Introduction to Operations Research |
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Page 35
... solutions are then identified by using an appropriate adjective . .. A feasible solution is a solution for which all ... feasible solutions , while the points ( -1 , 3 ) and ( 4 , 4 ) are infeasible solutions . The feasible region is the ...
... solutions are then identified by using an appropriate adjective . .. A feasible solution is a solution for which all ... feasible solutions , while the points ( -1 , 3 ) and ( 4 , 4 ) are infeasible solutions . The feasible region is the ...
Page 155
... solution in n - dimensional space . A corner - point feasible ( CPF ) solution is a feasible solution that does not lie on any line segment ' connecting two other feasible solutions . = As this definition implies , a feasible solution ...
... solution in n - dimensional space . A corner - point feasible ( CPF ) solution is a feasible solution that does not lie on any line segment ' connecting two other feasible solutions . = As this definition implies , a feasible solution ...
Page 203
... feasible solutions or has feasible solutions but no optimal solution ( because the objective function is un- bounded ) . Our final property summarizes the primal - dual relationships under all these possibilities . Duality theorem : The ...
... feasible solutions or has feasible solutions but no optimal solution ( because the objective function is un- bounded ) . Our final property summarizes the primal - dual relationships under all these possibilities . Duality theorem : The ...
Contents
INTRODUCTION | 1 |
OVERVIEW OF THE OPERATIONS RESEARCH | 8 |
INTRODUCTION TO LINEAR PROGRAMMING | 25 |
Copyright | |
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Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 1995 |
Common terms and phrases
algorithm apply basic solution coefficients column concave function Consider the following constraint boundary convex corresponding cost Courseware CPF solution decision variables dual problem dynamic programming entering basic variable equations estimate example expected exponential distribution feasible region feasible solutions following problem forecast formulation functional constraints Gaussian elimination given identify initial BF solution IP problem iteration leaving basic variable linear programming model linear programming problem LP relaxation Markov chain matrix Maximize Maximize Z Minimize node nonbasic variables nonlinear programming nonnegative number of customers objective function obtained optimal policy optimal solution parameters payoff player presented in Sec primal problem Prob probability distribution procedure queueing models queueing system queueing theory random numbers resulting sensitivity analysis servers simplex method simulation slack variables solve steady-state strategy subproblem Table Theory tion transportation problem trial solution unit Wyndor Glass x₁ zero