Linear Programming and its Applications

Based on earlier work by a variety of authors in the 1930s and 1940s, the simplex method for solving linear programming problems was developed in 1947 by the American mathematician George B. Dantzig. Helped by the computer revolution, it has been described by some as the overwhelmingly most significant mathematical development of the last century. Owing to the simplex method, linear programming (or linear optimization, as some would have it) is pervasive in modern society for the planning and control of activities that are constrained by the availability of resources such as manpower, raw materials, budgets, and time. The purpose of this book is to describe the field of linear programming. While we aim to be reasonably complete in our treatment, we have given emphasis to the modeling aspects of the field. Accordingly, a number of applications are provided, where we guide the reader through the interactive process of mathematically modeling a particular practical situation, analyzing the consequences of the model formulated, and then revising the model in light of the results from the analysis.

Extended chapter on all major areas of applications Detailed managerial sensitivity analyses With a small and fully formulated case study and an extended chapter that deals with models that incorporate multiple objectives Reader will be able to understand the major phases of the process: the formulation, solution, and the interpretation of the results Includes supplementary material: sn.pub/extras

Texte du rabat
This book presents a unified treatment of linear programming. Without sacrificing mathematical rigor, the main emphasis of the book is on models and applications. The most important classes of problems are surveyed and presented by means of mathematical formulations, followed by solution methods and a discussion of a variety of "what-if" scenarios. Non-simplex based solution methods and newer developments such as interior point methods are covered along with a variety of approaches that incorporate multiple objectives in the model.

Contenu
Linear Algebra.- Computational Complexity.- Applications.- The Simplex Method.- Duality.- Extensions of the Simplex Method.- Postoptimality Analyses.- Non-Simplex Based Solution Methods.- Problem Reformulations.- Multiobjective Programming.