Ensembles of Adaptive One-Factor at-a-Time experiments : methods, evaluation, and theory

This thesis recommends an experimentation methodology which can be used to improve systems, processes and products. The proposed technique borrows insights from statistical prediction practices referred to as Ensemble Methods, to extend Adaptive One-Factor-at-a-Time (aOFAT) experimentation. The algorithm is developed for an input space where each variable assumes two or more discrete levels. Ensemble methods are common data mining procedures in which a set of similar predictors is created and the overall prediction is achieved through the aggregation of these units. In a methodologically similar way this study proposes to plan and execute multiple aOFAT experiments on the same system with minor differences in experimental setup, such as starting points, or order of variable changes. Experimental conclusions are arrived at by aggregating the multiple, individual aOFATs. Different strategies for selecting starting points, order of variable changes, and aggregation techniques are explored. The proposed algorithm is compared to the performance of a traditional form of experimentation, namely a single orthogonal array (full and fractional factorial designs), which is equally resource intensive. Comparisons between the two experimental algorithms are conducted using a hierarchical probability meta-model (HPM) and an illustrative case study. The case is a wet clutch system with the goal of minimizing drag torque. Across both studies (HPM and case study), it is found that the proposed procedure is superior in performance to the traditional method.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Sudarsanam, Nandan, 1981-

Advisor dc:contributor.advisor

• Daniel D. Frey.

• Engineering Systems Division.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/53211

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/53211