Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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Page 164
... optimal value of the objective function , Z1 = Z , might be interpreted as the total profit obtained by using the optimal solution . In this case , y indicates the rate at which profit would increase ( decrease ) if the amount of ...
... optimal value of the objective function , Z1 = Z , might be interpreted as the total profit obtained by using the optimal solution . In this case , y indicates the rate at which profit would increase ( decrease ) if the amount of ...
Page 364
... optimal . This characterization of optimal policies can be used to determine which policies are not optimal , but it cannot be used to find the optimal policy . Of course , one method for solving the optimization problem is to enumerate ...
... optimal . This characterization of optimal policies can be used to determine which policies are not optimal , but it cannot be used to find the optimal policy . Of course , one method for solving the optimization problem is to enumerate ...
Page 492
... optimal is equivalent to asking whether the ( still optimal or better - than - optimal ) complementary dual solution is still feasible . This statement follows immediately from Theorem 15.3 , which implies that all of the coefficients ...
... optimal is equivalent to asking whether the ( still optimal or better - than - optimal ) complementary dual solution is still feasible . This statement follows immediately from Theorem 15.3 , which implies that all of the coefficients ...
Contents
Introduction 3 2 | 3 |
Planning an Operations Research Study | 12 |
Probability Theory 223 | 77 |
Copyright | |
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allocation assigned assumed b₁ b₂ basic feasible solution c₁ 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 selected 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