Advances in Sensitivity Analysis and Parametric Programming

. Ed(s): Gal, Tomas; Greenberg, Harvey J.

ISBN 10: 079239917X / ISBN 13: 9780792399179

Published by Kluwer Academic Publishers, 1997

About this Item

Presents qualitative sensitivity analysis, as well as elements of mixed integer programming and gives a perspective of nonlinear programming. This book shows results using the optimal partition approach, stemming from interior methods, for both linear and quadratic programming. It also examines the special case of network models. Editor(s): Gal, Tomas; Greenberg, Harvey J. Series: International Series in Operations Research & Management Science. Num Pages: 581 pages, biography. BIC Classification: KJMD; KJT. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 33. Weight in Grams: 1023. . 1997. Hardback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780792399179

Bibliographic Details

• Title: Advances in Sensitivity Analysis and Parametric Programming
• Publisher: Kluwer Academic Publishers
• Publication Date: 1997
• Language: English
• ISBN 10: 079239917X
• ISBN 13: 9780792399179
• Binding: Hardcover
• Condition: New

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)