Ordinal optimization : soft optimization for hard problems / Yu-Chi Ho, Qian-Chuan Zhao, Qing-Shan Jia.

Includes bibliographical references (p. [305]-313) and index.

I. Introduction -- II. Ordinal Optimization Fundamentals -- 1. Two basic ideas of Ordinal Optimization (OO) -- 2. Definitions, terminologies, and concepts for OO -- 3. A simple demonstration of OO -- 4. The exponential convergence of order and goal softening -- 5. Universal alignment probabilities -- 6. Deterministic complex optimization problem and Kolmogorov equivalence -- 7. Example applications -- 8. Preview of remaining chapters -- III. Comparison of Selection Rules -- 1. Classification of selection rules -- 2. Quantify the efficiency of selection rules -- 3. Examples of search reduction -- 4. Some properties of good selection rules -- 5. Conclusion -- IV. Vector Ordinal Optimization -- 1. Definitions, terminologies, and concepts for VOO -- 2. Universal alignment probability -- 3. Exponential convergence w.r.t. order -- 4. Examples of search reduction -- V. Constrained Ordinal Optimization -- 1. Determination of selected set in COO -- 2. Example: Optimization with an imperfect feasibility model -- 3. Conclusion -- VI. Memory Limited Strategy Optimization -- 1. Motivation (the need to find good enough and simple strategies) -- 2. Good enough simple strategy search based on OO -- 3. Conclusion -- VII. Additional Extensions of the OO Methodology -- 1. Extremely large design space -- 2. Parallel implementation of OO -- 3. Effect of correlated observation noises -- 4. Optimal Computing Budget Allocation and Nested Partition -- 5. Performance order vs. performance value -- 6. Combination with other optimization algorithms -- VIII. Real World Application Examples -- 1. Scheduling problem for apparel manufacturing -- 2. The turbine blade manufacturing process optimization problem -- 3. Performance optimization for a remanufacturing system -- 4. Witsenhausen problem -- App. A. Fundamentals of Simulation and Performance Evaluation

App. B. Introduction to Stochastic Processes and Generalized Semi-Markov Processes as Models for Discrete Event Dynamic Systems and Simulations -- App. C. Universal Alignment Tables for the Selection Rules in Chapter III -- App. D. Exercises.