Project Scheduling under Limited Resources: Models, Methods, and ApplicationsApproaches 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. |
Contents
Introduction | 1 |
Project Scheduling Models | 5 |
212 Deriving Time Windows | 7 |
213 Mathematical Programming Formulation | 9 |
22 Variants and Extensions | 11 |
222 Generalized Temporal Constraints | 15 |
223 Generalized Resource Constraints | 17 |
224 Alternative Objectives | 20 |
553 Impact of Genetic Operators | 99 |
56 Extending the Genetic Algorithm | 100 |
562 Extended Representation | 101 |
563 Improved Initial Population | 103 |
564 Adapting the Genetic Operators | 105 |
566 Configuration of the Extended Genetic Algorithm | 112 |
Evaluation of SingleMode Heuristics | 115 |
62 Computational Results | 117 |
225 Multiple Projects | 22 |
231 Motivation | 23 |
232 Different Dimensions | 25 |
233 Different Types | 28 |
234 Additional Features | 30 |
Exact MultiMode Algorithms | 33 |
31 Enumeration Schemes | 34 |
311 The Precedence Tree | 35 |
312 Mode and Delay Alternatives | 37 |
313 Mode and Extension Alternatives | 39 |
32 Bounding Rules | 41 |
322 Preprocessing | 42 |
323 Dominating Sets of Schedules | 43 |
324 The Cutset Rule | 45 |
325 Immediate Selection | 46 |
326 A Precedence Tree Specific Rule | 47 |
33 Theoretical Comparison of Schedule Enumeration | 48 |
332 Enumeration with Bounding Rules | 51 |
34 Computational Results | 55 |
342 Comparison of the Algorithms | 57 |
Classification of SingleMode Heuristics | 61 |
41 Schedule Generation Schemes | 62 |
412 Parallel Schedule Generation Scheme | 64 |
42 Priority Rule Based Heuristics | 65 |
421 Priority Rules | 66 |
422 Proposed Methods | 67 |
43 Metaheuristic Approaches | 70 |
432 Representations | 73 |
433 Proposed Methods | 78 |
44 Other Heuristics | 80 |
443 Further Approaches | 81 |
SingleMode Genetic Algorithms | 83 |
511 The Theory of Evolution | 84 |
512 Basic Genetic Algorithm Scheme | 85 |
52 Activity List Based Genetic Algorithm | 86 |
522 Crossover and Mutation | 87 |
523 Selection | 90 |
53 Random Key Based Genetic Algorithm | 91 |
532 Crossover and Mutation | 92 |
54 Priority Rule Based Genetic Algorithm | 93 |
542 Crossover and Mutation | 94 |
551 Configuration of the Genetic Algorithms | 95 |
552 Comparison of the Genetic Algorithms | 97 |
621 Best Heuristics | 118 |
622 Performance of Metaheuristics | 122 |
624 Impact of Schedule Generation Scheme | 123 |
625 Impact of Resource Parameters | 124 |
626 Computation Times | 126 |
MultiMode Genetic Algorithm | 129 |
71 Components of the Genetic Algorithm | 130 |
711 Individuals and Fitness | 131 |
712 Initial Population | 132 |
713 Crossover and Mutation | 133 |
72 Improving Schedules by Local Search | 135 |
721 Single Pass Improvement | 136 |
722 Multi Pass Improvement | 137 |
723 Inheritance Beyond the Genetic Metaphor | 138 |
731 Configuration of the Algorithm | 139 |
732 Population Analysis | 141 |
733 Comparison with other Heuristics | 144 |
Case Studies | 149 |
811 Problem Description | 150 |
812 Modeling Approach | 152 |
813 Computational Results for Original Data | 156 |
814 Optimality Issues | 157 |
815 Impact of Data Variations | 159 |
816 Concluding Remarks | 162 |
82 Selecting Market Research Interviewers | 163 |
821 Problem Description | 164 |
822 Modeling Approach | 168 |
823 Dynamic Planning Environment | 173 |
Conclusions | 177 |
Test Instances | 181 |
A2 Instance Sets Generated by ProGen | 182 |
A21 SingleMode Instance Sets | 183 |
A22 MultiMode Instance Sets | 184 |
Solving the MRCPSP using AMPL | 187 |
B2 AMPLData File for the MRCPSP | 190 |
| 193 | |
List of Abbreviations | 209 |
List of Basic Notation | 211 |
List of Tables | 215 |
| 217 | |
| 219 | |
Other editions - View all
Project Scheduling under Limited Resources: Models, Methods, and Applications Sönke Hartmann No preview available - 2011 |
Common terms and phrases
activity list based Algorithm 3.1 approach Average deviations bin packing problem Bounding Rule capacity Chapter consider crossover current partial schedule decision point decoding procedure defined delay alternatives duration eligible activities employed enumerated extended extension alternatives finish genetic algorithm genetic operators given heuristics improvement individual initial population instance sets ji+1 Kolisch 120 Left Shift Rule local search makespan metaheuristic mode alternative mode assignment multi multi-mode left shift mutation nonrenewable resource number of interviews obtain optimal solution Özdamar packing problem parallel SGS parameters performed precedence feasible precedence relations precedence tree priority rule based priority value ProGen project instance project scheduling models project scheduling problem random key RCPSP renewable resources repetitions resource constraints resource strength resource-constrained project scheduling right shifts schedule generation scheme search space Section selection semi-active serial SGS simulated annealing single-mode strip packing Subsection Table tabu search Theorem unary operator variant
Popular passages
Page 195 - RW Conway, WL Maxwell and LW Miller, Theory of Scheduling (Addison Wesley, Reading, MA, 1967).