## Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement LearningSimulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. Since it became possible to analyze random systems using computers, scientists and engineers have sought the means to optimize systems using simulation models. Only recently, however, has this objective had success in practice. Cutting-edge work in computational operations research, including non-linear programming (simultaneous perturbation), dynamic programming (reinforcement learning), and game theory (learning automata) has made it possible to use simulation in conjunction with optimization techniques. As a result, this research has given simulation added dimensions and power that it did not have in the recent past. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. |

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### Contents

BACKGROUND | 1 |

NOTATION | 9 |

A REFRESHER | 15 |

BASIC CONCEPTS UNDERLYING SIMULATION | 29 |

AN OVERVIEW | 47 |

RESPONSE SURFACES AND NEURAL NETS | 57 |

PARAMETRIC OPTIMIZATION | 93 |

DYNAMIC PROGRAMMING | 133 |

MARKOV CHAIN AUTOMATA THEORY | 277 |

BACKGROUND MATERIAL | 287 |

PARAMETRIC OPTIMIZATION | 317 |

CONTROL OPTIMIZATION | 343 |

CASE STUDIES | 409 |

CODES | 433 |

CONCLUDING REMARKS | 537 |

REINFORCEMENT LEARNING | 211 |