Foreword. Preface. 1. A Historical Sketch on Sensitivity Analysis and Parametric Programming; T. Gal. 2. A Systems Perspective: Entity Set Graphs; H. Müller-Merbach. 3. Linear Programming 1: Basic Principles; H.J. Greenberg. 4. Linear Programming 2: Degeneracy Graphs; T. Gal. 5. Linear Programming 3: The Tolerance Approach; R.E. Wendell. 6. The Optimal Set and Optimal Partition Approach; A.B. Berkelaar, et al. 7. Network Models; G.L. Thompson. 8. Qualitative Sensitivity Analysis; A. Gautier, et al. 9. Integer and Mixed-Integer Programming; C. Blair. 10. Nonlinear Programming; A.S. Drud, L. Lasdon. 11. Multi-Criteria and Goal Programming; J. Dauer, Yi-Hsin Liu. 12. Stochastic Programming and Robust Optimization; H. Vladimirou, S.A. Zenios. 13. Redundancy; R.J. Caron, et al. 14. Feasibility and Viability; J.W. Chinneck. 15. Fuzzy Mathematical Programming; H.-J. Zimmermann. Subject Index.
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.
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