Mathematical Problems in Engineering
Volume 2011 (2011), Article ID 162580, 14 pages
Research Article

The Application of Memetic Algorithms for Forearm Crutch Design: A Case Study

1Industrial Engineering Program, School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
2Center for Rapid Product Development, Air Force Institute of Technology, Wright-Patterson Air Force Base, Dayton, OH 45433-7765, USA
3Department of Systems and Engineering Management, Air Force Institute of Technology, Wright-Patterson Air Force Base, Dayton, OH 45433-7765, USA

Received 31 July 2010; Revised 16 December 2010; Accepted 5 January 2011

Academic Editor: Reza Jazar

Copyright © 2011 Teresa Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Product design has normally been performed by teams, each with expertise in a specific discipline such as material, structural, and electrical systems. Traditionally, each team would use its member's experience and knowledge to develop the design sequentially. Collaborative design decisions explore the use of optimization methods to solve the design problem incorporating a number of disciplines simultaneously. It is known that such optimized product design is superior to the design found by optimizing each discipline sequentially due to the fact that it enables the exploitation of the interactions between the disciplines. In this paper, a bi-level decentralized framework based on Memetic Algorithm (MA) is proposed for collaborative design decision making using forearm crutch as the case. Two major decisions are considered: the weight and the strength. We introduce two design agents for each of the decisions. At the system level, one additional agent termed facilitator agent is created. Its main function is to locate the optimal solution for the system objective function which is derived from the Pareto concept. Thus to Pareto optimum for both weight and strength is obtained. It is demonstrated that the proposed model can converge to Pareto solutions.