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{ "item_title" : "Advances in Sensitivity Analysis and Parametric Programming", "item_author" : [" Tomas Gal", "H. J. Greenberg "], "item_description" : "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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/1/46/137/796/146137796X_b.jpg", "price_data" : { "retail_price" : "279.99", "online_price" : "279.99", "our_price" : "279.99", "club_price" : "279.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Advances in Sensitivity Analysis and Parametric Programming|Tomas Gal

Advances in Sensitivity Analysis and Parametric Programming

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Overview

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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Details

  • ISBN-13: 9781461377962
  • ISBN-10: 146137796X
  • Publisher: Springer
  • Publish Date: October 2012
  • Dimensions: 9.21 x 6.14 x 1.23 inches
  • Shipping Weight: 1.85 pounds
  • Page Count: 581

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