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 574
Use dynamic programming to solve this problem . 11.3-18 . Consider the following nonlinear programming problem . Minimize Z = x + 2x subject to x } + xż z 2 . ( There are no nonnegativity constraints . ) Use dynamic programming to solve ...
Use dynamic programming to solve this problem . 11.3-18 . Consider the following nonlinear programming problem . Minimize Z = x + 2x subject to x } + xż z 2 . ( There are no nonnegativity constraints . ) Use dynamic programming to solve ...
Page 683
In the special case where the primal problem is a linear programming problem , the x ; variables drop out of the dual problem and it becomes the familiar dual problem of linear programming ( where the U ; variables here correspond to ...
In the special case where the primal problem is a linear programming problem , the x ; variables drop out of the dual problem and it becomes the familiar dual problem of linear programming ( where the U ; variables here correspond to ...
Page 714
Consider the following quadratic programming problem : Maximize f ( x ) = 8x ; – xỉ + 4x2 – xż , subject to x + x2 5 2 and X1 20 , * 2 20 . ( a ) Use the KKT conditions to derive an optimal solution . ( b ) Now suppose that this problem ...
Consider the following quadratic programming problem : Maximize f ( x ) = 8x ; – xỉ + 4x2 – xż , subject to x + x2 5 2 and X1 20 , * 2 20 . ( a ) Use the KKT conditions to derive an optimal solution . ( b ) Now suppose that this problem ...
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Contents
SUPPLEMENT TO APPENDIX 3 | 3 |
Problems | 6 |
SUPPLEMENT TO CHAPTER | 18 |
Copyright | |
52 other sections not shown
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Common terms and phrases
activity additional algorithm allocation allowable amount apply assignment basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider constraint Construct corresponding cost CPF solution decision variables demand described determine distribution dual problem entering equal equations estimates example feasible feasible region feasible solutions FIGURE final flow formulation functional constraints given gives goal identify illustrate increase indicates initial iteration linear programming Maximize million Minimize month needed node nonbasic variables objective function obtained operations optimal optimal solution original parameters path plant possible presented primal problem Prob procedure profit programming problem provides range remaining resource respective resulting revised shown shows side simplex method simplex tableau slack solve step supply Table tableau tion unit values weeks Wyndor Glass 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 |