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 1
1 Introduction 1.1 THE ORIGINS OF OPERATIONS RESEARCH Since the advent of the industrial revolution , the world has seen a remarkable growth in the size and complexity of organizations . The artisans ' small shops of an earlier era have ...
1 Introduction 1.1 THE ORIGINS OF OPERATIONS RESEARCH Since the advent of the industrial revolution , the world has seen a remarkable growth in the size and complexity of organizations . The artisans ' small shops of an earlier era have ...
Page 3
Thus , in a certain sense , operations research involves creative scientific research into the fundamental properties of operations . However , there is more to it than this . Specifically , OR is also concerned with the practical ...
Thus , in a certain sense , operations research involves creative scientific research into the fundamental properties of operations . However , there is more to it than this . Specifically , OR is also concerned with the practical ...
Page 20
A system of this type was developed for Texaco ' to help plan and schedule its blending operations at its various refineries . Called OMEGA ( Optimization Method for the Estimation of Gasoline Attributes ) , it is an interactive system ...
A system of this type was developed for Texaco ' to help plan and schedule its blending operations at its various refineries . Called OMEGA ( Optimization Method for the Estimation of Gasoline Attributes ) , it is an interactive system ...
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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