## Operations Research: A Practical IntroductionStudents with diverse backgrounds will face a multitude of decisions in a variety of engineering, scientific, industrial, and financial settings. They will need to know how to identify problems that the methods of operations research (OR) can solve, how to structure the problems into standard mathematical models, and finally how to apply or develop computational tools to solve the problems. Perfect for any one-semester course in OR, Operations Research: A Practical Introduction answers all of these needs. In addition to providing a practical introduction and guide to using OR techniques, it includes a timely examination of innovative methods and practical issues related to the development and use of computer implementations. It provides a sound introduction to the mathematical models relevant to OR and illustrates the effective use of OR techniques with examples drawn from industrial, computing, engineering, and business applications Many students will take only one course in the techniques of Operations Research. Operations Research: A Practical Introduction offers them the greatest benefit from that course through a broad survey of the techniques and tools available for quantitative decision making. It will also encourage other students to pursue more advanced studies and provides you a concise, well-structured, vehicle for delivering the best possible overview of the discipline. |

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### Contents

1ntroduction to Operations Research | 3 |

References | 14 |

Linear Programming | 17 |

Network Analysis | 79 |

Integer Programming | 142 |

Nonlinear Optimization | 195 |

Markov Processes | 223 |

Queuing Models | 255 |

Simulation | 275 |

Decision Analysts | 301 |

Heuristic Techniques for Optimization | 347 |

Review of Essential Mathematics | 375 |

List of Trademark Names | 381 |

### Common terms and phrases

activities algorithm allocation analysis applications assignment basic variables behavior branch-and-bound coefficients column configuration Consider constraints cover inequality decision tree decision variables decision-maker denote described determine developed distribution dual elements equations example expected exponential exponential distribution extreme points F1GURE feasible region Figure flow formulation heuristic initial integer programming integer solution iteration knapsack problem Lagrangian linear programming problem LP solution machine Markov Markov chain mathematical programming matrix maximize maximize z maximum minimize minimum spanning tree node non-basic variable nonlinear programming objective function objective function value obtain Operations Research optimal solution outcome package performance possible primal programming model quadratic queue queuing systems scheduling servers shortest path Simplex algorithm Simplex method simulated annealing solver solving spanning tree steady-state strategy subproblems tableau techniques utility vector warehouse zero zero-one