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. |
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Page 360
Scheduled Installations Maximum Production Unit Cost * of Production Unit Cost * of Storage Month 1 2 3 4 10 15 25 20 25 35 ... Thus , the cumulative number of engines produced by the end of months 1 , 2 , 3 , and 4 must be at least 10 ...
Scheduled Installations Maximum Production Unit Cost * of Production Unit Cost * of Storage Month 1 2 3 4 10 15 25 20 25 35 ... Thus , the cumulative number of engines produced by the end of months 1 , 2 , 3 , and 4 must be at least 10 ...
Page 518
For each of these paths , find the approximate proba- bility that the path will be completed within 22 months . ... 2 months 3.5 months 21 months 1 month 1.5 months 18 months 0.5 month 1 month 15 months 3 months 5 months 24 months 1 ...
For each of these paths , find the approximate proba- bility that the path will be completed within 22 months . ... 2 months 3.5 months 21 months 1 month 1.5 months 18 months 0.5 month 1 month 15 months 3 months 5 months 24 months 1 ...
Page 1045
Past Sales Current Sales Month Year 1 Year 2 Year 3 This Year May 412 423 431 458 June 446 472 459 494 July 420 415 433 468 August 471 492 518 555 September 355 340 309 387 October 312 301 335 364 November 567 629 594 662 December 533 ...
Past Sales Current Sales Month Year 1 Year 2 Year 3 This Year May 412 423 431 458 June 446 472 459 494 July 420 415 433 468 August 471 492 518 555 September 355 340 309 387 October 312 301 335 364 November 567 629 594 662 December 533 ...
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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 allocation allowable range artificial variables assignment problem augmenting path basic solution Big M method changes coefficients column Consider the following constraint boundary corresponding CPLEX decision variables dual problem dynamic programming entering basic variable example feasible region feasible solutions final simplex tableau final tableau following problem formulation functional constraints Gaussian elimination given goal goal programming graphically identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LP relaxation lution Maximize Maximize Z maximum flow problem Minimize needed node nonbasic variables objective function obtained optimal solution optimality test path Plant presented in Sec primal problem Prob procedure range to stay resource right-hand sides sensitivity analysis shadow prices slack variables solve this model Solver spreadsheet step subproblem surplus variables tion transportation problem transportation simplex method weeks Wyndor Glass x₁ zero