Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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Page 65
... sample ) , then the random variable , X = Σ Xi , i = 1 has a binomial distribution with parameters n and p . 3.14 LAW OF LARGE NUMBERS In Sec . 3.8 , it was pointed out that the mean of a random sample tends to converge to the ...
... sample ) , then the random variable , X = Σ Xi , i = 1 has a binomial distribution with parameters n and p . 3.14 LAW OF LARGE NUMBERS In Sec . 3.8 , it was pointed out that the mean of a random sample tends to converge to the ...
Page 90
... sample . The decisions will still be related to making statements about the states of nature . The objective will be to partition , the set of all possible values of the states of nature , into two mutually exclusive sets , 1 and 2 ...
... sample . The decisions will still be related to making statements about the states of nature . The objective will be to partition , the set of all possible values of the states of nature , into two mutually exclusive sets , 1 and 2 ...
Page 114
... sample data are available the decision - maker has both subjective and objective procedures available to aid him in his choice . In the category of subjective procedures are analyses made by plotting histograms and sample cumulative ...
... sample data are available the decision - maker has both subjective and objective procedures available to aid him in his choice . In the category of subjective procedures are analyses made by plotting histograms and sample cumulative ...
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