Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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Page 37
... parameter p , then it can be shown that the random variable X = X1 + X2 + ··· + Xn is a binomial random variable with parameters n and p . Thus , if a fair coin is flipped 10 times , then the total number of tails X , which is ...
... parameter p , then it can be shown that the random variable X = X1 + X2 + ··· + Xn is a binomial random variable with parameters n and p . Thus , if a fair coin is flipped 10 times , then the total number of tails X , which is ...
Page 122
... parameter A , i.e. , Px ( k ) = λke - λ k ! Let A be considered as a random variable having a prior density function ... parameter A , is also distributed as a Pois- son random variable but with parameter nλ , show that the posterior ...
... parameter A , i.e. , Px ( k ) = λke - λ k ! Let A be considered as a random variable having a prior density function ... parameter A , is also distributed as a Pois- son random variable but with parameter nλ , show that the posterior ...
Page 316
... parameter A and the same exponential service - time distribution for each server , where su > X. Then the steady - state output of this service facility is also a Poisson process with parameter X.51 Notice that Theorem 10.1 makes no ...
... parameter A and the same exponential service - time distribution for each server , where su > X. Then the steady - state output of this service facility is also a Poisson process with parameter X.51 Notice that Theorem 10.1 makes no ...
Contents
Introduction 32 | 3 |
Planning an Operations Research Study | 12 |
Probability Theory 223 | 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 interval 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