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. |
From inside the book
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Page 477
... activity turns out to be the same as its esti- mated duration and ( 2 ) each activity begins as soon as all its immediate predecessors are finished . The starting and finishing times of each activity if no delays occur anywhere in ...
... activity turns out to be the same as its esti- mated duration and ( 2 ) each activity begins as soon as all its immediate predecessors are finished . The starting and finishing times of each activity if no delays occur anywhere in ...
Page 481
... activity finishes , this rule is saying that the activity must finish in time to enable all its immediate successors to be- gin by their latest start times . For example , consider activity M in Fig . 10.1 . Its only immediate ...
... activity finishes , this rule is saying that the activity must finish in time to enable all its immediate successors to be- gin by their latest start times . For example , consider activity M in Fig . 10.1 . Its only immediate ...
Page 498
... activity j ( for j = B , C , . . . , N ) , given the values of xă , XB , XN . ( No such variable is needed for activity A , since an activity that begins the project is au- tomatically assigned a value of 0. ) By treating the FINISH ...
... activity j ( for j = B , C , . . . , N ) , given the values of xă , XB , XN . ( No such variable is needed for activity A , since an activity that begins the project is au- tomatically assigned a value of 0. ) By treating the FINISH ...
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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 dual problem dual simplex method dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal programming 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 slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion unit profit values weeks Wyndor Glass x₁ zero