Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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Page 142
... feasible solution exists and that the optimal value of Z is finite . Properties 2 and 3 indicate that the number of basic feasible solutions is strictly positive and finite . Property 4 indicates that only this finite number of solutions ...
... feasible solution exists and that the optimal value of Z is finite . Properties 2 and 3 indicate that the number of basic feasible solutions is strictly positive and finite . Property 4 indicates that only this finite number of solutions ...
Page 159
... optimal solution . How does one know that there are other optimal solutions ? Note that one of the non - basic variables , 13 , has a zero coefficient in the current objective function , Z = 18 + 0x3 - 15. Recall that each coefficient ...
... optimal solution . How does one know that there are other optimal solutions ? Note that one of the non - basic variables , 13 , has a zero coefficient in the current objective function , Z = 18 + 0x3 - 15. Recall that each coefficient ...
Page 164
... optimal value of the objective function ,. Z , Z , might be interpreted as the total profit obtained by using the optimal solution . In this case , y indicates the rate at which profit would increase ( decrease ) if the amount of ...
... optimal value of the objective function ,. Z , Z , might be interpreted as the total profit obtained by using the optimal solution . In this case , y indicates the rate at which profit would increase ( decrease ) if the amount of ...
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