Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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Page 151
... maximize , the objective function . This occurred , for example , in Example 3 of Sec . 5.3 . Minimization can be handled very easily in either of two ways . One is to make the following very minor changes in the simplex method . The ...
... maximize , the objective function . This occurred , for example , in Example 3 of Sec . 5.3 . Minimization can be handled very easily in either of two ways . One is to make the following very minor changes in the simplex method . The ...
Page 546
... maximize Ꮓ subject to = — - -X1 + 3x2 2x3 + x7 , and 3x11x2 + 2x3 + x7 ≤ 7 - -2x1 + 4x2 -4x1 + 3x2 + 8X3 - 2x712 - x710 x ; ≥ 0 , for j = 1 , 2 , 3 , 7 . Is the old solution , plus x7 = 0 , still optimal ? If not , find the new ...
... maximize Ꮓ subject to = — - -X1 + 3x2 2x3 + x7 , and 3x11x2 + 2x3 + x7 ≤ 7 - -2x1 + 4x2 -4x1 + 3x2 + 8X3 - 2x712 - x710 x ; ≥ 0 , for j = 1 , 2 , 3 , 7 . Is the old solution , plus x7 = 0 , still optimal ? If not , find the new ...
Page 550
... maximize 8x1 + 2x2 + 7x3 + 12x4 + 9xь + 3x6 + 10x7 , subject to 3x1 + x2 + 5x3 + 3x1 + 3x2 + 6x3 + 2x1 + 3x2 + 4x3 + 10x4 + 2x5 + 2x6 + 8x4 + 5x5 + 4x6 + 5x4 + 4x5 + X6 + 7x7 ≤ 10 6x7 ≤ 25 2x7 ≤ 16 and x ; ≥ 0 , for j = 1 , 2 ...
... maximize 8x1 + 2x2 + 7x3 + 12x4 + 9xь + 3x6 + 10x7 , subject to 3x1 + x2 + 5x3 + 3x1 + 3x2 + 6x3 + 2x1 + 3x2 + 4x3 + 10x4 + 2x5 + 2x6 + 8x4 + 5x5 + 4x6 + 5x4 + 4x5 + X6 + 7x7 ≤ 10 6x7 ≤ 25 2x7 ≤ 16 and x ; ≥ 0 , for j = 1 , 2 ...
Contents
Introduction 32 | 3 |
Planning an Operations Research Study | 12 |
Probability Theory 223 | 77 |
Copyright | |
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allocation assigned assumed b₁ b₂ basic feasible solution basic solution calling units coefficient concave function Consider constraints convex convex function convex set corresponding decision variables decision-maker demand denote density function discrete random variable dual problem entering basic variable estimate event example expected value exponential distribution formulation given Hence illustrate integer inventory iteration leaving basic variable linear programming problem Markov chain mathematical matrix maximize minimize mixed strategy node non-basic variables non-negative normal distribution objective function obtained operations research optimal policy optimal solution optimal value original parameter payoff period player Poisson input possible primal problem probability distribution queueing model queueing system queueing theory random numbers sample space server service facility set of equations simplex method simulation slack variables solution procedure solve steady-state Suppose technique Theorem tion total cost variance waiting x₁ zero