The student is introduced to development of decision support systems (DSS) for application to complex engineering management and design problems under conflicting objectives and uncertainty. A number of techniques are introduced for aiding in the analysis of a wide range of complex multiobjective engineering problems. Several stochastic optimization methods are presented for including risk and reliability in engineering design. Basic concepts of expert systems (ES) are discussed to show an essential synergy between DSS and ES for development of decision support structures that allow inclusion of human domain knowledge, heuristics and fuzzy logic. Heuristic methods such as genetic algorithms and particle swarm optimization are offered as a means of solving complex engineering design and management problems that defy traditional techniques of mathematical programming and operations research. Machine learning methods using artificial neural networks are introduced for solving complex dynamic scheduling and control problems in engineering. Each student is required to present a final class project involving application of the tools and concepts presented in the class to a real-world engineering decision problem. Course taught at Colorado State University.
Labadie, John, "Engineering Decision Support and Expert Systems - Colorado State University" (2015). All ECSTATIC Materials. Paper 42.