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 viii
The late Gerald J. Lieberman sadly passed away shortly before the completion of this edi- tion . He had been Professor Emeritus of Operations Research and Statistics at Stanford Uni- versity , where he was the founding chair of the ...
The late Gerald J. Lieberman sadly passed away shortly before the completion of this edi- tion . He had been Professor Emeritus of Operations Research and Statistics at Stanford Uni- versity , where he was the founding chair of the ...
Page 185
( b ) Formulate and solve a linear programming problem to maximize profit given the produc- tion , resource , and demand constraints . Before she makes her final decision , Katherine plans to explore the following ques- tions ...
( b ) Formulate and solve a linear programming problem to maximize profit given the produc- tion , resource , and demand constraints . Before she makes her final decision , Katherine plans to explore the following ques- tions ...
Page 199
The key ques- tion is : How do we tell whether a particular constraint boundary equation is one of the defining equations when the problem is in augmented form ? The answer , fortunately , is a simple one .
The key ques- tion is : How do we tell whether a particular constraint boundary equation is one of the defining equations when the problem is in augmented form ? The answer , fortunately , is a simple one .
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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