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Summary

An up-to-date and comprehensive treatment of the fundamentals of scheduling theory, including recent advances and state-of-the-art topicsPrinciples of Sequencing and Scheduling strikes a unique balance between theory and practice, providing an accessible introduction to the concepts, methods, and results of scheduling theory and its core topics. With real-world examples and up-to-date modeling techniques, the book equips readers with the basic knowledge needed for understanding scheduling theory and delving into its applications. The authors begin with an introduction and overview of sequencing and scheduling, including single-machine sequencing, optimization and heuristic solution methods, and models with earliness and tardiness penalties. The most current material on stochastic scheduling, including correct scheduling of safety time and the use of simulation for optimization, is then presented and integrated with deterministic models. Additional topical coverage includes:Extensions of the basic modelParallel-machine modelsFlow shop schedulingScheduling groups of jobsThe job shop problemSimulation models for the dynamic job shopNetwork methods for project schedulingResource-constrained project schedulingStochastic and safe schedulingExtensive end-of-chapter exercises are provided, some of which are spreadsheet-oriented, and link scheduling theory to the most popular analytic platform among today's students and practitioners-the Microsoft Office Excelr spreadsheet. Extensive references direct readers to additional literature, and the book's related Web site houses material that reinforces the book's concepts, including research notes, data sets, and examples from the text.Principles of Sequencing and Scheduling is an excellent book for courses on sequencing and scheduling at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, computer science, operations research, and engineering.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.

Introduction
Introduction to Sequencing and Scheduling
Scheduling Theory
Philosophy and Coverage of the Book
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
Optimization Methods for the Single-Machine Problem
Introduction
Adjacent Pairwise Interchange Methods
A Dynamic Programming Approach
Dominance Properties
A Branch and Bound Approach
Summary
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
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
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
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
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
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
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
Stochastic Flow Shop Scheduling
Introduction
Stochastic Counterpart Models
The Two-Machine Model
The m-Machine Model
Safe Scheduling Models with Stochastic Independence
Flow Shops with Linear Association
Empirical Observations
Summary
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
The m-Machine Model with Consistent Sublots
The Two-Sublot Solution
A Heuristic Procedure for s Sublots
Summary
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
The Job Shop Problem
Introduction
Types of Schedules
Schedule Generation
The Shifting Bottleneck Procedure
Bottleneck Machines
Heuristic and Optimal Solutions
Neighborhood Search Heuristics
Summary
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
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
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 and Tie-Breakers
Summary
Safe Scheduling for Projects
Introduction
Stochastic Balance Principles for Activity Networks
The Assembly Coordination Model
Balancing General Project Networks
Additional Examples
Hierarchical Balancing
Crashing Stochastic Activities
Summary
Appendices
A.
A.1.
A.1.1
A.1.2
A.1.3
A.1.4
A.1.5
A.2
A.3
A.4
B
B.1
B.2
B.3
B.4
B.5
B.6
C
C.1
C.2
C.2.1
C.2.2
C.2.3
C.3
Index
Table of Contents provided by Publisher. All Rights Reserved.

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