Project scheduling with scarce resources: models, methods, and applications
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Hochschulschrift/Dissertation Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Hamburg
Kovač
1999
|
| Schriftenreihe: | Schriftenreihe innovative betriebswirtschaftliche Forschung und Praxis
100 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021939521&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | X, 300 S. graph. Darst. |
| ISBN: | 3860649043 |
Internformat
MARC
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| 100 | 1 | |a Schirmer, Andreas |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Project scheduling with scarce resources |b models, methods, and applications |c Andreas Schirmer |
| 264 | 1 | |a Hamburg |b Kovač |c 1999 | |
| 300 | |a X, 300 S. |b graph. Darst. | ||
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| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Schriftenreihe innovative betriebswirtschaftliche Forschung und Praxis |v 100 | |
| 502 | |a Zugl.: Kiel, Univ., Diss., 1998 | ||
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Datensatz im Suchindex
| _version_ | 1819339574047932416 |
|---|---|
| adam_text | Contents
Preface i
1. Introduction 1
1.1. Motivation 1
1.2. Scope and Objectives 2
1.3. Synopsis 3
PART I: PROJECT SCHEDULING UNDER RENEWABLE
RESOURCES 7
2. The Resource-Constrained Project Scheduling Problem 9
2.1. Problem Setting 9
2.2. Model Formulation 11
2.3. Theoretical Analysis 13
2.3.1. Complexity Results 13
2.3.2. Classification Results 18
2.3.3. Connectedness Results 20
2.4. Algorithmic Approaches 23
3. Benchmark Instances and Reference Solutions 27
3.1. Instance Generating Approaches 27
3.2. Literature Review 32
3.3. Instance Sets and Reference Solutions 33
4. Deterministic Construcrion Methods 35
4.1. Scheduling Scheines 35
4.1.1. Serial Scheduling 36
4.1.2. Parallel Scheduling 38
iv Contents
4.2. Priority Rules 40
4.3. Experimental Analysis 42
4.3.1. Experimental Design 42
4.3.2. Effect of Priority Rules 43
5. Randomized Construction Methods 45
5.1. Classical Random Sampling Scheines 45
5.1.1. Pure Random Sampling (RAS) 46
5.1.2. Biased Random Sampling (BRS) 47
5.1.3. Regret-Based Biased Random Sampling (RBRS) 47
5.2. New Random Sampling Schemes 48
5.2.1. Motivation 48
5.2.2. Normalized Biased Random Sampling (NBRS) 51
5.2.3. Modified Regret-Based Biased Random Sampling (MRBRS). 52
5.3. A Note on the Rules MIN LST and MIN SLK 53
5.4. Bounding Rules 54
5.5. Experimental Analysis 56
5.5.1. Experimental Design 56
5.5.2. Effect of Alpha 57
5.5.3. Effect of Priority Rules 60
5.5.4. Effect of Alpha - Revisited 64
5.5.5. Effect of Instance Sets 66
5.5.6. Effect of Scheduling Schemes 67
5.5.7. Effect of Random Sampling Schemes 68
5.5.8. Effect of Bounding 71
5.5.9. Effect of Iterations 73
6. Class-Based Construction Methods 77
6.1. Control Schemes 77
6.2. Experimental Analysis 80
6.2.1. Experimental Design 80
6.2.2. Eflfect of Instance Characteristics 80
Contents v
6.2.3. Effect of Sample Size 83
6.2.4. Effect of Instance Size 84
6.3. Algorithmic Design 85
6.4. Effectiveness 87
6.5. Efficiency 88
6.6. Implications for Sampling Methods 89
6.7. Excursus: Case-Based Reasoning 90
6.7.1. Motivation and Concept 90
6.7.2. Applications in Operations Research 90
6.7.3. Implementation 92
6.7.3.1. Case Library Initialization 92
6.7.3.2. Case Selection 93
6.7.3.3. Case Library Updating 93
7. Adaptive Construction Methods 95
7.1. Motivation and Concept 95
7.2. Parameterized Composite Priority Rules 96
7.3. Algorithmic Design 98
7.3.1. Randomized Cooling-Based Control 98
7.3.2. Local Search-Based Control 102
7.3.3. Randomized Local Search-Based Control 105
7.4. Experimental Analysis 105
7.4.1. Experimental Design 105
7.4.2. Randomized Cooling-Based Control 107
7.4.2.1. Effect of Gamma 107
7.4.2.2. Effect of Rule Set Cardinality and Composition 109
7.4.2.3. Effect of Beta 110
7.4.2.4. Efficiency Ill
7.4.3. Local Search-Based Control 111
7.4.3.1. Effect of Sigma Ill
7.4.3.2. Effect of Rule Set Cardinality and Composition 113
7.4.3.3. Efficiency 113
vi Contents
7.4.4. Randomized Local Search-Based Control 114
7.4.4.1. Effect of Sigma 114
7.4.4.2. Effect of Rule Set Cardinality and Composition 115
7.4.4.3. Efficiency 115
8. Comparison of Algorithms 117
PART II: PROJECT SCHEDULING UNDER PARTIALLY
RENEWABLE RESOURCES 121
9. The Partially Renewable Resource-Constrained Project Scheduling
Problem 123
9.1. Partially Renewable Resources 123
9.1.1. Motivation and Concept 123
9.1.2. Relation to Other Resource Concepts 126
9.1.2.1. Nonrenewable Resources 126
9.1.2.2. Renewable Resources 127
9.1.2.3. Renewable Resources with Time-Varying Capacities. 127
9.1.2.4. Partially Renewable Resources of Icmeli Tukel, Rom. 128
9.2. Problem Setting 128
9.3. Model Formulations 130
9.3.1. Simple Model 130
9.3.2. Refined Model 131
9.3.3. Normalized Model 132
9.3.4. Assessment 133
9.4. Modelling Capabilities 135
9.4.1. Interpreting Logical Relations 135
9.4.2. Modelling Logical Relations 136
9.4.3. Modelling Practical Requirements 138
9.4.4. Assessment 141
9.5. Theoretical Analysis 142
9.5.1. Special Properties 142
9.5.2. Complexity Results 14J
Contents vii
9.5.3. Classification Results 148
9.5.4. Connectedness Results 149
9.6. Algorithmic Approaches 150
10. Benchmark Instances and Reference Solutions 155
10.1. Literature Review 155
10.2. Instance Generating 155
10.2.1. Project Data 156
10.2.2. Precedence Relations 157
10.2.3. Period Subsets 159
10.2.4. Resource Demand 162
10.2.5. Resource Capacities 163
10.3. Issues of Instance Feasibility 163
10.3.1. Infeasible Instance Clusters 164
10.3.2. Promising Instance Clusters 167
10.4. Instance Sets and Reference Solutions 167
11. Deterministic Construction Methods 169
11.1. Scheduling Scheme 169
11.2. Priority Rules 172
11.3. Experimental Analysis 176
11.3.1. Experimental Design 176
11.3.2. Effect of Priority Rules 178
12. Randomized Construction Methods 181
12.1. Bounding Rules 181
12.2. Experimental Analysis 183
12.2.1. Experimental Design 183
12.2.2. Effect of Priority Rules 183
12.2.3. Effect of Alpha 187
12.2.4. Effect of Bounding 189
12.2.5. Effectiveness 190
viii Contents
13. Class-Based Construction Methods 193
13.1. Experimental Analysis 193
13.1.1. Experimental Design 193
13.1.2. Effect of Instance Characteristics 193
13.1.3. Effect of Sample Size 195
13.1.4. Effect of Instance Size 196
13.2. Algorithmic Design 196
13.3. Effectiveness 197
14. Adaptive Construction Methods 199
14.1. Algorithmic Design 199
14.2. Experimental Analysis 199
14.2.1. Experimental Design 199
14.2.2. Local Search-Based Control 201
14.2.2.1. Effect of Sigma 201
14.2.2.2. Effect of Rule Set Cardinality and Composition 203
14.2.2.3. Efficiency 206
14.2.3. Randomized Local Search-Based Control 206
14.2.3.1. Effect of Sigma 206
14.2.3.2. Effect of Rule Set Cardinality and Composition 207
14.2.3.3. Efficiency 210
15. Iterative Improvement Methods 211
15.1. Algorithmic Design 212
15.1.1. Local Left-Shifting 212
15.1.2. Global Left-Shifting 219
15.1.3. Hybrid Left-Shifting 221
15.1.4. Some Roads Not Travelled 225
15.2. Experimental Analysis 226
15.2.1. Experimental Design 22i
15.2.2. Effectiveness 227
15.2.3. Efficiency 225
Contents ix
16. Tabu Search Methods 231
16.1. Solution Encoding 232
16.2. Neighborhood 233
16.3. Tabu Status 235
16.4. Move Selection 237
16.5. Initial Solution 237
16.6. Diversification 238
16.7. Some Roads Not Travelled 240
16.8. Experimental Analysis 241
16.8.1. Experimental Design 241
16.8.2. Effect of Neighborhood Parameters 241
16.8.3. Effect of Tabu Tenure 243
16.8.4. Effect of Penalty Weight 243
16.8.5. Effect of Initial Solutions 244
16.8.6. Effect of Diversification 245
16.9. Algorithmic Design 246
16.10. Effectiveness 247
17. Comparison of Algorithms 249
PART III: APPLICATIONS 255
18. A Case Study from Lufthansa Technical Training 257
18.1. Problem Setting 258
18.2. Operational Instance 262
18.3. Algorithmic Design 262
18.4. Computational Results 263
18.5. The Decision Support System 264
18.6. Conclusions and Outlook 267
19. A Case Study from Lufthansa Flight Training 269
19.1. Problem Setting 270
19.2. Operational Instance 274
x Contents
19.3. Algorithmic Design 274
19.4. Computational Results 276
19.5. Conclusions and Outlook 277
20. Summary and Conclusions 279
Notation 283
References 285
Index 297
|
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| genre_facet | Hochschulschrift |
| id | DE-604.BV026358679 |
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| indexdate | 2024-12-20T15:03:05Z |
| institution | BVB |
| isbn | 3860649043 |
| language | English |
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| physical | X, 300 S. graph. Darst. |
| publishDate | 1999 |
| publishDateSearch | 1999 |
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| publisher | Kovač |
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| series | Schriftenreihe innovative betriebswirtschaftliche Forschung und Praxis |
| series2 | Schriftenreihe innovative betriebswirtschaftliche Forschung und Praxis |
| spellingShingle | Schirmer, Andreas Project scheduling with scarce resources models, methods, and applications Schriftenreihe innovative betriebswirtschaftliche Forschung und Praxis Projektplanung (DE-588)4115649-3 gnd Heuristik (DE-588)4024772-7 gnd |
| subject_GND | (DE-588)4115649-3 (DE-588)4024772-7 (DE-588)4113937-9 |
| title | Project scheduling with scarce resources models, methods, and applications |
| title_auth | Project scheduling with scarce resources models, methods, and applications |
| title_exact_search | Project scheduling with scarce resources models, methods, and applications |
| title_full | Project scheduling with scarce resources models, methods, and applications Andreas Schirmer |
| title_fullStr | Project scheduling with scarce resources models, methods, and applications Andreas Schirmer |
| title_full_unstemmed | Project scheduling with scarce resources models, methods, and applications Andreas Schirmer |
| title_short | Project scheduling with scarce resources |
| title_sort | project scheduling with scarce resources models methods and applications |
| title_sub | models, methods, and applications |
| topic | Projektplanung (DE-588)4115649-3 gnd Heuristik (DE-588)4024772-7 gnd |
| topic_facet | Projektplanung Heuristik Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021939521&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV012213500 |
| work_keys_str_mv | AT schirmerandreas projectschedulingwithscarceresourcesmodelsmethodsandapplications |