Approaches to project scheduling under resource constraints are discussed in this book. After an overview of different models, it deals with exact and heuristic scheduling algorithms. The focus is on the development of new algorithms. Computational experiments demonstrate the efficiency of the new heuristics. Finally, it is shown how the models and methods discussed here can be applied to projects in research and development as well as market research.
Approaches to project scheduling under resource constraints are discussed in this book. After an overview of different models, it deals with exact and heuristic scheduling algorithms. The focus is on the development of new algorithms. Computational experiments demonstrate the efficiency of the new heuristics. Finally, it is shown how the models and methods discussed here can be applied to projects in research and development as well as market research.
Produktdetails
Produktdetails
Lecture Notes in Economics and Mathematical Systems 478
1 Introduction.- 2 Project Scheduling Models.- 2.1 Basic Model: The RCPSP.- 2.2 Variants and Extensions.- 2.3 Relations to Packing and Cutting Problems.- 3 Exact Multi-Mode Algorithms.- 3.1 Enumeration Schemes.- 3.2 Bounding Rules.- 3.3 Theoretical Comparison of Schedule Enumeration.- 3.4 Computational Results.- 4 Classification of single-Mode Heuristics.- 4.1 Schedule Generation Schemes.- 4.2 Priority Rule Based Heuristics.- 4.3 Metaheuristic Approaches.- 4.4 Other Heuristics.- 5 Single-Mode Genetic Algorithms.- 5.1 Evolution and Optimization.- 5.2 Activity List Based Genetic Algorithm.- 5.3 Random Key Based Genetic Algorithm.- 5.4 Priority Rule Based Genetic Algorithm.- 5.5 Computational Results.- 5.6 Extending the Genetic Algorithm.- 6 Evaluation of Single-Mode Heuristics.- 6.1 Test Design.- 6.2 Computational Results.- 7 Multi-Mode Genetic Algorithm.- 7.1 Components of the Genetic Algorithm.- 7.2 Improving Schedules by Local Search.- 7.3 Computational Results.- 8 Case Studies.- 8.1 Scheduling Medical Research Experiments.- 8.2 Selecting Market Research Interviewers.- 9 Conclusions.- A Test Instances.- A.1 Patterson Instance Set.- A.2 Instance Sets Generated by ProGen.- A.2.1 Single-Mode Instance Sets.- A.2.2 Multi-Mode Instance Sets.- B Solving the MRCPSP using AMPL.- B.1 AMPL-Formulation of the MRCPSP.- B.2 AMPL-Data File for the MRCPSP.- List of Abbreviations.- List of Basic Notation.- List of Tables.- List of Figures.
1 Introduction.- 2 Project Scheduling Models.- 2.1 Basic Model: The RCPSP.- 2.2 Variants and Extensions.- 2.3 Relations to Packing and Cutting Problems.- 3 Exact Multi-Mode Algorithms.- 3.1 Enumeration Schemes.- 3.2 Bounding Rules.- 3.3 Theoretical Comparison of Schedule Enumeration.- 3.4 Computational Results.- 4 Classification of single-Mode Heuristics.- 4.1 Schedule Generation Schemes.- 4.2 Priority Rule Based Heuristics.- 4.3 Metaheuristic Approaches.- 4.4 Other Heuristics.- 5 Single-Mode Genetic Algorithms.- 5.1 Evolution and Optimization.- 5.2 Activity List Based Genetic Algorithm.- 5.3 Random Key Based Genetic Algorithm.- 5.4 Priority Rule Based Genetic Algorithm.- 5.5 Computational Results.- 5.6 Extending the Genetic Algorithm.- 6 Evaluation of Single-Mode Heuristics.- 6.1 Test Design.- 6.2 Computational Results.- 7 Multi-Mode Genetic Algorithm.- 7.1 Components of the Genetic Algorithm.- 7.2 Improving Schedules by Local Search.- 7.3 Computational Results.- 8 Case Studies.- 8.1 Scheduling Medical Research Experiments.- 8.2 Selecting Market Research Interviewers.- 9 Conclusions.- A Test Instances.- A.1 Patterson Instance Set.- A.2 Instance Sets Generated by ProGen.- A.2.1 Single-Mode Instance Sets.- A.2.2 Multi-Mode Instance Sets.- B Solving the MRCPSP using AMPL.- B.1 AMPL-Formulation of the MRCPSP.- B.2 AMPL-Data File for the MRCPSP.- List of Abbreviations.- List of Basic Notation.- List of Tables.- List of Figures.
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