Advances in Sensitivity Analysis and Parametric Programming

Tomas Gal

ISBN 10: 079239917X / ISBN 13: 9780792399179

Published by Springer, Springer Mai 1997, 1997

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 608 pp. Englisch.

Seller Inventory # 9780792399179

Bibliographic Details

• Title: Advances in Sensitivity Analysis and Parametric Programming
• Publisher: Springer, Springer Mai 1997
• Publication Date: 1997
• Language: English
• ISBN 10: 079239917X
• ISBN 13: 9780792399179
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 37D millimeters 241 millimeters width by 160 millimeters height by 37 millimeters depth
• Weight: 1068 grams

About this title

Synopsis

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.

Review

`... well-organized book can be highly recommended to all who are engaged in operations research and decision making with incomplete information and/or multiple criteria. I do not hesitate to congratulate the two editors on their excellent work.'
OR Spektrum, 22 (2000)