality and complementary slackness theorems are offered. The book also covers primal and dual quadratic programs, the complementary pivot method, primal and dual linear fractional functional programs, and (matrix) game theory solutions via linear programming, and data envelopment analysis (DEA). This book:
- Appeals to those wishing to solve linear optimization problems in areas such as economics, business administration and management, agriculture and energy, strategic planning, public decision making, and health care
- Fills the need for a linear programming applications component in a management science or economics course
- Provides a complete treatment of linear programming as applied to activity selection and usage
- Contains many detailed example problems as well as textual and graphical explanations
Linear Programming and Resource Allocation Modeling is an excellent resource for professionals looking to solve linear optimization problems, and advanced undergraduate to beginning graduate level management science or economics students.
Michael J. Panik, PhD, is Professor Emeritus in the Department of Economics at the University of Hartford, CT. He has taught courses in economic and business statistics, quantitative decision methods, introductory and advanced quantitative methods, and econometrics. Dr. Panik is the author of several books, including Stochastic Differential Equations and Growth Curve Modeling: Theory and Applications, both published by Wiley. He is also a co-author of Introduction to Quantitative Methods in Business: With Applications Using Microsoft Office Excel.
