## Advanced Models for Manufacturing Systems ManagementThis book presents the mathematical models applicable to manufacturing systems management, covering problems from production to real time control. It explores manufacturing systems from the viewpoints of both physical structure and performance measures. Two broad classes of mathematical models are covered in detail: Advanced Models for Manufacturing Systems Management describes dynamic systems modeling by state equations, a unifying framework for a wide variety of models. The text/reference stresses model building, but it examines model solving as well. Computational techniques are illustrated, such as linear programming, branch and bound methods, and dynamic programming. Particular emphasis is given to the development of heuristic methods from mathematical models. The book provides readers with valuable tools for management and design. The use of descriptive models within an optimization algorithm is considered. Numerous examples illustrate theoretical concepts throughout text. Appendices are given at the end of the book in order to recall fundamentals, such as linear programming and graph theory. Appendices also appear within each chapter. In this way, readers can follow the main reading path without getting involved with details; these appendices can be read at a later time. This textual structure makes this book particularly well suited for self-study. Advanced Models for Manufacturing Systems Management is beneficial reading for both students and practitioners. |

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### Contents

Manufacturing systems modeling | 1 |

Optimization models and model solving | 20 |

Discretetime models | 52 |

DEDS models for scheduling problems | 102 |

Evaluative models 101 | 191 |

Putting things together | 230 |

A Fundamentals of Mathematical Programming | 246 |

B Linear Programming and Network Optimization | 278 |

Enumerative and Heuristic Methods for Discrete | 307 |

Dynamic Programming | 346 |

E Stochastic modeling | 357 |

Problems | 376 |

384 | |

399 | |

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### Common terms and phrases

algorithm applied approach assume basic batch binary variables Branch and Bound bucket capacity constraints Chapter CLSP completion computational consider continuous relaxation convex corresponding CTMC decision variables decomposition denote discrete optimization disjunctive arcs disjunctive graph dual function dualize due dates Dynamic Programming equations evaluate Example exponential feasible set feasible solution flow formulation given global optimality heuristic integer inventory level knapsack problem Lagrangian relaxation linear load lot-sizing problem lower bound LP problem makespan manufacturing system mathematical model matrix MILP models minimize node Note NP-complete NP-hard objective function obtain operation optimal solution optimal value optimization problem performance measures permutation Petri nets polynomial possible primal production queueing network relaxed problem requires scheduling problems Section sequence setup costs shortest path shortest path problem Simplex method simulated annealing simulation single-machine solved stochastic strategy subgradient subproblems subset tabu search theorem throughput tion vector WSPT

### Popular passages

Page 384 - G. Cohen, GJ Olsder and JP Quadrat (1992). Synchronization and Linearity — An Algebra for Discrete Event Systems. Wiley, New York.

Page 387 - A bottleneck-based beam search for job scheduling in a flexible manufacturing system", International Journal of Production Research 27 (1989) 1949-1961.

Page 386 - Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research 41 (1993) 157-183.

Page 387 - LN, 1990. Set partitioning and column generation heuristics for capacitated dynamic lotsizing. European Journal of Operational Research 46, 38-47. Cattrysse, D., Salomon, M., Kuik. R.. Van Wassenhove, LN, 1993. A dual ascent and column generation heuristic for the discrete lotsizing and scheduling problem with setup times.

Page 384 - Determining Lot Sizes and Resource Requirements: A Review," Operations Research 35, 329-345.

Page 387 - An Application of Bin Packing to Multi-processor Scheduling, SIAM Journal of Computing.