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Advances in Sensitivity Analysis and Parametric Programming [Hardcover]

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  • Category: Books (Mathematics)
  • ISBN-10:  079239917X
  • ISBN-10:  079239917X
  • ISBN-13:  9780792399179
  • ISBN-13:  9780792399179
  • Publisher:  Springer
  • Publisher:  Springer
  • Pages:  581
  • Pages:  581
  • Binding:  Hardcover
  • Binding:  Hardcover
  • Pub Date:  01-Feb-1997
  • Pub Date:  01-Feb-1997
  • SKU:  079239917X-11-SPRI
  • SKU:  079239917X-11-SPRI
  • Item ID: 100711896
  • List Price: $279.99
  • Seller: ShopSpell
  • Ships in: 5 business days
  • Transit time: Up to 5 business days
  • Delivery by: Jul 03 to Jul 05
  • Notes: Brand New Book. Order Now.
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.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 (deterlÃj
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