Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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Page 51
... Note that if j μ ) 2 is called the variance of the random variable X and is often denoted by o2 . The square root of the variance , o , is called the standard deviation of the ran- dom variable X. It is easily shown , in terms of ...
... Note that if j μ ) 2 is called the variance of the random variable X and is often denoted by o2 . The square root of the variance , o , is called the standard deviation of the ran- dom variable X. It is easily shown , in terms of ...
Page 59
... Note that , given X1 = s and X2 = t , the conditional density functions , fxx ( t ) and fx1 | X - t ( s ) , respectively , satisfy all of the conditions for a density function . They are non - negative and , furthermore , 2 ƒ x ; \ x1 ...
... Note that , given X1 = s and X2 = t , the conditional density functions , fxx ( t ) and fx1 | X - t ( s ) , respectively , satisfy all of the conditions for a density function . They are non - negative and , furthermore , 2 ƒ x ; \ x1 ...
Page 383
Frederick S. Hillier, Gerald J. Lieberman. Note that since shortages are permitted , ( y2 - ) can be negative ; further note that E [ C1 ( x1 ) ] is just a function of y2 and yo , with y obtained from ( 53 ) . At the beginning of period ...
Frederick S. Hillier, Gerald J. Lieberman. Note that since shortages are permitted , ( y2 - ) can be negative ; further note that E [ C1 ( x1 ) ] is just a function of y2 and yo , with y obtained from ( 53 ) . At the beginning of period ...
Contents
Introduction 32 | 3 |
Planning an Operations Research Study | 12 |
Probability Theory 223 | 77 |
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