Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | Undetermined |
| Veröffentlicht: |
o. O.
dissertation.com
2001
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XII, 260 S. graph. Darst. |
| ISBN: | 1581121415 |
Internformat
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Datensatz im Suchindex
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| adam_text | Table of Contents
1 Introduction 1
11 Goal of Dissertation 6
1.2 Structure of Dissertation 7
1.3 Revenue Management Defined 8
1.4 Origins of Revenue Management 9
1.5 Seat Inventory Control 12
1.6 Booking Limits and Nesting 15
1.7 Levels of Seat Inventory Control 17
1.7.1 First Come, First Served 18
1.7.2 Leg level 19
1.7.3 Virtual Nesting 20
1.7.4 Origin and Destination Itinerary Level 21
1.8 Optimization Methods 23
18.1 Expected Marginal Seat Revenue (EMSR) 24
1.8.2 Network Formulations 26
1.8.3 Deterministic Linear Program 27
1.8.4 Probabilistic Nonlinear Program 28
2 Forecasting Methods: Literature Review and Current Practices 30
2.1 Types of Forecasting 32
2.2 Macro level Forecasting 33
2.3 Passenger Choice Modeling 34
2.4 Micro Level Forecasting 34
2.4.1 Exponential Smoothing 38
2.4.2 Moving Average 39
2.4.3 Linear Regression 40
2.4.4 Additive Pickup Model 41
2.4.5 Multiplicative Pickup Model 46
2.5 Censored Data 48
2.6 Cost of Using Censored Data 49
2.6.1 Sensitivity Analysis 51
2.7 Methods for Handling Incomplete Data 52
2.7.1 Complete Data Methods 53
2.7.2 Imputation Methods 54
2.7.3 Statistical Model Methods 55
2.8 Unconstraining Censored Data 56
2.8.1 The Goal of Unconstraining Censored Data 57
v
2.8.2 Capture the Data Directly 62
2.8.3 Ignore the Censored Data 63
2.8.4 Discard the Censored Data 64
2.8.5 Mean Imputation Method 66
2 8.6 Median Imputation Method 69
2.8.7 Percentile Imputation Method 71
2.8.8 Multiplicative Booking Profile Method 73
2.8.9 Expectation Maximization (EM) Algorithm 78
2.8.10 The EM Algorithm for Unconstraining Censored Demand Data 80
2 8.11 Details of the E Step for the EM Algorithm 84
2.8.12 Details of the M Step for the EM Algorithm 86
2.8.13 Numerical Illustration for the EM Algorithm 88
2.8.14 Projection Detruncation Method 91
2.8.15 Details of the E Step for the PD Method 96
2.8.16 Details of the M Step for the PD Method 98
2.8.17 Numerical Illustration for the PD Algorithm 99
3 Modeling the Censored Data Problem 102
3.1 Censored Data Simulation 104
3.1.1 Demand Generation and Data Collection 105
3 1.2 Simulate Censoring of the Data 107
3 2 Simulation Validation 116
3 3 Design of Experiments 119
3.3.1 Factors and Treatments 119
3.3.2 Experimental Units 123
3.3.3 Performance Measurements 124
3 34 Definition of Constrained Data 128
3.3.5 Number of Replications 131
3.4 Experiment Procedure 133
3.2 Distribution Analysis 136
4 Analysis and Comparison of Unconstraining Methods 145
4.1 Experiment Results for the Ignore Method 147
4 2 Experiment Results for the Discard Method 156
4 3 Experiment Results for the Mean Imputation Method 167
4.4 Experiment Results for the Median Imputation Method 177
4 5 Experiment Results for the Percentile Imputation Method 186
4 6 Experiment Results for the Booking Profile Method 194
4.7 Experiment Results for the Expectation Maximization Algorithm 206
4 7 I Rate of Convergence for the EM Algorithm 215
4.8 Extended EM Algorithm 217
4.8.1 Extension to the EM Algorithm 219
4 8.2 Experiment Results lor the Extended EM Algorithm 221
4.9 Experiment Results for the Projection Detruncation Algorithm 230
4.9.1 Sensitivity to Tau 239
4.9.2 Rate of Convergence for the PD Method 239
4 10 Comparison of Unconstraining Methods Performance 241
4.10.1 A Note on the Performance Results 245
5 Conclusion 247
5.1 Contributions 248
5.2 Future Research Directions 249
5.2.1 Underestimates of Demand 249
5.2 2 Overestimates of Demand 251
5.3 Implementation Issues 252
Bibliography 254
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| indexdate | 2024-07-09T20:06:13Z |
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| isbn | 1581121415 |
| language | Undetermined |
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| physical | XII, 260 S. graph. Darst. |
| publishDate | 2001 |
| publishDateSearch | 2001 |
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| publisher | dissertation.com |
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| spelling | Zeni, Richard H. Verfasser aut Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data Richard H. Zeni o. O. dissertation.com 2001 XII, 260 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Test (DE-588)4059549-3 gnd rswk-swf Luftverkehrsgesellschaft (DE-588)4036559-1 gnd rswk-swf Luftverkehr (DE-588)4036597-9 gnd rswk-swf Nachfrage (DE-588)4041036-5 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Revenue Management (DE-588)4444408-4 gnd rswk-swf Theorie (DE-588)4059787-8 gnd rswk-swf Schätzung (DE-588)4193791-0 gnd rswk-swf Luftverkehrsgesellschaft (DE-588)4036559-1 s Revenue Management (DE-588)4444408-4 s DE-604 Luftverkehr (DE-588)4036597-9 s Prognoseverfahren (DE-588)4358095-6 s Nachfrage (DE-588)4041036-5 s Schätzung (DE-588)4193791-0 s Test (DE-588)4059549-3 s Theorie (DE-588)4059787-8 s HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013117895&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zeni, Richard H. Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data Test (DE-588)4059549-3 gnd Luftverkehrsgesellschaft (DE-588)4036559-1 gnd Luftverkehr (DE-588)4036597-9 gnd Nachfrage (DE-588)4041036-5 gnd Prognoseverfahren (DE-588)4358095-6 gnd Revenue Management (DE-588)4444408-4 gnd Theorie (DE-588)4059787-8 gnd Schätzung (DE-588)4193791-0 gnd |
| subject_GND | (DE-588)4059549-3 (DE-588)4036559-1 (DE-588)4036597-9 (DE-588)4041036-5 (DE-588)4358095-6 (DE-588)4444408-4 (DE-588)4059787-8 (DE-588)4193791-0 |
| title | Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data |
| title_auth | Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data |
| title_exact_search | Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data |
| title_full | Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data Richard H. Zeni |
| title_fullStr | Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data Richard H. Zeni |
| title_full_unstemmed | Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data Richard H. Zeni |
| title_short | Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data |
| title_sort | improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data |
| topic | Test (DE-588)4059549-3 gnd Luftverkehrsgesellschaft (DE-588)4036559-1 gnd Luftverkehr (DE-588)4036597-9 gnd Nachfrage (DE-588)4041036-5 gnd Prognoseverfahren (DE-588)4358095-6 gnd Revenue Management (DE-588)4444408-4 gnd Theorie (DE-588)4059787-8 gnd Schätzung (DE-588)4193791-0 gnd |
| topic_facet | Test Luftverkehrsgesellschaft Luftverkehr Nachfrage Prognoseverfahren Revenue Management Theorie Schätzung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013117895&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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