Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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Page 123
... observation of Y , corresponding to x = 55 feet . = ( c ) Suppose that two future observations on Y , both corresponding tox 55 feet , are to be made . Construct prediction intervals for both of these observations so that the ...
... observation of Y , corresponding to x = 55 feet . = ( c ) Suppose that two future observations on Y , both corresponding tox 55 feet , are to be made . Construct prediction intervals for both of these observations so that the ...
Page 456
... observations between 0.014 and 0.328 even though the probability that a random observation will fall inside this inter- val is greater than 1/4 . Second , certain portions of a distribution may be more critical than others for obtaining ...
... observations between 0.014 and 0.328 even though the probability that a random observation will fall inside this inter- val is greater than 1/4 . Second , certain portions of a distribution may be more critical than others for obtaining ...
Page 464
... observation . This provides a sequence of statistically independent observa- tions . Furthermore , since they are averages , these observations should tend to have an approximately normal distribution because of relevant versions of the ...
... observation . This provides a sequence of statistically independent observa- tions . Furthermore , since they are averages , these observations should tend to have an approximately normal distribution because of relevant versions of the ...
Contents
Introduction 32 | 3 |
Planning an Operations Research Study | 12 |
Probability Theory 223 | 23 |
Copyright | |
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Common terms and phrases
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