A Discrete Choice Modeling Approach to Modular Systems Design

The paper proposes an approach for design of modular systems based on original technique for modeling and formulation of combinatorial optimization problems. The proposed approach is described on the example of personal computer configuration design. It takes into account the existing compatibility restrictions between the modules and can be extended and modified to reflect different functional and users- requirements. The developed design modeling technique is used to formulate single objective nonlinear mixedinteger optimization tasks. The practical applicability of the developed approach is numerically tested on the basis of real modules data. Solutions of the formulated optimization tasks define the optimal configuration of the system that satisfies all compatibility restrictions and user requirements.




References:
[1] P. T. Kidd. Agile Manufacturing: Forging New Frontiers, Addison
Wesley, New York, 1994.
[2] J. K. Gershenson, G. J. Prasad. Modularity in product design for
manufacturability. Int. J. of Agile Manufacturing, vol. 1, no 1, pp. 1-11,
1997.
[3] Tzu-Liang (Bill) Tseng, Chun-Che Huang. Design support systems: A
case study of modular design of the set-top box from design knowledge
externalization perspective, Decision Support Systems, vol. 44, no 4, pp.
909-924, 2008.
[4] J. T. Dorsey, T. J. Collins, W. R. Doggett, R. V. Moe. Framework for
defining and assessing benefits of a modular assembly design approach
for exploration systems. in Proc. Space Technology and Applications
International Forum - STAIF 2006, vol. 813, pp. 969-981.
[5] A.K. Kamrani, E.A. Nasr. Collaborative Engineering. Springer, 2008,
ch. 10.
[6] A. S├│bester, A. I.J. Forrester, D. J.J. Toal, E. Tresidder, S. Tucker,
Engineering design applications of surrogate-assisted optimization
techniques, Optimization and Engineering, DOI 10.1007/s11081-012-
9199-x, 2012.
[7] K. Fujita. Product variety optimization under modular architecture.
Computer-Aided Design, vol. 34, no. 12, pp. 953-965, 2002.
[8] M. S. Levin. Combinatorial Optimization in System Configuration
Design, Automation and Remote Control, vol. 70, no. 3, pp. 519-561,
2009.
[9] K. Fujita, H. Sakaguchi, S. Akagi. Product variety deployment and its
optimization under modular architecture and module communalization.
Proc. of the ASME Design Engineering Technical Conferences, 1999,
Las Vegas, Nevada, DETC99/DFM-8923.
[10] Re-Designing The Computer: The Birth of the Modular Computer". Xi3
Corporation. http://xi3.com/white_paper.pdf.
[11] V. Tam, K. T. Ma. Using heuristic-based optimizers to handle the
personal computer configuration problems, in Proc. 12th IEEE Int. Conf.
on Tools with Artificial Intelligence, 2000, pp. 108-111.
[12] V. Tam, K. T. Ma. Optimizing personal computer configurations with
heuristic-based search methods, Artificial Intelligence Review, vol. 17,
no 2, pp. 129-140, 2002.
[13] L. Jae-Kyu, S. Sung-Hoon, K. Suhn-Baum. Configuration of personal
computer by constraint and rule satisfaction problem approach. in Proc.
First Asian Paciific DSI Conference, Hongkong, 1996, pp. 1-22.
[14] T. Soininen, I. Niemela, J. Tiihonen, R. Sulonen. Unified configuration
knowledge representation using weight constraint rules, in Proc. ECAI-
2000 Workshop on Configuration, pp. 79-84, Berlin, 2000.
[15] J. McDermott. R1: A Rule-based Configurer of Computer Systems,
Artificial Intelligence, vol. 19, no. 1, pp. 39-88, 1982.
[16] V. B. Kreng, Tseng-Pin Lee., Modular product design with grouping
genetic algorithm - a case study. Computers & Industrial Engineering,
vol. 46, no. 3, pp. 443-460, 2004.
[17] Lindo Systems, http://www.lindo.com.