Introduction to Operations ResearchCD-ROM contains: Student version of MPL Modeling System and its solver CPLEX -- MPL tutorial -- Examples from the text modeled in MPL -- Examples from the text modeled in LINGO/LINDO -- Tutorial software -- Excel add-ins: TreePlan, SensIt, RiskSim, and Premium Solver -- Excel spreadsheet formulations and templates. |
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Page 556
... possible states to consider . We now have an infinite number of possible states ( 240 ≤ s3 ≤ 255 ) , so it is no longer feasible to solve separately for x for each possible value of $ 3 . Therefore , we instead have solved for x3 as a ...
... possible states to consider . We now have an infinite number of possible states ( 240 ≤ s3 ≤ 255 ) , so it is no longer feasible to solve separately for x for each possible value of $ 3 . Therefore , we instead have solved for x3 as a ...
Page 587
... possible constraints such that only some K of these constraints must hold . ( Assume that K < N. ) Part of the opti- mization process is to choose the combination of K constraints that permits the objective function to reach its best ...
... possible constraints such that only some K of these constraints must hold . ( Assume that K < N. ) Part of the opti- mization process is to choose the combination of K constraints that permits the objective function to reach its best ...
Page 751
... possible situations is referred to as a possible state of nature . For each combination of an action and a state of nature , the decision maker knows what the resulting payoff would be . The payoff is a quantitative measure of the value ...
... possible situations is referred to as a possible state of nature . For each combination of an action and a state of nature , the decision maker knows what the resulting payoff would be . The payoff is a quantitative measure of the value ...
Other editions - View all
Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
activity algebraic algorithm allowable range artificial variables b₂ basic solution c₁ c₂ changes coefficients column Consider the following cost Courseware CPLEX decision variables described dual problem dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau flow following problem formulation functional constraints Gaussian elimination given graphical identify increase initial BF solution integer iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize Z maximum flow problem Minimize needed node nonbasic variables nonnegativity constraints objective function obtained optimal solution optimality test parameters path plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices shown simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero
Bibliographic information
| Title | Introduction to Operations Research McGraw-Hill International Editions McGraw-Hill industrial & plant engineering series McGraw-Hill series in industrial engineering and management science |
| Authors | Frederick S. Hillier, Gerald J. Lieberman |
| Edition | 7, illustrated |
| Publisher | McGraw-Hill, 2001 |
| Original from | Pennsylvania State University |
| Digitized | 26 Aug 2009 |
| ISBN | 0071181636, 9780071181631 |
| Length | 1214 pages |
| Export Citation | BiBTeX EndNote RefMan |