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Job Shop Scheduling with Consideration of Due Dates : Potentials of Local Search Based Solution Techniques

By: Kuhpfahl, Jens.
Material type: TextTextSeries: eBooks on Demand.Produktion und Logistik: Publisher: Wiesbaden : Springer Fachmedien Wiesbaden, 2015Description: 1 online resource (206 p.).ISBN: 9783658102920.Subject(s): Economics | Operations researchGenre/Form: Electronic books.Additional physical formats: Print version:: Job Shop Scheduling with Consideration of Due Dates : Potentials of Local Search Based Solution TechniquesDDC classification: 650 Online resources: Click here to view this ebook.
Contents:
Foreword; Acknowlegdement; Contents; List of Figures; List of Tables; List of Notations; List of Abbreviations; Chapter 1 Introduction; 1.1 Aims and Contributions of the Thesis; 1.2 Overview of the Thesis; 1.3 Publications; Chapter 2 Job Shop Scheduling - Formulation and Modeling; 2.1 Problem Structure; 2.2 Classification into the Scheduling Theory; 2.3 Mathematical Model and Complexity; 2.4 The Disjunctive Graph Model; 2.5 The Concept of the Critical Tree; 2.6 Exemplification on the instance ft06 (f = 1.3) ; Chapter 3 Literature Review; 3.1 Exact Algorithms; 3.2 Dispatching Rules
3.3 Shifting Bottleneck Heuristic3.4 Local Search based Algorithms and Techniques; 3.5 Other Heuristic Approaches; 3.6 Hybrid Approaches; 3.7 Summary; Chapter 4 Neighborhood Definitions for the JSPTWT; 4.1 The Basic Concept of Neighborhood Search; 4.2 Existing Neighborhoods; 4.3 New Neighborhoods; 4.4 Characteristics of the proposed Neighborhoods; 4.4.1 Feasibility Property; 4.4.2 Connectivity Property; 4.4.3 Estimate of the Size of the Neighborhoods; 4.5 Performance Analysis; 4.5.1 Test Suite; 4.5.2 Local Search with a Single Neighborhood Operator
4.5.3 Local Search with Pairs of Neighborhood Operators4.5.4 Local Search with all Neighborhood Operators; 4.6 Summary; Chapter 5 Neighbor Evaluation Procedures in Local Search based Algorithms for solving the JSPTWT; 5.1 Basic Principles; 5.2 A Lower Bound Procedure for the Applica-tion of the CET Neighborhood; 5.3 A Lower Bound Procedure for the Applica-tion of the SCEI Neighborhood; 5.4 A new approach: Heads Updating; 5.5 Performance Test; 5.6 Summary; Chapter 6 Solving the JSPTWT - a new Solution Procedure; 6.1 Metaheuristic Concepts; 6.1.1 Basic Concept of Metaheuristics
6.1.2 Some Metaheuristics6.1.3 The Fitness Landscape: a brief Side Trip; 6.2 Algorithmic Concept for a new Solution Pro-cedure; 6.2.1 Motivation and Overview of the Algorithmic Concept; 6.2.2 Construction Algorithm; 6.2.3 Improvement Algorithm; 6.2.4 Adaptive Components; 6.2.5 Configuration and Parameter Values; Chapter 7 Computational Study; 7.1 Benchmark Instances of the JSPTWT; 7.1.1 Modification of JSP Instances; 7.1.2 Standard Benchmark Set of Singer and Pinedo; 7.1.3 Lawrence's Instances; 7.2 Other Objective Functions; 7.2.1 JSP with minimizing the Total Flow Time
7.2.2 JSP with minimizing the Number of Tardy Jobs7.3 Summary; Chapter 8 Conclusion; Bibliography; Appendix A Applying Neighborhood Operators: the example ft06 (f = 1.3); Appendix B Overview of considered Dispatching Rules; Appendix C Computational Results from the Literature; Appendix D Analysis of the EGRASP result for the problem instance orb08 (f = 1.6); Appendix E Computational Results for the JSPTWU
Summary: Jens Kuhpfahl analyzes the job shop scheduling problem with minimizing the total weighted tardiness as objective. First, he provides a suitable graph representation based on a disjunctive graph formulation. Second, several key components of local search procedures are analyzed and enhanced. The resulting outputs of these investigations contribute to the development of a new solution procedure whose performance quality leads to superior computational results.
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Foreword; Acknowlegdement; Contents; List of Figures; List of Tables; List of Notations; List of Abbreviations; Chapter 1 Introduction; 1.1 Aims and Contributions of the Thesis; 1.2 Overview of the Thesis; 1.3 Publications; Chapter 2 Job Shop Scheduling - Formulation and Modeling; 2.1 Problem Structure; 2.2 Classification into the Scheduling Theory; 2.3 Mathematical Model and Complexity; 2.4 The Disjunctive Graph Model; 2.5 The Concept of the Critical Tree; 2.6 Exemplification on the instance ft06 (f = 1.3) ; Chapter 3 Literature Review; 3.1 Exact Algorithms; 3.2 Dispatching Rules

3.3 Shifting Bottleneck Heuristic3.4 Local Search based Algorithms and Techniques; 3.5 Other Heuristic Approaches; 3.6 Hybrid Approaches; 3.7 Summary; Chapter 4 Neighborhood Definitions for the JSPTWT; 4.1 The Basic Concept of Neighborhood Search; 4.2 Existing Neighborhoods; 4.3 New Neighborhoods; 4.4 Characteristics of the proposed Neighborhoods; 4.4.1 Feasibility Property; 4.4.2 Connectivity Property; 4.4.3 Estimate of the Size of the Neighborhoods; 4.5 Performance Analysis; 4.5.1 Test Suite; 4.5.2 Local Search with a Single Neighborhood Operator

4.5.3 Local Search with Pairs of Neighborhood Operators4.5.4 Local Search with all Neighborhood Operators; 4.6 Summary; Chapter 5 Neighbor Evaluation Procedures in Local Search based Algorithms for solving the JSPTWT; 5.1 Basic Principles; 5.2 A Lower Bound Procedure for the Applica-tion of the CET Neighborhood; 5.3 A Lower Bound Procedure for the Applica-tion of the SCEI Neighborhood; 5.4 A new approach: Heads Updating; 5.5 Performance Test; 5.6 Summary; Chapter 6 Solving the JSPTWT - a new Solution Procedure; 6.1 Metaheuristic Concepts; 6.1.1 Basic Concept of Metaheuristics

6.1.2 Some Metaheuristics6.1.3 The Fitness Landscape: a brief Side Trip; 6.2 Algorithmic Concept for a new Solution Pro-cedure; 6.2.1 Motivation and Overview of the Algorithmic Concept; 6.2.2 Construction Algorithm; 6.2.3 Improvement Algorithm; 6.2.4 Adaptive Components; 6.2.5 Configuration and Parameter Values; Chapter 7 Computational Study; 7.1 Benchmark Instances of the JSPTWT; 7.1.1 Modification of JSP Instances; 7.1.2 Standard Benchmark Set of Singer and Pinedo; 7.1.3 Lawrence's Instances; 7.2 Other Objective Functions; 7.2.1 JSP with minimizing the Total Flow Time

7.2.2 JSP with minimizing the Number of Tardy Jobs7.3 Summary; Chapter 8 Conclusion; Bibliography; Appendix A Applying Neighborhood Operators: the example ft06 (f = 1.3); Appendix B Overview of considered Dispatching Rules; Appendix C Computational Results from the Literature; Appendix D Analysis of the EGRASP result for the problem instance orb08 (f = 1.6); Appendix E Computational Results for the JSPTWU

Jens Kuhpfahl analyzes the job shop scheduling problem with minimizing the total weighted tardiness as objective. First, he provides a suitable graph representation based on a disjunctive graph formulation. Second, several key components of local search procedures are analyzed and enhanced. The resulting outputs of these investigations contribute to the development of a new solution procedure whose performance quality leads to superior computational results.

Description based upon print version of record.

Author notes provided by Syndetics

<p>Dr. Jens Kuhpfahl wrote his dissertation under the supervision of Prof. Dr. Christian Bierwirth at the Chair of Production and Logistics at the University of Halle (Saale).</p>

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