This research concerns the integration of Adaptive Search (AS) technique such as the Genetic Algorithms (GA) with knowledge based software to develop a research prototype of an Adaptive Search Manager (ASM). The developed approach allows to utilise both quantitative and qualitative information in engineering design decision making. A Fuzzy Expert System manipulates AS software within the design environment concerning the preliminary design of gas turbine blade cooling systems. Steady state cooling hole geometry models have been developed for the project in collaboration with Rolls Royce plc. The research prototype of ASM uses a hybrid of Adaptive Restricted Tournament Selection (ARTS) and Knowledge Based Hill Climbing (KBHC) to identify multiple "good" design solutions as potential design options. ARTS is a GA technique that is particularly suitable for real world problems having multiple sub-optima. KBHC uses information gathered during the ARTS search as well as information from the designer to perform a deterministic hill climbing. Finally, a local stochastic hill climbing fine tunes the "good" designs. Design solution sensitivity, design variable sensitivities and constraint sensitivities are calculated following Taguchi's methodology, which extracts sensitivity information with a very small number of model evaluations. Each potential design option is then qualitatively evaluated separately for manufacturability, choice of materials and some designer's special preferences using the knowledge of domain experts. In order to guarantee that the qualitative evaluation module can evaluate any design solution from the entire design space with a reasonably small number of rules, a novel knowledge representation technique is developed. The knowledge is first separated in three categories: inter-variable knowledge, intra-variable knowledge and heuristics. Inter-variable knowledge and intra-variable knowledge are then integrated using a concept of compromise. Information about the "good" design solutions is presented to the designer through a designer's interface for decision support.

Assembly Sequence Planning (ASP) is part of Assembly Planning. The assembly sequence is the most important part of an assembly plan. Assembly has an important share in both lead time and cost of a product. Therefore, its optimisation is necessary to ensure the competitivity of manufactured goods. The aim of this thesis is the optimisation of assembly sequences for mechanical products, for real/realistic problems and constraints. This thesis represents an integrated approach in assembly sequence planning and optimisation. It tackles real problems by building the generality in the models. / Thesis (PhD)--University of South Australia, 2003.

Thesis (MEng)--University of South Australia, 1996

Thesis (Ph. D.)--University of Hong Kong, 2008. / Also available in print.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 91-96). Also available via the Internet.

Thesis (Ph. D., Bioinformatics and Computational Biology)--University of Idaho, August 2008. / Major professor: James A. Foster. Includes bibliographical references (leaves 90-94). Also available online (PDF file) by subscription or by purchasing the individual file.

Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references. Also available in print.

Thesis (M.S.)--Brigham Young University. Dept. of Civil and Environmental Engineering, 2005. / Includes bibliographical references (p. 87-89).

Thesis (Ph. D.)--West Virginia University, 1999. / Title from document title page. Document formatted into pages; contains vi, 91 p. : ill. Vita. Includes abstract. Includes bibliographical references (p. 41-42).

Thesis (Ph.D.)--Tennessee Technological University, 2008. / Title from title page screen (viewed on Sept. 28, 2009). Bibliography: leaves 132-138.