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Kenneth R. (Dartmouth College) Baker, KR Baker, Dan (American University of Armenia) Trietsch

Principles of Sequencing and Scheduling, Second Edition

Name: Principles of Sequencing and Scheduling, Second Edition
Author: Kenneth R. (Dartmouth College) Baker

Gebonden Engels 2018 9781119262565

144,96

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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. 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.

Specificaties

ISBN13:9781119262565

Taal:Engels

Bindwijze:gebonden

Aantal pagina's:656

Uitgever:John Wiley & Sons

Serie:Wiley Series in Operations Research and Management Science

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Inhoudsopgave

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

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