Principles of Sequencing and Scheduling

Name: Principles of Sequencing and Scheduling
Price: 80.24 USD
Availability: InStock
ISBN: 9780470391655

ISBN-10: 0470391650

ISBN-13: 9780470391655

Edition: 2009

Authors: Kenneth R. Baker, Dan Trietsch, Baker

List price: $233.95

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Description:

Sequencing and scheduling rely heavily on decision making and can have a major impact on the productivity of a process. Written by two well-known experts in the field, Principles of Sequencing and Scheduling thoroughly discusses the fundamental and core topics of scheduling theory. An expanded, updated, and more comprehensive version of Kenneth Bakerâ€²s two previous works, this accessible text thoroughly covers deterministic models and includes recent developments and approaches of stochastic models. This is an ideal reference for practicing applied mathematicians, statisticians, computer scientists, operations researchers, and industrial engineers.

Book details

List price: $233.95
Copyright year: 2009
Publisher: John Wiley & Sons, Incorporated
Publication date: 3/30/2009
Binding: Hardcover
Pages: 512
Size: 6.26" wide x 9.53" long x 1.15" tall
Weight: 1.782
Language: English

Kenneth R. Baker, PhD, is Nathaniel Leverone Professor of Management at Dartmouth College. A Fellow of the Institute for Operations Research and the Management Sciences (INFORMS), Dr. Baker has published extensively in his areas of research interest, which include mathematical modeling, spreadsheet engineering, and scheduling. He is the coauthor of Management Science: The Art of Modeling with Spreadsheets, Second Edition, also published by Wiley. Dan Trietsch, PhD, is Professor of Industrial Engineering at the American University of Armenia. He has authored over thirty journal articles on topics such as network design, statistical quality control, and various aspects of scheduling.



Preface



Introduction



Introduction to Sequencing and Scheduling



Scheduling Theory



Philosophy and Coverage of the Book


References



Single-Machine Sequencing



Introduction



Preliminaries



Problems Without Due Dates: Elementary Results



Flowtime and Inventory



Minimizing Total Flowtime



Minimizing Total Weighted Flowtime



Problems with Due Dates: Elementary Results



Lateness Criteria



Minimizing the Number of Tardy Jobs



Minimizing Total Tardiness



Due Dates as Decisions



Summary


References


Exercises



Optimization Methods for the Single-Machine Problem



Introduction



Adjacent Pairwise Interchange Methods



A Dynamic Programming Approach



Dominance Properties



A Branch and Bound Approach



Summary


References


Exercises



Heuristic Methods for the Single-Machine Problem



Introduction



Dispatching and Construction Procedures



Random Sampling



Neighborhood Search Techniques



Tabu Search



Simulated Annealing



Genetic Algorithms



The Evolutionary Solver



Summary


References


Exercises



Earliness and Tardiness Costs



Introduction



Minimizing Deviations from a Common Due Date



Four Basic Results



Due Dates as Decisions



The Restricted Version



Asymmetric Earliness and Tardiness Costs



Quadratic Costs



Job-Dependent Costs



Distinct Due Dates



Summary


References


Exercises



Sequencing for Stochastic Scheduling



Introduction



Basic Stochastic Counterpart Models



The Deterministic Counterpart



Minimizing the Maximum Cost



The Jensen Gap



Stochastic Dominance and Association



Using Risk Solver



Summary


References


Exercises



Safe Scheduling



Introduction



Meeting Service-Level Targets



Trading Off Tightness and Tardiness



The Stochastic E/T Problem



Setting Release Dates



The Stochastic U-Problem: A Service-Level Approach



The Stochastic U-Problem: An Economic Approach



Summary


References


Exercises



Extensions of the Basic Model



Introduction



Nonsimultaneous Arrivals



Minimizing the Makespan



Minimizing Maximum Tardiness



Other Measures of Performance



Related Jobs



Minimizing Maximum Tardiness



Minimizing Total Flowtime with Strings



Minimizing Total Flowtime with Parallel Chains



Sequence-Dependent Setup Times



Dynamic Programming Solutions



Branch and Bound Solutions



Heuristic Solutions



Stochastic Models with Sequence-Dependent Setup Times



Setting Tight Due Dates



Revisiting the Tightness/Tardiness Trade-off



Summary


References


Exercises



Parallel-Machine Models



Introduction



Minimizing the Makespan



Nonpreemptable Jobs



Nonpreemptable Related Jobs



Preemptable Jobs



Minimizing Total Flowtime



Stochastic Models



The Makespan Problem with Exponential Processing Times



Safe Scheduling with Parallel Machines



Summary


References


Exercises



Flow Shop Scheduling



Introduction



Permutation Schedules



The Two-Machine Problem



Johnson's Rule



A Proof of Johnson's Rule



The Model with Time Lags



The Model with Setups



Special Cases of The Three-Machine Problem



Minimizing the Makespan



Branch and Bound Solutions



Heuristic Solutions



Variations of the m-Machine Model



Ordered Flow Shops



Flow Shops with Blocking



No-Wait Flow Shops



Summary


References


Exercises



Stochastic Flow Shop Scheduling



Introduction



Stochastic Counterpart Models



Safe Scheduling Models with Stochastic Independence



Flow Shops with Linear Association



Empirical Observations



Summary


References


Exercises



Lot Streaming Procedures for the Flow Shop



Introduction



The Basic Two-Machine Model



Preliminaries



The Continuous Version



The Discrete Version



Models with Setups



The Three-Machine Model with Consistent Sublots



The Continuous Version



The Discrete Version



The Three-Machine Model with Variable Sublots



Item and Batch Availability



The Continuous Version



The Discrete Version



Computational Experiments



The Fundamental Partition



Defining the Fundamental Partition



A Heuristic Procedure for s Sublots



Summary


References


Exercises



Scheduling Groups of Jobs



Introduction



Scheduling Job Families



Minimizing Total Weighted Flowtime



Minimizing Maximum Lateness



Minimizing Makespan in the Two-Machine Flow Shop



Scheduling with Batch Availability



Scheduling with a Batch Processor



Minimizing the Makespan with Dynamic Arrivals



Minimizing Makespan in the Two-Machine Flow Shop



Minimizing Total Flowtime with Dynamic Arrivals



Batch-Dependent Processing Times



Summary


References


Exercises



The Job Shop Problem



Introduction



Types of Schedules



Schedule Generation



The Shifting Bottleneck Procedure



Bottleneck Machines



Heuristic and Optimal Solutions



Neighborhood Search Heuristics



Summary


References


Exercises



Simulation Models for the Dynamic Job Shop



Introduction



Model Elements



Types of Dispatching Rules



Reducing Mean Flowtime



Meeting Due Dates



Background



Some Clarifying Experiments



Experimental Results



Summary


References



Network Methods for Project Scheduling



Introduction



Logical Constraints and Network Construction



Temporal Analysis of Networks



The Time/Cost Trade-off



Traditional Probabilistic Network Analysis



The PERT Method



Theoretical Limitations of PERT



Summary


References


Exercises



Resource-Constrained Project Scheduling



Introduction



Extending the Job Shop Model



Extending the Project Model



Heuristic Construction and Search Algorithms



Construction Heuristics



Neighborhood Search Improvement Schemes



Selecting Priority Lists



Summary


References


Exercises



Safe Scheduling for Projects



Introduction



Stochastic Balance Principles For Activity Networks



The Assembly Coordination Model



Balancing a General Project Network



Additional Examples



Hierarchical Balancing



Crashing Stochastic Activities



Summary


References


Exercises



Practical Processing Time Distributions



Important Processing Time Distributions



The Uniform Distribution



The Exponential Distribution



The Normal Distribution



The Lognormal Distribution



The Parkinson Distribution



Increasing and Decreasing Completion Rates



Stochastic Dominance



Linearly Associated Processing Times


References



The Critical Ratio Rule



A Basic Trade-off Problem



Optimal Policy for Discrete Probability Models



A Special Discrete Case: Equally Likely Outcomes



Optimal Policy for Continuous Probability Models



A Special Continuous Case: The Normal Distribution



Calculating d + yE(T) for the Normal Distribution


References



Integer Programming Models for Sequencing



Introduction



The Single-Machine Model



Sequence-Position Decisions



Precedence Decisions



Time-Indexed Decisions



The Flow Shop Model


References


Name Index


Subject Index