## Introduction to Operations ResearchCD-ROM contains: Student version of MPL Modeling System and its solver CPLEX -- MPL tutorial -- Examples from the text modeled in MPL -- Examples from the text modeled in LINGO/LINDO -- Tutorial software -- Excel add-ins: TreePlan, SensIt, RiskSim, and Premium Solver -- Excel spreadsheet formulations and templates. |

### From inside the book

Results 1-3 of 36

Page 734

... one is the expected

expected value, this quantity is m n Expected

where ptj is the

strategy j.

... one is the expected

**payoff**. By applying the probability theory definition ofexpected value, this quantity is m n Expected

**payoff**for player 1 = V V p^x^j,where ptj is the

**payoff**if player 1 uses pure strategy i and player 2 uses purestrategy j.

Page 764

Frederick S. Hillier, Gerald J. Lieberman. Expected Value of Experimentation.

Rather than just obtain an upper bound on the expected increase in

excluding the cost of the experiment) due to performing experimentation, we now

will do ...

Frederick S. Hillier, Gerald J. Lieberman. Expected Value of Experimentation.

Rather than just obtain an upper bound on the expected increase in

**payoff**(excluding the cost of the experiment) due to performing experimentation, we now

will do ...

Page 767

Record this expected

and designate this quantity as also being the expected

leading to this fork. 3. For each decision fork, compare the expected

...

Record this expected

**payoff**for each decision fork in boldface next to the fork,and designate this quantity as also being the expected

**payoff**for the branchleading to this fork. 3. For each decision fork, compare the expected

**payoffs**of its...

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### Contents

SUPPLEMENT TO APPENDIX 3 | 3 |

Problems | 6 |

An Algorithm for the Assignment Problem | 18 |

Copyright | |

44 other sections not shown

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### Common terms and phrases

activity additional algorithm alternative amount analysis apply assigned assumed basic variable begin BF solution bound calculate called changes coefficients column complete Consider constraints Construct corresponding cost CPF solution customers decision demand described determine developed distribution entering equations estimated example expected feasible FIGURE final flow formulation given gives hour identify illustrate increase indicates initial inventory iteration linear programming machine Maximize maximum mean million Minimize month needed node objective function obtained operations optimal optimal solution original parameter path payoff perform plant player possible presented Prob probability problem procedure profit programming problem queueing respectively resulting shown shows side simplex method solution solve step strategy Table tableau tion transportation unit weeks