| Preface | p. xiii |
| Acknowledgements | p. xv |
| Introduction | p. 1 |
| Ordinal Optimization Fundamentals | p. 7 |
| Two basic ideas of Ordinal Optimization (OO) | p. 7 |
| Definitions, terminologies, and concepts for OO | p. 9 |
| A simple demonstration of OO | p. 13 |
| The exponential convergence of order and goal softening | p. 15 |
| Large deviation theory | p. 16 |
| Exponential convergence w.r.t. order | p. 21 |
| Proof of goal softening | p. 26 |
| Blind pick | p. 26 |
| Horse race | p. 28 |
| Universal alignment probabilities | p. 37 |
| Blind pick selection rule | p. 38 |
| Horse race selection rule | p. 39 |
| Deterministic complex optimization problem and Kolmogorov equivalence | p. 48 |
| Example applications | p. 51 |
| Stochastic simulation models | p. 51 |
| Deterministic complex models | p. 53 |
| Preview of remaining chapters | p. 54 |
| Comparison of Selection Rules | p. 57 |
| Classification of selection rules | p. 60 |
| Quantify the efficiency of selection rules | p. 69 |
| Parameter settings in experiments for regression functions | p. 73 |
| Comparison of selection rules | p. 77 |
| Examples of search reduction | p. 80 |
| Example: Picking with an approximate model | p. 80 |
| Example: A buffer resource allocation problem | p. 84 |
| Some properties of good selection rules | p. 88 |
| Conclusion | p. 90 |
| Vector Ordinal Optimization | p. 93 |
| Definitions, terminologies, and concepts for VOO | p. 94 |
| Universal alignment probability | p. 99 |
| Exponential convergence w.r.t. order | p. 104 |
| Examples of search reduction | p. 106 |
| Example: When the observation noise contains normal distribution | p. 106 |
| Example: The buffer allocation problem | p. 108 |
| Constrained Ordinal Optimization | p. 113 |
| Determination of selected set in COO | p. 115 |
| Blind pick with an imperfect feasibility model | p. 115 |
| Impact of the quality of the feasibility model on BPFM | p. 119 |
| Example: Optimization with an imperfect feasibility model | p. 122 |
| Conclusion | p. 124 |
| Memory Limited Strategy Optimization | p. 125 |
| Motivation (the need to find good enough and simple strategies) | p. 126 |
| Good enough simple strategy search based on OO | p. 128 |
| Building crude model | p. 128 |
| Random sampling in the design space of simple strategies | p. 133 |
| Conclusion | p. 135 |
| Additional Extensions of the OO Methodology | p. 137 |
| Extremely large design space | p. 138 |
| Parallel implementation of OO | p. 143 |
| The concept of the standard clock | p. 144 |
| Extension to non-Markov cases using second order approximations | p. 147 |
| Second order approximation | p. 148 |
| Numerical testing | p. 152 |
| Effect of correlated observation noises | p. 154 |
| Optimal Computing Budget Allocation and Nested Partition | p. 159 |
| OCBA | p. 160 |
| NP | p. 164 |
| Performance order vs. performance value | p. 168 |
| Combination with other optimization algorithms | p. 175 |
| Using other algorithms as selection rules in OO | p. 177 |
| GA+OO | p. 177 |
| SA+OO | p. 183 |
| Simulation-based parameter optimization for algorithms | p. 186 |
| Conclusion | p. 188 |
| Real World Application Examples | p. 189 |
| Scheduling problem for apparel manufacturing | p. 190 |
| Motivation | p. 191 |
| Problem formulation | p. 192 |
| Demand models | p. 193 |
| Production facilities | p. 195 |
| Inventory dynamic | p. 196 |
| Summary | p. 197 |
| Application of ordinal optimization | p. 198 |
| Random sampling of designs | p. 199 |
| Crude model | p. 200 |
| Experimental results | p. 202 |
| Experiment 1: 100 SKUs | p. 202 |
| Experiment 2: 100 SKUs with consideration on satisfaction rate | p. 204 |
| Conclusion | p. 206 |
| The turbine blade manufacturing process optimization problem | p. 207 |
| Problem formulation | p. 208 |
| Application of OO | p. 213 |
| Conclusion | p. 219 |
| Performance optimization for a remanufacturing system | p. 220 |
| Problem formulation of constrained optimization | p. 220 |
| Application of COO | p. 224 |
| Feasibility model for the constraint | p. 224 |
| Crude model for the performance | p. 224 |
| Numerical results | p. 225 |
| Application of VOO | p. 227 |
| Conclusion | p. 232 |
| Witsenhausen problem | p. 232 |
| Application of OO to find a good enough control law | p. 234 |
| Crude model | p. 235 |
| Selection of promising subsets | p. 237 |
| Application of OO for simple and good enough control laws | p. 245 |
| Conclusion | p. 251 |
| Fundamentals of Simulation and Performance Evaluation | p. 253 |
| Introduction to simulation | p. 253 |
| Random numbers and variables generation | p. 255 |
| The linear congruential method | p. 255 |
| The method of inverse transform | p. 257 |
| The method of rejection | p. 258 |
| Sampling, the central limit theorem, and confidence intervals | p. 260 |
| Nonparametric analysis and order statistics | p. 262 |
| Additional problems of simulating DEDS | p. 262 |
| The alias method of choosing event types | p. 264 |
| Introduction to Stochastic Processes and Generalized Semi-Markov Processes as Models for Discrete Event Dynamic Systems and Simulations | p. 267 |
| Elements of stochastic sequences and processes | p. 267 |
| Modeling of discrete event simulation using stochastic sequences | p. 271 |
| Universal Alignment Tables for the Selection Rules in Chapter III | p. 279 |
| Exercises | p. 291 |
| True/False questions | p. 291 |
| Multiple-choice questions | p. 293 |
| General questions | p. 297 |
| References | p. 305 |
| Index | p. 315 |
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