Stochastic simulation optimization for discrete event systems [[electronic resource] ] : perturbation analysis, ordinal optimization, and beyond / / editors, Chun-Hung Chen, Qing-Shan Jia, Loo Hay Lee |
Pubbl/distr/stampa | Hackensack, NJ, : World Scientific, c2013 |
Descrizione fisica | 1 online resource (274 p.) |
Disciplina | 003/.83 |
Altri autori (Persone) |
ChenChun-Hung <1964->
JiaQing-Shan <1980-> LeeLoo Hay |
Soggetto topico |
Discrete-time systems - Mathematical models
Perturbation (Mathematics) Systems engineering - Computer simulation |
Soggetto genere / forma | Electronic books. |
ISBN | 981-4513-01-6 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Preface; Foreword: A Tribute to a Great Leader in Perturbation Analysis and Ordinal Optimization; Foreword: The Being and Becoming of Perturbation Analysis; Foreword: Remembrance of Things Past; Contents; Part I: Perturbation Analysis; Chapter 1. The IPA Calculus for Hybrid Systems; 1.1. Introduction; 1.2. Perturbation Analysis of Hybrid Systems; 1.2.1. Infinitesimal Perturbation Analysis (IPA): The IPA calculus; 1.3. IPA Properties; 1.4. General Scheme for Abstracting DES to SFM; 1.5. Conclusions and FutureWork; References
Chapter 2. Smoothed Perturbation Analysis: A Retrospective and Prospective Look2.1. Introduction; 2.2. Brief History of SPA; 2.3. Another Example; 2.4. Overview of a General SPA Framework; 2.5. Applications; 2.5.1. Queueing; 2.5.2. Inventory; 2.5.3. Finance; 2.5.4. Stochastic Activity Networks (SANs); 2.5.5. Others; 2.6. Random Retrospective and Prospective Concluding Remarks; Acknowledgements; References; Chapter 3. Perturbation Analysis and Variance Reduction in Monte Carlo Simulation; 3.1. Introduction; 3.2. Systematic and Generic Control Variate Selection 3.2.1. Control variate technique: a brief review3.2.2. Parametrized estimation problems; 3.2.3. Deterministic function approximation and generic CV selection; 3.3. Control Variates for Sensitivity Estimation; 3.3.1. A parameterized estimation formulation of sensitivity estimation; 3.3.2. Finite difference based controls; 3.3.3. Illustrating example; 3.4. Database Monte Carlo (DBMC) Implementation; 3.5. Conclusions; Acknowledgements; References; Chapter 4. Adjoints and Averaging; 4.1. Introduction; 4.2. Adjoints: Classical Setting; 4.3. Adjoints: Waiting Times; 4.4. Adjoints: Vector Recursions 4.5. Averaging4.6. Concluding Remarks; References; Chapter 5. Infinitesimal Perturbation Analysis and Optimization Algorithms; 5.1. Preliminary Remarks; 5.2. Motivation; 5.3. Single-server Queues; 5.3.1. Controlled single-server queue; 5.3.2. Infinitesimal perturbation analysis; 5.3.3. Optimization algorithm; 5.4. Convergence; 5.4.1. Stochastic approximation convergence theorem; 5.4.2. Updating after every busy period; 5.4.3. Updating after every service time; 5.4.4. Example; 5.5. Final Remarks; References; Chapter 6. Simulation-based Optimization of Failure-prone Continuous Flow Lines 6.1. Introduction6.2. Two-machine Continuous Flow Lines; 6.3. Gradient Estimation of a Two-machine Line; 6.4. Modeling Assembly/Disassembly Networks Subject to TDF Failures with Stochastic Fluid Event Graphs; 6.5. Evolution Equations and Sample Path Gradients; 6.6. Optimization of Stochastic Fluid Event Graphs; 6.7. Conclusion; References; Chapter 7. Perturbation Analysis, Dynamic Programming, and Beyond; 7.1. Introduction; 7.2. Perturbation Analysis of Queueing Systems Based on Perturbation Realization Factors; 7.2.1. Performance gradient; 7.2.2. Policy iteration 7.3. Performance Optimization of Markov Systems Based on Performance Potentials |
Record Nr. | UNINA-9910452310203321 |
Hackensack, NJ, : World Scientific, c2013 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Stochastic simulation optimization for discrete event systems : perturbation analysis, ordinal optimization and beyond / / editors, Chun-Hung Chen, George Mason University, USA, Qing-Shan Jia, Tsinghua University, China, Loo Hay Lee, National University of Singapore, Singapore |
Pubbl/distr/stampa | Hackensack, NJ, : World Scientific, c2013 |
Descrizione fisica | 1 online resource (xxviii, 245 pages) : illustrations |
Disciplina | 003/.83 |
Collana | Gale eBooks |
Soggetto topico |
Discrete-time systems - Mathematical models
Perturbation (Mathematics) Systems engineering - Computer simulaton |
ISBN | 981-4513-01-6 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Preface; Foreword: A Tribute to a Great Leader in Perturbation Analysis and Ordinal Optimization; Foreword: The Being and Becoming of Perturbation Analysis; Foreword: Remembrance of Things Past; Contents; Part I: Perturbation Analysis; Chapter 1. The IPA Calculus for Hybrid Systems; 1.1. Introduction; 1.2. Perturbation Analysis of Hybrid Systems; 1.2.1. Infinitesimal Perturbation Analysis (IPA): The IPA calculus; 1.3. IPA Properties; 1.4. General Scheme for Abstracting DES to SFM; 1.5. Conclusions and FutureWork; References
Chapter 2. Smoothed Perturbation Analysis: A Retrospective and Prospective Look2.1. Introduction; 2.2. Brief History of SPA; 2.3. Another Example; 2.4. Overview of a General SPA Framework; 2.5. Applications; 2.5.1. Queueing; 2.5.2. Inventory; 2.5.3. Finance; 2.5.4. Stochastic Activity Networks (SANs); 2.5.5. Others; 2.6. Random Retrospective and Prospective Concluding Remarks; Acknowledgements; References; Chapter 3. Perturbation Analysis and Variance Reduction in Monte Carlo Simulation; 3.1. Introduction; 3.2. Systematic and Generic Control Variate Selection 3.2.1. Control variate technique: a brief review3.2.2. Parametrized estimation problems; 3.2.3. Deterministic function approximation and generic CV selection; 3.3. Control Variates for Sensitivity Estimation; 3.3.1. A parameterized estimation formulation of sensitivity estimation; 3.3.2. Finite difference based controls; 3.3.3. Illustrating example; 3.4. Database Monte Carlo (DBMC) Implementation; 3.5. Conclusions; Acknowledgements; References; Chapter 4. Adjoints and Averaging; 4.1. Introduction; 4.2. Adjoints: Classical Setting; 4.3. Adjoints: Waiting Times; 4.4. Adjoints: Vector Recursions 4.5. Averaging4.6. Concluding Remarks; References; Chapter 5. Infinitesimal Perturbation Analysis and Optimization Algorithms; 5.1. Preliminary Remarks; 5.2. Motivation; 5.3. Single-server Queues; 5.3.1. Controlled single-server queue; 5.3.2. Infinitesimal perturbation analysis; 5.3.3. Optimization algorithm; 5.4. Convergence; 5.4.1. Stochastic approximation convergence theorem; 5.4.2. Updating after every busy period; 5.4.3. Updating after every service time; 5.4.4. Example; 5.5. Final Remarks; References; Chapter 6. Simulation-based Optimization of Failure-prone Continuous Flow Lines 6.1. Introduction6.2. Two-machine Continuous Flow Lines; 6.3. Gradient Estimation of a Two-machine Line; 6.4. Modeling Assembly/Disassembly Networks Subject to TDF Failures with Stochastic Fluid Event Graphs; 6.5. Evolution Equations and Sample Path Gradients; 6.6. Optimization of Stochastic Fluid Event Graphs; 6.7. Conclusion; References; Chapter 7. Perturbation Analysis, Dynamic Programming, and Beyond; 7.1. Introduction; 7.2. Perturbation Analysis of Queueing Systems Based on Perturbation Realization Factors; 7.2.1. Performance gradient; 7.2.2. Policy iteration 7.3. Performance Optimization of Markov Systems Based on Performance Potentials |
Record Nr. | UNINA-9910779883803321 |
Hackensack, NJ, : World Scientific, c2013 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Stochastic simulation optimization for discrete event systems : perturbation analysis, ordinal optimization and beyond / / editors, Chun-Hung Chen, George Mason University, USA, Qing-Shan Jia, Tsinghua University, China, Loo Hay Lee, National University of Singapore, Singapore |
Pubbl/distr/stampa | Hackensack, NJ, : World Scientific, c2013 |
Descrizione fisica | 1 online resource (xxviii, 245 pages) : illustrations |
Disciplina | 003/.83 |
Collana | Gale eBooks |
Soggetto topico |
Discrete-time systems - Mathematical models
Perturbation (Mathematics) Systems engineering - Computer simulaton |
ISBN | 981-4513-01-6 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Preface; Foreword: A Tribute to a Great Leader in Perturbation Analysis and Ordinal Optimization; Foreword: The Being and Becoming of Perturbation Analysis; Foreword: Remembrance of Things Past; Contents; Part I: Perturbation Analysis; Chapter 1. The IPA Calculus for Hybrid Systems; 1.1. Introduction; 1.2. Perturbation Analysis of Hybrid Systems; 1.2.1. Infinitesimal Perturbation Analysis (IPA): The IPA calculus; 1.3. IPA Properties; 1.4. General Scheme for Abstracting DES to SFM; 1.5. Conclusions and FutureWork; References
Chapter 2. Smoothed Perturbation Analysis: A Retrospective and Prospective Look2.1. Introduction; 2.2. Brief History of SPA; 2.3. Another Example; 2.4. Overview of a General SPA Framework; 2.5. Applications; 2.5.1. Queueing; 2.5.2. Inventory; 2.5.3. Finance; 2.5.4. Stochastic Activity Networks (SANs); 2.5.5. Others; 2.6. Random Retrospective and Prospective Concluding Remarks; Acknowledgements; References; Chapter 3. Perturbation Analysis and Variance Reduction in Monte Carlo Simulation; 3.1. Introduction; 3.2. Systematic and Generic Control Variate Selection 3.2.1. Control variate technique: a brief review3.2.2. Parametrized estimation problems; 3.2.3. Deterministic function approximation and generic CV selection; 3.3. Control Variates for Sensitivity Estimation; 3.3.1. A parameterized estimation formulation of sensitivity estimation; 3.3.2. Finite difference based controls; 3.3.3. Illustrating example; 3.4. Database Monte Carlo (DBMC) Implementation; 3.5. Conclusions; Acknowledgements; References; Chapter 4. Adjoints and Averaging; 4.1. Introduction; 4.2. Adjoints: Classical Setting; 4.3. Adjoints: Waiting Times; 4.4. Adjoints: Vector Recursions 4.5. Averaging4.6. Concluding Remarks; References; Chapter 5. Infinitesimal Perturbation Analysis and Optimization Algorithms; 5.1. Preliminary Remarks; 5.2. Motivation; 5.3. Single-server Queues; 5.3.1. Controlled single-server queue; 5.3.2. Infinitesimal perturbation analysis; 5.3.3. Optimization algorithm; 5.4. Convergence; 5.4.1. Stochastic approximation convergence theorem; 5.4.2. Updating after every busy period; 5.4.3. Updating after every service time; 5.4.4. Example; 5.5. Final Remarks; References; Chapter 6. Simulation-based Optimization of Failure-prone Continuous Flow Lines 6.1. Introduction6.2. Two-machine Continuous Flow Lines; 6.3. Gradient Estimation of a Two-machine Line; 6.4. Modeling Assembly/Disassembly Networks Subject to TDF Failures with Stochastic Fluid Event Graphs; 6.5. Evolution Equations and Sample Path Gradients; 6.6. Optimization of Stochastic Fluid Event Graphs; 6.7. Conclusion; References; Chapter 7. Perturbation Analysis, Dynamic Programming, and Beyond; 7.1. Introduction; 7.2. Perturbation Analysis of Queueing Systems Based on Perturbation Realization Factors; 7.2.1. Performance gradient; 7.2.2. Policy iteration 7.3. Performance Optimization of Markov Systems Based on Performance Potentials |
Record Nr. | UNINA-9910823201703321 |
Hackensack, NJ, : World Scientific, c2013 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|