Project Scheduling under Limited Resources: Models, Methods, and Applications

Front Cover
Springer Science & Business Media, Nov 17, 1999 - Business & Economics - 221 pages
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.
 

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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
Bibliography
193
List of Abbreviations
209
List of Basic Notation
211
List of Tables
215
List of Figures
217
Index
219
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Page 195 - RW Conway, WL Maxwell and LW Miller, Theory of Scheduling (Addison Wesley, Reading, MA, 1967).

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