Contents -- Foreword -- Preface -- Acknowledgments -- 1. Introduction to Stochastic Simulation Optimization -- 1.1 Introduction -- 1.2 Problem Definition -- 1.3 Classification -- 1.3.1. Design space is small -- 1.3.2. Design space is large -- 1.4 Summary -- 2. Computing Budget Allocation -- 2.1 Simulation Precision versus Computing Budget -- 2.2 Computing Budget Allocation for Comparison of Multiple Designs -- 2.3 Intuitive Explanations of Optimal Computing Budget Allocation -- 2.4 Computing Budget Allocation for Large Simulation Optimization -- 2.5 Roadmap -- 3. Selecting the Best from a Set of Alternative Designs -- 3.1 A Bayesian Framework for Simulation Output Modeling -- 3.2 Probability of Correct Selection -- 3.3 Maximizing the Probability of Correct Selection -- 3.3.1. Asymptotically optimal solution -- 3.3.2. OCBA simulation procedure -- 3.4 Minimizing the Total Simulation Cost -- 3.5 Non-Equal Simulation Costs -- 3.6 Minimizing Opportunity Cost -- 3.7 OCBA Derivation Based on Classical Model -- 4. Numerical Implementation and Experiments -- 4.1 Numerical Testing -- 4.1.1. OCBA algorithm -- 4.1.2. Different allocation procedures for comparison -- 4.1.3. Numerical experiments -- 4.2 Parameter Setting and Implementation of the OCBA Procedure -- 4.2.1. Initial number of simulation replications, n0 -- 4.2.2. One-time incremental computing budget, . -- 4.2.3. Rounding off Ni to integers -- 4.2.4. Variance -- 4.2.5. Finite computing budget and normality assumption -- 5. Selecting An Optimal Subset -- 5.1 Introduction and Problem Statement -- 5.2 Approximate Asymptotically Optimal Allocation Scheme -- 5.2.1. Determination of c value -- 5.2.2. Sequential allocation scheme -- 5.3 Numerical Experiments -- 5.3.1. Computing budget allocation procedures -- 5.3.2. Numerical results -- 6. Multi-objective Optimal Computing Budget Allocation.

6.1 Pareto Optimality -- 6.2 Multi-objective Optimal Computing Budget Allocation Problem -- 6.2.1. Performance index for measuring the dominance relationships and the quality of the selected Pareto set -- 6.2.1.1. A performance index to measure the degree of non-dominated for a design -- 6.2.1.2. Construction of the observed Pareto set -- 6.2.1.3. Evaluation of the observed Pareto set by two types of errors -- 6.2.2. Formulation for the multi-objective optimal computing budget allocation problem -- 6.3 Asymptotic Allocation Rule -- 6.4 A Sequential Allocation Procedure -- 6.5 Numerical Results -- 6.5.1. A 3-design case -- 6.5.2. Test problem with neutral spread designs -- 6.5.3. Test problem with steep spread designs -- 7. Large-Scale Simulation and Optimization -- 7.1 A General Framework of Integration of OCBA with Metaheuristics -- 7.2 Problems with Single Objective -- 7.2.1. Neighborhood random search (NRS) -- 7.2.2. Cross-entropy method (CE) -- 7.2.3. Population-based incremental learning (PBIL) -- 7.2.4. Nested partitions -- 7.3 Numerical Experiments -- 7.4 Multiple Objectives -- 7.4.1. Nested partitions -- 7.4.2. Evolutionary algorithm -- 7.5 Concluding Remarks -- 8. Generalized OCBA Framework and Other Related Methods -- 8.1 Optimal Computing Budget Allocation for Selecting the Best by Utilizing Regression Analysis (OCBA-OSD) -- 8.2 Optimal Computing Budget Allocation for Extended Cross-Entropy Method (OCBA-CE) -- 8.3 Optimal Computing Budget Allocation for Variance Reduction in Rare-event Simulation -- 8.4 Optimal Data Collection Budget Allocation (ODCBA) for Monte Carlo DEA -- 8.5 Other Related Works -- Appendix A: Fundamentals of Simulation -- A.1 What is Simulation? -- A.2 Steps in Developing A Simulation Model -- A.3 Concepts in Simulation Model Building -- A.4 Input Data Modeling -- A.5 Random Number and Variables Generation.

A.5.1. The Linear congruential generators (LCG) -- A.5.2. Random variate generation -- A.5.2.1. Inverse transform method -- A.5.2.2. Acceptance rejection method -- A.6 Output Analysis -- A.6.1. Output analysis for terminating simulation -- A.6.2. Output analysis for steady-state simulation -- A.7 Verification and Validation -- Appendix B: Basic Probability and Statistics -- B.1 Probability Distribution -- B.2 Some Important Statistical Laws -- B.3 Goodness of Fit Test -- Appendix C: Some Proofs in Chapter 6 -- C.1 Proof of Lemma 6.1 -- C.2 Proof of Lemma 6.2 -- C.3 Proof of Lemma 6.3 -- C.4 Proof of Lemma 6.5 (Asymptotic Allocation Rules) -- C.4.1. Determination of roles -- C.4.2. Allocation rules -- C.4.2.1. h ∈ SA, o ∈ SA -- C.4.2.2. d ∈ SB -- Appendix D: Some OCBA Source Codes -- References -- Index.