## Aimms Optimization ModelingThe AIMMS Optimization Modeling book provides not only an introduction to modeling but also a suite of worked examples. It is aimed at users who are new to modeling and those who have limited modeling experience. Both the basic concepts of optimization modeling and more advanced modeling techniques are discussed. The Optimization Modeling book is AIMMS version independent. |

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### Contents

Introduction to Optimization Modeling | 3 |

Formulating Optimization Models | 14 |

Algebraic Representation of Models | 33 |

Sensitivity Analysis | 42 |

Network Flow Models | 53 |

General Optimization Modeling Tricks | 65 |

Integer Linear Programming Tricks | 77 |

Basic Optimization Modeling Applications | 91 |

Intermediate Optimization Modeling Applications | 139 |

A TwoLevel Decision Problem | 149 |

A Bandwidth Allocation Problem | 163 |

A Power System Expansion Problem | 173 |

An Inventory Control Problem | 186 |

Advanced Optimization Modeling Applications | 201 |

A File Merge Problem | 227 |

A Cutting Stock Problem | 241 |

A Media Selection Problem | 100 |

A Diet Problem | 109 |

A Farm Planning Problem | 117 |

A Pooling Problem | 127 |

A Telecommunication Network Problem | 251 |

A Facility Location Problem | 264 |

Appendices | 287 |

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### Common terms and phrases

Aimms Aimms modeling algorithm amount applications approach Arnhem auxiliary model Benders binary chapter coefficients companies computational Consider constraints corresponding customer zones cutting pattern cutting stock problem decision variables demand denote determine distribution center DMU’s equal event parameters example facility location problem feasible region Figure final products flight attendants fraction Gouda Implement inequality initial input integer programming integer solution investment categories kcub linear programming model lower bound Maastricht master problem Maximize measure Mexican chips Minimize model formulation model statement network flow network flow model nodes nonlinear programming notation number of families objective function objective function value optimal solution optimization model path plain chips pool tanks portfolio possible potato chips potato chips model previous section profit random variable reduced cost referred result scenarios selected shadow prices simplex method solved solver specified stochastic programming Summary symbolic Table tion unit upper bound variance zero