Introduction to Operations ResearchMethodology; Fundamentals; Techniques: mathematical programming; Techniques: probalistic models;Techniques: advanced topics in mathematical programming. |
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Page 141
... convex sets . 16 An algebraic definition of basic feasible solution is given shortly , after the required concepts have been introduced . 17 See Appendix 1 for the definition of an extreme point of a convex set . In the above example ...
... convex sets . 16 An algebraic definition of basic feasible solution is given shortly , after the required concepts have been introduced . 17 See Appendix 1 for the definition of an extreme point of a convex set . In the above example ...
Page 587
... set of feasible solutions is a convex set . This property and the concavity of f ( x1 , x2 ,・・・, Xn ) imply that any local optimum is also a global optimum , i.e. , any feasible solution which maximizes f ( x1 , x2 , ... , In ) over ...
... set of feasible solutions is a convex set . This property and the concavity of f ( x1 , x2 ,・・・, Xn ) imply that any local optimum is also a global optimum , i.e. , any feasible solution which maximizes f ( x1 , x2 , ... , In ) over ...
Page 589
... convex set of feasible solutions may be enclosed within , but approximated as closely as desired , by a set con- structed entirely from linear constraints . This fact is intuitively plausible since , by definition of convex set ( see ...
... convex set of feasible solutions may be enclosed within , but approximated as closely as desired , by a set con- structed entirely from linear constraints . This fact is intuitively plausible since , by definition of convex set ( see ...
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