## Scheduling: Theory, Algorithms, and SystemsThis new edition of the well established text Scheduling - Theory, Algorithms, and Systems provides an up-to-date coverage of important theoretical models in the scheduling literature as well as significant scheduling problems that occur in the real world. It again includes supplementary material in the form of slide-shows from industry and movies that show implementations of scheduling systems. The main structure of the book as per previous edition consists of three parts. The first part focuses on deterministic scheduling and the related combinatorial problems. The second part covers probabilistic scheduling models; in this part it is assumed that processing times and other problem data are random and not known in advance. The third part deals with scheduling in practice; it covers heuristics that are popular with practitioners and discusses system design and implementation issues. All three parts of this new edition have been revamped and streamlined. The references have been made completely up-to-date. Theoreticians and practitioners alike will find this book of interest. Graduate students in operations management, operations research, industrial engineering, and computer science will find the book an accessible and invaluable resource. Scheduling - Theory, Algorithms, and Systems will serve as an essential reference for professionals working on scheduling problems in manufacturing, services, and other environments. Reviews of third edition: This well-established text covers both the theory and practice of scheduling. The book begins with motivating examples and the penultimate chapter discusses some commercial scheduling systems and examples of their implementations." (Mathematical Reviews, 2009) |

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### Contents

Part II Stochastic Models | 246 |

Part III Scheduling in Practice | 374 |

Appendices | 558 |

References | 623 |

661 | |

667 | |

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### Common terms and phrases

algorithm applied batch branch-and-bound Chapter Class 1 jobs class of nonpreemptive Cmax computed constraint programming denote described deterministic disjunctive arcs dispatching rules due date dynamic programming example expected makespan exponentially distributed flow shops Gantt chart genetic algorithms graph heuristic idle instance iteration Lemma linear program Lmax lower bound LP relaxation machine environment machines in parallel minimizes the total models node nonpreemptive static list number of jobs number of machines objective function operation optimal schedule optimal sequence pairwise interchange parallel machine Pinedo polynomial precedence constraints preemptions preemptive dynamic policies priority prmp procedure processed on machine proof random variable release dates scheduling problems scheduling systems set of jobs simulated annealing single machine solution Springer Science+Business Media static list policies stochastic dominance strongly NP-hard subproblem subset Theorem total completion total weighted tardiness wjTj WSEPT rule WSPT zero