The standard view of Operations Research/Management Science (OR/MS) dichotomizes the field into deterministic and probabilistic (nondeterministic, stochastic) subfields. This division can be seen by reading the contents page of just about any OR/MS textbook. The mathematical models that help to define OR/MS are usually presented in terms of one subfield or the other. This separation comes about somewhat artificially: academic courses are conveniently subdivided with respect to prerequisites; an initial overview of OR/MS can be presented without requiring knowledge of probability and statistics; text books are conveniently divided into two related semester courses, with deterministic models coming first; academics tend to specialize in one subfield or the other; and practitioners also tend to be expert in a single subfield. But, no matter who is involved in an OR/MS modeling situation (deterministic or probabilistic - academic or practitioner), it is clear that a proper and correct treatment of any problem situation is accomplished only when the analysis cuts across this dichotomy.
Author(s): Tomas Gal (auth.), Tomas Gal, Harvey J. Greenberg (eds.)
Series: International Series in Operations Research & Management Science 6
Edition: 1
Publisher: Springer US
Year: 1997
Language: English
Pages: 581
Tags: Operation Research/Decision Theory; Calculus of Variations and Optimal Control; Optimization; Manufacturing, Machines, Tools
Front Matter....Pages i-xxiii
A Historical Sketch on Sensitivity Analysis and Parametric Programming....Pages 1-10
A Systems Perspective: Entity Set Graphs....Pages 11-55
Linear Programming 1: Basic Principles....Pages 57-100
Linear Programming 2: Degeneracy Graphs....Pages 101-136
Linear Programming 3: The Tolerance Approach....Pages 137-157
The Optimal Set and Optimal Partition Approach to Linear and Quadratic Programming....Pages 159-202
Network Models....Pages 203-236
Qualitative Sensitivity Analysis....Pages 237-289
Integer and Mixed-Integer Programming....Pages 291-315
Nonlinear Programming....Pages 317-361
Multi-Criteria and Goal Programming....Pages 363-393
Stochastic Programming and Robust Optimization....Pages 395-447
Redundancy....Pages 449-489
Feasibility and Viability....Pages 491-531
Fuzzy Mathematical Programming....Pages 533-572
Back Matter....Pages 573-581