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With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive.
Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners with a comprehensive coverage of OCBA approach for stochastic simulation optimization. Starting with an intuitive explanation of computing budget allocation and a discussion of its impact on optimization performance, a series of OCBA approaches developed for various problems are then presented, from the selection of the best design to optimization with multiple objectives. Finally, this book discusses the potential extension of OCBA notion to different applications such as data envelopment analysis, experiments of design and rare-event simulation.
Sample Chapter(s)
Foreword (28 KB)
Chapter 1: Introduction to Stochastic Simulation Optimization (182 KB)
Chapter 2: Computing Budget Allocation (125 KB)
Chapter 7: Large-Scale Simulation and Optimization (789 KB)
Contents:
- Introduction to Stochastic Simulation Optimization
- Computing Budget Allocation
- Selecting the Best from a Set of Alternative Designs
- Numerical Implementation and Experiments
- Selecting an Optimal Subset
- Multi-objective Optimal Computing Budget Allocation
- Large-Scale Simulation and Optimization
- Generalized OCBA Framework and Other Related Methods
- Appendices:
- Fundamentals of Simulation
- Basic Probability and Statistics
- Some Proofs in Chapter 6
- Some OCBA Source Codes
Readership: Academics and professionals in the fields of stochastic analysis, systems and industrial engineering, probability and statistics, and computer science.
