Operations Research: An IntroductionSignificantly revised, this book provides balanced coverage of the theory, applications, and computations of operations research. The applications and computations in operations research are emphasized. Significantly revised, this text streamlines the coverage of the theory, applications, and computations of operations research. Numerical examples are effectively used to explain complex mathematical concepts. A separate chapter of fully analyzed applications aptly demonstrates the diverse use of OR. The popular commercial and tutorial software AMPL, Excel, Excel Solver, and Tora are used throughout the book to solve practical problems and to test theoretical concepts. New materials include Markov chains, TSP heuristics, new LP models, and a totally new simplex-based approach to LP sensitivity analysis. |
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Page 35
... Maximum Change in Level of Resource ( tons ) 1 Scarce 2 Scarce 3 Abundant 4 Abundant 1-2 = 7-6 = + 1 12-8 = + 4 -2-1 = -3 Maximum Change in Revenue z ( thousands of dollars ) 13-123 = + 18-12 = +5 123 - 123 = 0 123-123 = 0 Exercise 2.1 ...
... Maximum Change in Level of Resource ( tons ) 1 Scarce 2 Scarce 3 Abundant 4 Abundant 1-2 = 7-6 = + 1 12-8 = + 4 -2-1 = -3 Maximum Change in Revenue z ( thousands of dollars ) 13-123 = + 18-12 = +5 123 - 123 = 0 123-123 = 0 Exercise 2.1 ...
Page 449
... maximum losses . Again , the maximum is taken over all A's strategies . The following example illustrates the computations of the minimax and maxi- min values of a game . Example 11.4-2 . Consider the following payoff matrix , which ...
... maximum losses . Again , the maximum is taken over all A's strategies . The following example illustrates the computations of the minimax and maxi- min values of a game . Example 11.4-2 . Consider the following payoff matrix , which ...
Page 744
... maximum if the value of ƒ at every point in the neighborhood of Xo does not exceed ƒ ( Xo ) . In a similar manner ... maximum , and ƒ ( x1 ) and f ( x3 ) are local or relative maxima . Similarly , f ( x4 ) is a local minimum and ƒ ( x2 ) ...
... maximum if the value of ƒ at every point in the neighborhood of Xo does not exceed ƒ ( Xo ) . In a similar manner ... maximum , and ƒ ( x1 ) and f ( x3 ) are local or relative maxima . Similarly , f ( x4 ) is a local minimum and ƒ ( x2 ) ...
Contents
LINEAR INTEGER AND DYNAMIC | 23 |
Algebraic | 64 |
Special Cases in Simplex Method Application | 84 |
Copyright | |
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Common terms and phrases
algorithm applied associated assuming b₁ basic solution basic variables c₁ Chapter coefficients column computations constraints corresponding cost per unit criterion critical path decision decision problem defined demand determine distribution dual simplex dynamic programming entering variable equal Example expected expected value exponential distribution feasible solution Figure Formulate given holding cost infeasible integer programming inventory model iteration leaving variable linear programming LP model Markov chain mathematical matrix maximize z maximum Mikks minimax minimize mixed cut node nonbasic variables nonnegative objective function objective value obtained optimal solution optimum P₁ period Poisson distribution primal probability procedure production profit pure strategies random variable recursive equation represents result satisfied schedule Section selected setup cost shortage simplex method slack variables solution space Solve stage strategies subject to maximize subproblems summarized Suppose t₁ Table transportation model vector x₁ y₁ yield z-transform zero