Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
From inside the book
Results 1-3 of 43
Page 33
... event E1 , which consists of the points ( 10 , 0 ) , ( 10 , 1 ) , ( 10 , 2 ) , · · ( 10 , 99 ) , has occurred . Consider the event E2 which represents a demand for the product in the second month that does not exceed one unit . This event ...
... event E1 , which consists of the points ( 10 , 0 ) , ( 10 , 1 ) , ( 10 , 2 ) , · · ( 10 , 99 ) , has occurred . Consider the event E2 which represents a demand for the product in the second month that does not exceed one unit . This event ...
Page 227
... events must be achieved . Thus , an event is the completion of all the activities leading into that node , and this event must precede the initiation of the activities lead- ing out of that node . ( In reality , it is often possible to ...
... events must be achieved . Thus , an event is the completion of all the activities leading into that node , and this event must precede the initiation of the activities lead- ing out of that node . ( In reality , it is often possible to ...
Page 231
... event , the earliest time for that event is the sum of the elapsed times of the activities leading to the event . Therefore , for this case , the expected value and variance of earliest time are the sum of the expected values and of the ...
... event , the earliest time for that event is the sum of the elapsed times of the activities leading to the event . Therefore , for this case , the expected value and variance of earliest time are the sum of the expected values and of the ...
Contents
Introduction 32 | 3 |
Planning an Operations Research Study | 12 |
Probability Theory 223 | 23 |
Copyright | |
17 other sections not shown
Other editions - View all
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