Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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Page 153
... original problem has been considerably revised by the introduction of the artificial variable . Recall that the original third con- straint was 3x1 - 2x2 ≤ -18 . By adding one variable and subtracting another , both of which can take ...
... original problem has been considerably revised by the introduction of the artificial variable . Recall that the original third con- straint was 3x1 - 2x2 ≤ -18 . By adding one variable and subtracting another , both of which can take ...
Page 165
... original optimal solution , then this new equation ( 0 ) can now be checked in the usual way ( i.e. , by checking whether all of the coefficients of non - basic variables are non - negative ) to determine whether this solution is ...
... original optimal solution , then this new equation ( 0 ) can now be checked in the usual way ( i.e. , by checking whether all of the coefficients of non - basic variables are non - negative ) to determine whether this solution is ...
Page 219
... original nodes . ( Otherwise , the intervening new node would be closer to the origin . ) Furthermore , it must be the closest new node to one of the original nodes to which it is connected by a single branch . ( Otherwise , whichever ...
... original nodes . ( Otherwise , the intervening new node would be closer to the origin . ) Furthermore , it must be the closest new node to one of the original nodes to which it is connected by a single branch . ( Otherwise , whichever ...
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