Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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Page 83
... example , returning to the inventory example , suppose that the following information is made available to the Air Force . A similar type of engine was produced for an earlier version of the current airplane under consideration . The ...
... example , returning to the inventory example , suppose that the following information is made available to the Air Force . A similar type of engine was produced for an earlier version of the current airplane under consideration . The ...
Page 158
... example where the optimal solution had already been reached , but other examples have been artificially constructed so that they do become en- trapped in just such a perpetual loop . Fortunately , although it has been shown that such a ...
... example where the optimal solution had already been reached , but other examples have been artificially constructed so that they do become en- trapped in just such a perpetual loop . Fortunately , although it has been shown that such a ...
Page 342
... example , if travel - time model 1 is used , then Καλ E ( C3 ) = Cf + SC 83 w + CrW + λ υ The method of solution is quite straightforward . Since L = XW ( see Sec . 10.3.8 ) , it follows that \ E ( C3 ) = E ( C1 ) + [ Cƒ + CwλE ( T ) ...
... example , if travel - time model 1 is used , then Καλ E ( C3 ) = Cf + SC 83 w + CrW + λ υ The method of solution is quite straightforward . Since L = XW ( see Sec . 10.3.8 ) , it follows that \ E ( C3 ) = E ( C1 ) + [ Cƒ + CwλE ( T ) ...
Contents
Introduction | 3 |
Planning an Operations Research Study | 12 |
Probability Theory | 23 |
Copyright | |
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allocation assigned assumed b₁ 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 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