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. |
From inside the book
Results 1-3 of 55
Page 5
... infeasible alternative , leaving the automatic machine as the only feasible choice . Finally , for batch sizes greater than 200 units , both alternatives are infeasible . Exercise 1.2-1 Determine the optimum decision in Figure 1-2 ...
... infeasible alternative , leaving the automatic machine as the only feasible choice . Finally , for batch sizes greater than 200 units , both alternatives are infeasible . Exercise 1.2-1 Determine the optimum decision in Figure 1-2 ...
Page 90
... INFEASIBLE SOLUTION If the constraints cannot be satisfied simultaneously , the model is said to have no feasible solution . This situation can never occur if all the constraints are of the type ( assuming nonnegative right - side ...
... INFEASIBLE SOLUTION If the constraints cannot be satisfied simultaneously , the model is said to have no feasible solution . This situation can never occur if all the constraints are of the type ( assuming nonnegative right - side ...
Page 142
... infeasible . 2. The current solution can become nonoptimal . † The two categories are based on the results of the primal - dual computations presented in Section 4.2 . If you review them again at this point , you will discover the ...
... infeasible . 2. The current solution can become nonoptimal . † The two categories are based on the results of the primal - dual computations presented in Section 4.2 . If you review them again at this point , you will discover the ...
Contents
LINEAR INTEGER AND DYNAMIC | 23 |
Algebraic | 64 |
Special Cases in Simplex Method Application | 84 |
Copyright | |
29 other sections not shown
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