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 54
( Try it and see if you first obtain the following inappropriate choice of decision variables . ) ... decisions is the amount of each product grade to produce , it would seem natural to define one set of decision variables accordingly .
( Try it and see if you first obtain the following inappropriate choice of decision variables . ) ... decisions is the amount of each product grade to produce , it would seem natural to define one set of decision variables accordingly .
Page 75
With 10 plants , 10 machines , 10 products , and 10 months , this gives a total of 21,000 decision variables , as outlined below . Decision Variables . 10,000 production variables : one for each combination of a plant , machine ...
With 10 plants , 10 machines , 10 products , and 10 months , this gives a total of 21,000 decision variables , as outlined below . Decision Variables . 10,000 production variables : one for each combination of a plant , machine ...
Page 243
Any problem Primal Variable ( Decision variable ) x ; ( Slack variable ) Xn + i TABLE 6.7 Association between variables in primal and dual problems Associated Dual Variable Z ; - C ; ( surplus variable ) j = 1 , 2 , y ; ( decision ...
Any problem Primal Variable ( Decision variable ) x ; ( Slack variable ) Xn + i TABLE 6.7 Association between variables in primal and dual problems Associated Dual Variable Z ; - C ; ( surplus variable ) j = 1 , 2 , y ; ( decision ...
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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
activity additional algorithm allowable amount apply assigned basic solution basic variable BF solution bound boundary called changes coefficients column complete Consider Construct corresponding cost CPF solution decision variables described determine developed dual problem entering 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 linear programming model Maximize million Minimize month needed node objective function obtained operations optimal optimal solution original parameters path perform plant possible presented primal problem Prob procedure profit programming problem provides range resource respective resulting revised sensitivity analysis shown shows side simplex method simplex tableau slack solve step Table tableau tion unit values weeks Wyndor Glass x₁ zero