Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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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 ...
Page 255
... optimal value of the decision variable for each state at the last stage must either be known or obtainable without considering other stages . For every other season , the solution for the optimal employ- ment level must consider the ...
... optimal value of the decision variable for each state at the last stage must either be known or obtainable without considering other stages . For every other season , the solution for the optimal employ- ment level must consider the ...
Page 489
... value per unit of the respective resources . The Dual Theorem , Z = Z , says that the total cost ( implicit value ) of the resources equals the total profit from the activities consuming them in an optimal manner . The Corollary to ...
... value per unit of the respective resources . The Dual Theorem , Z = Z , says that the total cost ( implicit value ) of the resources equals the total profit from the activities consuming them in an optimal manner . The Corollary to ...
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