Stochastic modeling and optimization with applications in queues, finance, and supply chains
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| Sprache: | English |
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Springer
2003
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| 245 | 1 | 0 | |a Stochastic modeling and optimization |b with applications in queues, finance, and supply chains |c David D. Yao ... (eds.) |
| 264 | 1 | |a New York [u.a.] |b Springer |c 2003 | |
| 300 | |a XI, 468 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 7 | |a Optimaliseren |2 gtt | |
| 650 | 7 | |a Stochastische modellen |2 gtt | |
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| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Wirtschaft | |
| 650 | 4 | |a Economics |x Mathematical models |v Congresses | |
| 650 | 4 | |a Finance |x Mathematical models |v Congresses | |
| 650 | 4 | |a Stochastic analysis |v Congresses | |
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Datensatz im Suchindex
| DE-BY-TUM_call_number | 0102 MAT 914f 2006 A 1459 |
|---|---|
| DE-BY-TUM_katkey | 1535804 |
| DE-BY-TUM_location | 01 |
| DE-BY-TUM_media_number | 040020290679 |
| _version_ | 1820803787069063168 |
| adam_text | Contents
Preface v
1 Discrete time Singularly Perturbed Markov Chains 1
G. Yin and Q. Zhang
1.1 Singularly Perturbed Markov Chains 2
1.1.1 Motivation 2
1.1.2 Preliminary 3
1.1.3 Singularly Perturbed Models 5
1.1.4 Motivating Examples 9
1.2 Asymptotic Expansions 12
1.3 Occupation Measures 18
1.4 Nonstationary Markov Chains and Applications 23
1.4.1 Asymptotic Properties for Smooth Transition Matrices 23
1.4.2 Bounded and Measurable Transition Matrices .... 29
1.4.3 Applications to Nearly Optimal Controls 32
1.5 Notes and Remarks 36
1.5.1 Notes on the Literature 36
1.5.2 Possible Future Research Topics 37
1.6 References 38
2 Nearly Optimal Controls of Markovian Systems 43
Q. Zhang, R.H. Liu, and G. Yin
2.1 Singularly Perturbed MDP 44
2.1.1 Irreducible MDP under Discounted Cost 46
2.1.2 Irreducible MDP under Long Run Average Cost . . 51
2.1.3 MDP with General Transition Matrices 54
2.1.4 Historical Notes 61
2.2 Hybrid LQG Control 62
2.2.1 Aggregation and Approximation 66
2.2.2 Asymptotic Optimality 71
2.2.3 Hybrid LQG with General Transition Matrices ... 75
2.2.4 A Numerical Example 80
2.2.5 Historical Notes 82
2.3 Conclusions 83
2.4 References 83
3 Stochastic Approximation, with Applications 87
Han Fu Chen
viii Contents
3.1 SA Algorithms 87
3.2 General Convergence Theorems by TS Method 90
3.3 Convergence Theorems Under State Independent Conditions 99
3.4 Applications 102
3.4.1 Application to Optimization 102
3.4.2 Application to Signal Processing 105
3.5 Notes 107
3.6 References 108
4 Performance Potential Based Optimization and MDPs 111
Xi Ren Cao
4.1 Sensitivity Analysis and Performance Potentials 112
4.2 Markov Decision Processes 116
4.3 Problems with Discounted Performance Criteria 118
4.4 Single Sample Path Based Implementations 121
4.5 Time Aggregation 123
4.6 Connections to Perturbation Analysis 126
4.7 Application Examples 128
4.8 Notes 130
4.9 References 134
5 An Interior Point Approach to Multi Stage Stochastic Pro¬
gramming 137
Shuzhong Zhang
5.1 Two Stage Stochastic Linear Programming 139
5.2 A Case Study 142
5.3 Multiple Stage Stochastic Programming 144
5.4 An Interior Point Method 146
5.5 Finding Search Directions 156
5.6 Model Diagnosis 164
5.7 Notes 167
5.8 References 168
6 A Brownian Model of Stochastic Processing Networks 171
Hong Chen
6.1 Preliminaries 172
6.2 Stochastic Processing Network Model 174
6.3 Examples of Stochastic Processing Networks 176
6.3.1 Scheduling Control of Multiclass Queueing Network 176
6.3.2 A Simple Queueing Network with both Scheduling
and Routing 177
6.3.3 An Assemble To Order System 179
6.4 Brownian Model for Stochastic Processing Network 181
6.4.1 Comparison to Harrison s Brownian Model 183
6.4.2 Extensions 184
Contents ix
6.5 Brownian Approximation via Strong Approximation .... 185
6.6 Notes 186
6.7 Appendix: Strong Approximation vs. Heavy Traffic Approx¬
imation 187
6.8 References 191
7 Stability of General Processing Networks 193
Jim Dai and Otis B. Jennings
7.1 Motivating Simulations 195
7.2 Open Processing Networks 201
7.2.1 Network Description 202
7.2.2 The Standard Network and Dispatch Policies .... 205
7.2.3 Production Policies and Sensible Policies 206
7.2.4 Rate Stability 209
7.3 Network and Fluid Model Equations 210
7.3.1 Network Dynamics 210
7.3.2 Fluid Models 214
7.3.3 Connection between Processing Networks and Fluid
Models 217
7.4 Connection between Artificial and Standard Fluid Models . 219
7.4.1 Batch Processing Networks and Normal Policies . . 219
7.4.2 Stability under Sensible Production Policies 222
7.5 Examples of Stable Policies 223
7.5.1 Early Steps First 223
7.5.2 Generalized Round Robin 228
7.6 Extensions 230
7.7 Appendix 232
7.7.1 Departures As a Function of Server Effort 232
7.7.2 Proofs of Lemmas 7.12 and 7.18 236
7.8 Notes 240
7.9 References 241
8 Large Deviations, Long Range Dependence, and Queues 245
C. S. Chang, David D. Yao and Tim Zajic
8.1 Fractional Brownian Motion and a Related Filter 246
8.2 Moderate Deviations for Sample Path Processes 248
8.3 MDP for the Filtered Process 252
8.4 Queueing Applications: The Workload Process 258
8.5 Verifying the Key Assumptions 267
8.6 Notes 274
8.7 References 275
9 Markowitz s World in Continuous Time, and Beyond 279
Xun Yu Zhou
9.1 The Mean Variance Portfolio Selection Model 280
x Contents
9.2 A Stochastic LQ Control Approach 283
9.3 Efficient Frontier: Deterministic Market Parameters .... 285
9.4 Efficient Frontier: Random Adaptive Market Parameters . . 292
9.5 Efficient Frontier: Markov Modulated Market Parameters . 296
9.6 Efficient Frontier: No Short Selling 299
9.7 Mean Variance Hedging 300
9.8 Notes 303
9.9 References 305
10 Variance Minimization in Stochastic Systems 311
Duan Li, Fucai Qian and Peilin Fu
10.1 Variance Minimization Problem 311
10.2 General Variance Minimization Problem 314
10.3 Variance Minimization in Dynamic Portfolio Selection ... 316
10.4 Variance Minimization in Dual Control 323
10.5 Notes 330
10.6 References 330
11 A Markov Chain Method for Pricing Contingent Claims 333
Jin Chuan Duan, Genevieve Gauthier and Jean Guy Simonato
11.1 The Markov Chain Pricing Method 334
11.2 The Black Scholes (1973) Pricing Model 336
11.2.1 Choosing the Set of Asset Prices 337
11.2.2 Computing Transition Probabilities and Option Prices338
11.2.3 An Illustrative Example 339
11.2.4 A Markov Chain Interpretation of Binomial Tree . . 341
11.2.5 Numerical Examples 343
11.3 The GARCH Pricing Model 347
11.3.1 Choosing the Set of Discrete Prices and Volatilities . 349
11.3.2 Computing Transition Probabilities and Option Prices350
11.3.3 Numerical Examples 351
11.4 Valuing Exotic Options 355
11.5 Appendix: The Conditional Expected Value of hr and h .. 360
11.6 References 361
12 Stochastic Network Models and Optimization of a Hospital
System 363
Xiuli Chao, Liming Liu and Shaohui Zheng
12.1 A Multi Site Service Network Model 364
12.2 Patient Flow Management 366
12.3 Capacity Design 371
12.4 Switching Costs and Quality of Service 382
12.5 Insights and Future Research Directions 387
12.6 Notes 390
12.7 References 391
Contents xi
13 Optimal Airline Booking Control with Cancellations 395
Youyi Feng, Ping Lin and Baichun Xiao
13.1 Preliminaries 396
13.1.1 Model Description 396
13.1.2 Optimality Conditions and the Value Function . . . 398
13.2 The Minimum Acceptable Fare and Threshold Control . . . 400
13.2.1 The Minimum Acceptable Fare 400
13.2.2 Properties of MAF 402
13.2.3 Threshold Control and Computation of the Value
Function 412
13.3 Extensions of the Basic Model 414
13.3.1 Time Dependent Air Fares 414
13.3.2 Fare Dependent Partial Refunds 414
13.4 Numerical Experiments 418
13.5 Notes 421
13.6 References 424
14 Information Revision and Decision Making in Supply Chain
Management 429
Houmin Yan and Hanqin Zhang
14.1 Industrial Examples 429
14.1.1 The Procurement of Micro Controller 430
14.1.2 Analysis of Demand Forecast Data 431
14.1.3 The Deregulated Energy Markets 435
14.2 A Multi Period, Two Decision Model 435
14.3 A One Period, Multi Information Revision Model 443
14.4 Applications 450
14.4.1 Decision Making with Two Procurement Alternatives 450
14.4.2 The Application to Deregulated Energy Markets . . 450
14.5 Notes 451
14.6 References 455
About the Contributors 459
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| genre_facet | Konferenzschrift 2001 Peking |
| id | DE-604.BV014700277 |
| illustrated | Illustrated |
| indexdate | 2024-12-23T15:59:22Z |
| institution | BVB |
| isbn | 0387955828 |
| language | English |
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| spellingShingle | Stochastic modeling and optimization with applications in queues, finance, and supply chains Optimaliseren gtt Stochastische modellen gtt Stochastische programmering gtt Toepassingen gtt Mathematisches Modell Wirtschaft Economics Mathematical models Congresses Finance Mathematical models Congresses Stochastic analysis Congresses Optimierung (DE-588)4043664-0 gnd Stochastische Optimierung (DE-588)4057625-5 gnd Stochastisches Modell (DE-588)4057633-4 gnd |
| subject_GND | (DE-588)4043664-0 (DE-588)4057625-5 (DE-588)4057633-4 (DE-588)1071861417 |
| title | Stochastic modeling and optimization with applications in queues, finance, and supply chains |
| title_auth | Stochastic modeling and optimization with applications in queues, finance, and supply chains |
| title_exact_search | Stochastic modeling and optimization with applications in queues, finance, and supply chains |
| title_full | Stochastic modeling and optimization with applications in queues, finance, and supply chains David D. Yao ... (eds.) |
| title_fullStr | Stochastic modeling and optimization with applications in queues, finance, and supply chains David D. Yao ... (eds.) |
| title_full_unstemmed | Stochastic modeling and optimization with applications in queues, finance, and supply chains David D. Yao ... (eds.) |
| title_short | Stochastic modeling and optimization |
| title_sort | stochastic modeling and optimization with applications in queues finance and supply chains |
| title_sub | with applications in queues, finance, and supply chains |
| topic | Optimaliseren gtt Stochastische modellen gtt Stochastische programmering gtt Toepassingen gtt Mathematisches Modell Wirtschaft Economics Mathematical models Congresses Finance Mathematical models Congresses Stochastic analysis Congresses Optimierung (DE-588)4043664-0 gnd Stochastische Optimierung (DE-588)4057625-5 gnd Stochastisches Modell (DE-588)4057633-4 gnd |
| topic_facet | Optimaliseren Stochastische modellen Stochastische programmering Toepassingen Mathematisches Modell Wirtschaft Economics Mathematical models Congresses Finance Mathematical models Congresses Stochastic analysis Congresses Optimierung Stochastische Optimierung Stochastisches Modell Konferenzschrift 2001 Peking |
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