Introduction to Operations Research, Volume 1CD-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
Results 1-3 of 92
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 M. Activity M : LF LS for the FINISH node = = 44 , = — LS 44 duration ( 2 weeks ) = = 42 . ( Since activity M is one of the activities that together complete the project , we also could have automatically set its LF equal to ...
... activity M. Activity M : LF LS for the FINISH node = = 44 , = — LS 44 duration ( 2 weeks ) = = 42 . ( Since activity M is one of the activities that together complete the project , we also could have automatically set its LF equal to ...
Page 498
... activity j ( for j = B , C , . . . , N ) , given the values of XA , XB , ( 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 node as ...
... activity j ( for j = B , C , . . . , N ) , given the values of XA , XB , ( 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 node as ...
Other editions - View all
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 corresponding cost Courseware CPF solution CPLEX decision variables dual problem dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau flow following problem formulation functional constraints Gaussian elimination given graphical identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize subject 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 right-hand sides sensitivity analysis shadow prices simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion values weeks Wyndor Glass x₁ zero