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 40
The conclusion was that proportionality could indeed be assumed without
serious distortion . For other problems , what happens when the proportionality
assumption does not hold even as a reasonable approximation ? In most cases ,
this ...
The conclusion was that proportionality could indeed be assumed without
serious distortion . For other problems , what happens when the proportionality
assumption does not hold even as a reasonable approximation ? In most cases ,
this ...
Page 42
5x1x2 , so the total function value is 6 + 6 + 3 = 15 when ( x1 , x2 ) = ( 2 , 3 ) ,
which violates the additivity assumption that the value is just 6 + 6 = 12 . This
case can arise in exactly the same way as described for Case 2 in Table 3 . 5 ;
namely ...
5x1x2 , so the total function value is 6 + 6 + 3 = 15 when ( x1 , x2 ) = ( 2 , 3 ) ,
which violates the additivity assumption that the value is just 6 + 6 = 12 . This
case can arise in exactly the same way as described for Case 2 in Table 3 . 5 ;
namely ...
Page 43
Certainty Our last assumption concerns the parameters of the model , namely ,
the coefficients in the objective function cj , the ... Certainty assumption : The
value assigned to each parameter of a linear programming model is assumed to
be a ...
Certainty Our last assumption concerns the parameters of the model , namely ,
the coefficients in the objective function cj , the ... Certainty assumption : The
value assigned to each parameter of a linear programming model is assumed to
be a ...
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Contents
SUPPLEMENT TO APPENDIX 3 | 3 |
Problems | 6 |
An Algorithm for the Assignment Problem | 18 |
Copyright | |
59 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 constraints Construct corresponding cost CPF solution decision variables demand described determine distribution dual problem entering equal equations estimates example feasible 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 nonnegative 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 transportation 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 |