Abstract
Mass customization necessitates increased product variety at the customers’ end but comparatively lesser part variety at the manufacturer’s end. Product platform concepts have been successful to achieve this goal at large. One of the popular methods for product platform formation is to scale one or more design variables called the scaling variables. Effective optimization methods are needed to identify proper values of the scaling variables. This paper presents a graph-based optimization method called the scalable platforms using ant colony optimization (SPACO) method for identifying appropriate values of the scaling variables. In the graph-based representation, each node signifies a sub-range of values for a design variable. This application includes the concept of multiplicity in node selection because there are multiple nodes corresponding to the discretized values of a given design variable. In the SPACO method, the overall decision is a result of the cumulative decisions, made by simple computing agents called the ants, over a number of iterations. The space search technique initially starts as a random search technique over the entire search space and progressively turns into an autocatalytic (positive feedback) probabilistic search technique as the solution matures. We use a family of universal electric motors, widely cited in the literature, to test the effectiveness of the proposed method. Our simulation results, when compared to the results reported in the literature, prove that SPACO method is a viable optimization method for determining the values of design variables for scalable platforms.
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References
Bullnheimer, B., Hartl, R. F., & Strauss, C. (1997). A new rank-based version of the ant system: a computational study. Technical report POM-03/97, Institute of Management Science, University of Vienna. Central European Journal for Operation Research and Economics.
Bullnheimer, B., Hart, R. F., & Strauss, C. (1998). In I. H. Osama, S.S. Martello, & Roucairol (Eds.), Applying the ant systems to the vehicle routing problem. Advances and Trends in the Local Search Paradigms for Optimization, 109–120.
Colorni A., Dorigo M., Maniezzo V., Trubian M. (1994). Ant system for job-shop scheduling. Belgian Journal of Operation Research, Statistics and Computer Science 34, 39–53
Dorigo M., Gambardella L.M. (1997). Ant colonies for the traveling salesman problem. Bio Systems 43, 73–81
Dorigo, M., Maniezzo, V., & Colorni, A. (1991). Positive feedback as a search strategy. Technical Report, Dipartimento di Elettronica, Politechnico di Milano, IT, pp. 91–106.
Dorigo M., Maniezzo V., Colorni A. (1996). The ant systems: Optimization by a colony of cooperative agents. IEEE Transactions on Man, Machine and Cybernetics-Part B 26(1): 29–41
Dorigo M., Stutzle T. (2004). Ant colony optimization. The MIT Press, Cambridge, MA
D’Souza, B., & Simpson, T. W. (2002). A genetic algorithm based method for product family design optimization. ASME design engineering technical conferences—Design automation conference (DAC), Montreal, Quebec, Canada, September 29–October 2, Paper No. DETC2002/DAC-34106, pp. 1–9.
Feitzinger E., Lee H.L. (1997). Mass customization at Hewlett-Packard: The power of postponement. Harvard Business Review 75(1): 116–121
Fujita, K., Akagi, S., Yoneda, T., & Ishikawa, M. (1998). Simultaneous optimization of product family sharing system structure and configuration. Proceedings ASME design engineering technical conference, Paper No. DETC98/DFM-5722.
Gambardella, L. M., & Dorigo, M. (1995). Ant-Q: A reinforcement learning approach to the traveling salesman problem. Proceedings of the twelfth international conference on machine learning, ML-95, pp. 252–260.
Gambardella L.M., Taillard E.D., Dorigo M. (1999). Ant colonies for the qap. Journal of the Operation Research Society 50, 167–176
Krishnan V., Gupta S. (2001). Appropriateness and impact of platform based product development. Management Science 47(1): 52–68
Kumar R., Tiwari M.K., Shankar R. (2003). Scheduling of flexible manufacturing systems: An ant colony optimization approach. Journal of Engineering Manufacturing Part B 217(10): 1443–1453
Maniezzo, V., Colorni, A., & Dorigo, M. (1994). The ant systems applied to the quadratic assignment problem. Technical report IRIDIA, University Libre de Bruxelles, Belgium, pp. 28–94.
Messac A., Martinez M.P., Simpson T.W. (2002a). Effective product family design using physical programming. Engineering Optimization 34(3): 245–261
Messac A., Martinez M.P., Simpson T.W. (2002b). Introduction of a product family penalty function using physical programming. Transactions of the ASME 124, 164–172
Messac A., Melachrinoudis E., Sukam C.P. (2001). Mathematical and pragmatic perspectives of physical programming. AIAA Journal 39(5): 885–893
Naughton, K., Thornton, E., Kerwin, K., & Dawley, H. (1997). Can Honda build a world car? Business Week, 100(7) September 8.
Nayak R.U., Chen W., Simpson T.W. (2002). A variation-based method for product family design. Engineering Optimization 34(1): 65–81
Nelson S.A., Parkinson M.B., Papalambros P.Y. (2001). Multicriteria optimization in product platform design. Journal of Mechanical Design 123, 199–204
Robertson D., Ulrich K. (1998). Planning product platforms. Sloan Management Review 39(4): 19–31
Rothwell, R., & Gardiner, P. (1990). Robustness and product design families. In Design management: A handbook of issues and methods (pp. 279–292). Cambridge, MA: Basil Blackwell Inc.
Sabbagh K. (1996). Twenty-first century jet: The making and marketing of the Boeing 777. Scribner, New York
Sanderson S.W., Uzumeri M. (1997). The innovation imperative: Strategies for managing product models and families. Irwin, Chicago, IL
Siddique Z. (2000). Common platform development: Designing for product variety. Ph.D. Dissertation, Georgia Institute of Technology, USA
Siddique Z., Rosen D.W. (2001). On combinatorial design spaces for the configuration design of product families. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15(2): 91–108
Simpson T.W. (1998). A concept exploration method for product family design. Georgia Institute of Technology, USA
Simpson T.W. (2004). Product platform design and customization: Status and promise. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18(1): 1–39
Simpson, T. W., & D’Souza, B. (2002). Assessing variable levels of platform commonality within a product family using a multi-objective genetic algorithm. 9th AIAA/ISSMO symposium on multidisciplinary analysis and optimization, Atlanta, GA, AIAA 2002-5427.
Simpson T.W., Maier J.R.A., Mistree F. (2001). Product platform design: Method and application. Research in Engineering Design 13(1): 2–22
Stutzle, T., & Hoos, H. (1997). The MAX–MIN ant systems and local search for the traveling salesman problem. In Proceedings of IEEE-ICEC-EPS, IEEE international conference on the evolutionary computation and evolutionary programming conference, pp. 309–314.
Stutzle, T., & Hoos, H. (1998). MAX–MIN ant systems and local search for the combinatorial optimization problems. Advanced and Trends in Local Search Paradigms for the Optimization, 37–154.
Whitney D.E. (1993). Nippondenso Co. Ltd: A case study of strategic product design. Research in Engineering Design 5(1): 1–20
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Kumar, R., Allada, V. Scalable platforms using ant colony optimization. J Intell Manuf 18, 127–142 (2007). https://doi.org/10.1007/s10845-007-0009-9
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DOI: https://doi.org/10.1007/s10845-007-0009-9