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Principles of Sequencing and Scheduling
ISBN-13: 9781119262565 / Angielski / Twarda / 2018 / 656 str.
Principles of Sequencing and Scheduling
ISBN-13: 9781119262565 / Angielski / Twarda / 2018 / 656 str.
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An updated edition of the text that explores the core topics in scheduling theory
The second edition of Principles of Sequencing and Scheduling has been revised and updated to provide comprehensive coverage of sequencing and scheduling topics as well as emerging developments in the field.
Wydanie ilustrowane
1. Introduction
1.1. Introduction to Sequencing and Scheduling
1.2. Scheduling Theory
1.3. Philosophy and Coverage of the Book
2. Single–Machine Sequencing
2.1. Introduction
2.2. Preliminaries
2.3. Problems without Due Dates: Elementary Results
2.3.1. Flowtime and Inventory
2.3.2. Minimizing Total Flowtime
2.3.3. Minimizing Total Weighted Flowtime
2.4. Problems with Due Dates: Elementary Results
2.4.1. Lateness Criteria
2.4.2. Minimizing the Number of Tardy Jobs
2.4.3. Minimizing Total Tardiness
2.5. Flexibility in the Basic Model
2.5.1. Due Dates as Decisions
2.5.1. Job Selection Decisions
2.6. Summary
3. Optimization Methods for the Single–Machine Problem
3.1. Introduction
3.2. Adjacent Pairwise Interchange Methods
3.3. A Dynamic Programming Approach
3.4. Dominance Properties
3.5. A Branch and Bound Approach
3.6. Integer Programming
3.7. Summary
4. Heuristic Methods for the Single–Machine Problem
4.1. Introduction
4.2. Dispatching and Construction Procedures
4.3. Random Sampling
4.4. Neighborhood Search Techniques
4.5. Tabu Search
4.6. Simulated Annealing
4.7. Genetic Algorithms
4.8. The Evolutionary Solver
4.9. Summary
5. Earliness and Tardiness Costs
5.1. Introduction
5.2. Minimizing Deviations from a Common Due Date
5.2.1. Four Basic Results
5.2.2. Due Dates as Decisions
5.3. The Restricted Version
5.4. Asymmetric Earliness and Tardiness Costs
5.5. Quadratic Costs
5.6. Job–Dependent Costs
5.7. Distinct Due Dates
5.8. Summary
6. Sequencing for Stochastic Scheduling
6.1. Introduction
6.2. Basic Stochastic Counterpart Models
6.3. The Deterministic Counterpart
6.4. Minimizing the Maximum Cost
6.5. The Jensen Gap
6.6. Stochastic Dominance and Association
6.7. Using Analytic Solver Platform
6.8. Non–Probabilistic Approaches: Fuzzy and Robust Scheduling
6.9. Summary
7. Safe Scheduling
7.1. Introduction
7.2. Basic Stochastic Counterpart Models
7.2.1. Sample–Based Analysis
7.2.2. The Normal Model
7.3. Trading Off Tightness and Tardiness
7.3.1. An Objective Function for the Trade–Off
7.3.2. The Normal Model
7.3.3. A Branch and Bound Solution
7.4. The Stochastic E/T Problem
7.5. Using the Lognormal Distribution
7.6. Setting Release Dates
7.7. The Stochastic U–problem: A Service–Level Approach
7.8. The Stochastic U–problem: An Economic Approach
7.9. Summary
8. Extensions of the Basic Model
8.1. Introduction
8.2. Nonsimultaneous Arrivals
8.2.1. Minimizing the Makespan
8.2.2. Minimizing Maximum Tardiness
8.2.3. Other Measures of Performance
8.3. Related Jobs
8.3.1. Minimizing Maximum Tardiness
8.3.2. Minimizing Total Flowtime with Strings
8.3.3. Minimizing Total Flowtime with Parallel Chains
8.4. Sequence–Dependent Setup Times
8.4.1. Dynamic Programming Solutions
8.4.2. Branch and Bound Solutions
8.4.3. Heuristic Solutions
8.5. Stochastic Traveling Salesperson Models
8.6. Summary
9. Parallel–Machine Models
9.1. Introduction
9.2. Minimizing the Makespan
9.2.1. Nonpreemptable Jobs
9.2.2. Nonpreemptable Related Jobs
9.2.3. Preemptable Jobs
9.3. Minimizing Total Flowtime
9.4. Stochastic Models
9.4.1. The Makespan Problem with Exponential Processing Times
9.4.2. Safe Scheduling with Parallel Machines
9.5. Summary
10. Flow Shop Scheduling
10.1. Introduction
10.2. Permutation Schedules
10.3. The Two–Machine Problem
10.3.1. Johnson′s Rule
10.3.2. A Proof of Johnson′s Rule
10.3.3. The Model with Time Lags
10.3.4. The Model with Setups
10.4. Special Cases of the Three–Machine Problem
10.5. Minimizing the Makespan
10.5.1. Branch and Bound Solutions
10.5.2. Integer Programming Solutions
10.5.3. Heuristic Solutions
10.6. Variations of the m–Machine Model
10.6.1. Ordered Flow Shops
10.6.2. Flow Shops with Blocking
10.6.3. No–Wait Flow Shops
10.7. Summary
11. Stochastic Flow Shop Scheduling
11.1. Introduction
11.2. Stochastic Counterpart Models
11.3. Safe Scheduling Models with Stochastic Independence
11.4. Flow Shops with Linear Association
11.5. Empirical Observations
11.6. Summary
12. Lot Streaming Procedures for the Flow Shop
12.1. Introduction
12.2. The Basic Two–Machine Model
12.2.1. Preliminaries
12.2.2. The Continuous Version
12.2.3. The Discrete Version
12.2.4. Models with Setups
12.3. The Three–Machine Model with Consistent Sublots
12.3.2. The Continuous Version
12.3.3. The Discrete Version
12.4. The Three–Machine Model with Variable Sublots
12.4.1. Item and Batch Availability
12.4.2. The Continuous Version
12.4.3. The Discrete Version
12.4.4. Computational Experiments
12.5. The Fundamental Partition
12.5.1. Defining the Fundamental Partition
12.5.2. A Heuristic Procedure for s Sublots
12.6. Summary
13. Scheduling Groups of Jobs
13.1. Introduction
13.2. Scheduling Job Families
13.2.1. Minimizing Total Weighted Flowtime
13.2.2. Minimizing Maximum Lateness
13.2.3. Minimizing Makespan in the Two–Machine Flow Shop
13.3. Scheduling with Batch Availability
13.4. Scheduling with a Batch Processor
13.4.1. Minimizing the Makespan with Dynamic Arrivals
13.4.2. Minimizing Makespan in the Two–Machine Flow Shop
13.4.3. Minimizing Total Flowtime with Dynamic Arrivals
13.4.4. Batch–Dependent Processing Times
13.5. Summary
14. The Job Shop Problem
14.1. Introduction
14.2. Types of Schedules
14.3. Schedule Generation
14.4. The Shifting Bottleneck Procedure
14.4.1. Bottleneck Machines
14.4.2. Heuristic and Optimal Solutions
14.5. Neighborhood Search Heuristics
14.6. Summary
15. Simulation Models for the Dynamic Job Shop
15.1. Introduction
15.2. Model Elements
15.3. Types of Dispatching Rules
15.4. Reducing Mean Flowtime
15.5. Meeting Due Dates
15.5.1. Background
15.5.2. Some Clarifying Experiments
15.5.3. Experimental Results
15.6. Summary
16. Network Methods for Project Scheduling
16.1. Introduction
16.2. Logical Constraints and Network Construction
16.3. Temporal Analysis of Networks
16.4. The Time/Cost Trade–off
16.5. Traditional Probabilistic Network Analysis
16.5.1. The PERT Method
16.5.2. Theoretical Limitations of PERT
16.6. Summary
17. Resource–Constrained Project Scheduling
17.1. Introduction
17.2. Extending the Job Shop Model
17.3. Extending the Project Model
17.4. Heuristic Construction and Search Algorithms
17.4.1. Construction Heuristics
17.4.2. Neighborhood Search Improvement Schemes
17.4.3. Selecting Priority Lists
17.5. Stochastic Sequencing with Limited Resources
17.6. Summary
18. Project Analytics
18.1. Introduction
18.2. Basic Partitioning
18.3. Correcting for Rounding
18.4. Accounting for the Parkinson Effect
18.5. Identifying Mixtures
18.6. Addressing Subjective Estimation Bias
18.7. Linear Association
18.7.1. Systemic Bias
18.7.2. Cross–Validation
18.7.3. Using Nonparametric Bootstrap Sampling
18.8. Summary
19. PERT21: Analytics–Based Safe Project Scheduling
19.1. Introduction
19.2. Stochastic Balance Principles for Activity Networks
19.2.1. The Assembly Coordination Model
19.2.2. Balancing a General Project Network
19.2.3. Additional Examples
19.3. Hierarchical Balancing and Progress Payments
19.4. Crashing Stochastic Activities
19.5. Summary
Appendices
A. Practical Processing Time Distributions
A.1. Important Processing Time Distributions
A.1.1. The Uniform Distribution
A.1.2. The Exponential Distribution
A.1.3. The Normal Distribution
A.1.4. The Lognormal Distribution
A.1.5. The Parkinson Distribution
A.2. Mixtures of Distributions
A.3. Increasing and Decreasing Completion Rates
A.4. Stochastic Dominance
A.5. Linearly–Associated Processing Times
B. The Critical Ratio Rule
B.1. A Basic Trade–Off Problem
B.2. Optimal Policy for Discrete Probability Models
B.3. A Special Discrete Case: Equally–Likely Outcomes
B.4. Optimal Policy for Continuous Probability Models
B.5. A Special Continuous Case: The Normal Distribution
B.6. Calculating d + E(T) for the Normal Distribution
B.7. Calculations for the Lognormal Distribution
Index
Kenneth R. Baker, PhD, is Nathaniel Leverone Professor of Management at the Tuck School of Business and INFORMS Fellow. He is a Founding Associate Editor for the International Journal of Planning and Scheduling.
Dan Trietsch, PhD, is an independent researcher and consultant in scheduling and project analytics, with extensive teaching experience, mostly at the graduate level. He is an Area Editor for the International Journal of Information Technology Project Management and a Board Member of the International Journal of Planning and Scheduling.
An updated edition of the text that explores the core topics in scheduling theory
The second edition of Principles of Sequencing and Scheduling has been revised and updated to provide comprehensive coverage of sequencing and scheduling topics as well as emerging developments in the field. The text offers balanced coverage of deterministic models and stochastic models and includes new developments in safe scheduling and project scheduling, including coverage of project analytics. These new topics help bridge the gap between classical scheduling and actual practice. The authors noted experts in the field present a coherent and detailed introduction to the basic models, problems, and methods of scheduling theory.
This book offers an introduction and overview of sequencing and scheduling and covers such topics as single–machine and multi–machine models, deterministic and stochastic problem formulations, optimization and heuristic solution approaches, and generic and specialized software methods. This new edition adds coverage on topics of recent interest in shop scheduling and project scheduling. This important resource:
- Offers comprehensive coverage of deterministic models as well as recent approaches and developments for stochastic models
- Emphasizes the application of generic optimization software to basic sequencing problems and the use of spreadsheet–based optimization methods
- Includes updated coverage on safe scheduling, lognormal modeling, and job selection
- Provides basic coverage of robust scheduling as contrasted with safe scheduling
- Adds a new chapter on project analytics, which supports the PERT21 framework for project scheduling in a stochastic environment.
- Extends the coverage of PERT 21 to include hierarchical scheduling
- Provides end–of–chapter references and access to advanced Research Notes, to aid readers in the further exploration of advanced topics
Written for upper–undergraduate and graduate level courses covering such topics as scheduling theory and applications, project scheduling, and operations scheduling, the second edition of Principles of Sequencing and Scheduling is a resource that covers scheduling techniques and contains the most current research and emerging topics.
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