Masters Theses

Keywords and Phrases

Product portfolios; Repository based search methods


"A strong need has emerged in the design industry, for an improved method of coding artifact information along with providing an effective and robust search method for matching specific functional information with customer needs. The new code is a representation of a subset of the information currently established in the UMR design repository ( and is an evolution of past Group Technology Coding Schemes. The code is broken down into five elements: Component, Material, Manufacturing, Function, and Flow. The new GT code provides for a more complex coding scheme due to improved technology and allows for several arrangements based on needs and industry. The methodology and coding scheme described represents the initial conceptual design for a search algorithm.

The genetic algorithm (GA) presented will provide a robust method of searching the design domain. The GA will be an improved method over an enumerative search due to efficiency and probability based search methods. The GA will also have the ability to give an exceptional solution based on a user input objective along with suggested redesigns. An methodology of the GA is discussed along with scenarios and outlining the individual stages: Initial Population Selection, Evaluation, Reproduction, Recombination, and Mutation. This work establishes the theory to support a new genetic algorithm design approach for product family design based on an existing repository of design knowledge"--Abstract, page iv.


Stone, Robert B.

Committee Member(s)

McAdams, Daniel A.
Ramsay, Christopher W.


Mechanical and Aerospace Engineering

Degree Name

M.S. in Manufacturing Engineering


This material is based upon work supported by the National Science Foundation under Grant No. IIS-032541.


University of Missouri--Rolla

Publication Date

Fall 2005

Journal article titles appearing in thesis/dissertation

  • A group technology based representation for product portfolios
  • A framework for improving product portfolio design with genetic algorithms


xi, 75 pages

Note about bibliography

Includes bibliographical references.


© 2005 Robert L. Jordan, Jr., All rights reserved.

Document Type

Thesis - Restricted Access

File Type




Subject Headings

Information retrieval -- Computer-aided design
Group technology
Genetic algorithms

Thesis Number

T 8898

Print OCLC #


Link to Catalog Record

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