Bayesian Heuristic Approach to Discrete and Global Optimization

Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This text demonstrates that this framework is also well suited for the exploitation of heuristic methods in the solution of such problems, especially large scale ones for which exact optimization approaches can be prohibitively costly. It covers all aspects ranging from the formal presentation of the Bayesian Approach, to its extension to the Bayesian Heuristic Strategy, and its utilization within the informal, interactive Dynamic Visualization strategy. The developed framework is applied in forecasting, in neural network optimization, and in a large number of discrete and continuous optimization problems. Specific application areas which are discussed include scheduling and visualization problems in chemical engineering, manufacturing process control, and epidemiology. Computational results and comparisons with a broad range of test examples are presented. The software required for implementation of the Bayesian Heuristic Approach is included. The book should be of interest to both researchers in operations research, systems engineering, and optimization methods, as well as applications specialists concerned with the solution of large scale discrete and/or nonconvex optimization problems in a broad range of engineering and technological fields. It may be used as supplementary material for graduate level courses.