Global Product Family Design: Simultaneous Optimal Design of Module Commonalization and Supply Chain Configuration
Global product family design is the problem in which product variants and supply chain configuration are simultaneously designed. It has become a significant concern of manufacturing industries under globalization. In this chapter, simultaneous design of module commonalization and supply chain configuration is formulated as a multi-objective mixed-integer programming problem under the criteria on quality, cost, and delivery. Then, an optimization algorithm for obtaining Pareto optimal solutions is configured by using a neighborhood cultivation genetic algorithm and simplex method, and a clustering technique of such Pareto solutions is introduced with a principal component analysis method for investigating the optimality and compromise in global product family design. Finally, some numerical case studies are demonstrated.
KeywordsEurope Transportation Expense
The author acknowledges that computer programming and computation of optimization examples were done by Ken Nasu, who was formerly a graduate student of Osaka University, and Yuma Ito, who is currently a graduate student of Osaka University.
- Fujita K, Sakaguchi H, Akagi S (1999) Product variety deployment and its optimization under modular architecture and module commonalization. In: Proceedings of the 1999 ASME design engineering technical conferences, Paper No. DETC99/DFM-8923Google Scholar
- Fujita K, Muraoka M, Mistunaka A, Nomaguchi Y (2011) Preliminary study on design concept exploration of truss structures by multi-objective optimization and self-organizing map. In: Proceedings of 9th world congress on structural and multidisciplinary optimization (WCSMO-9), Paper Code 361_2Google Scholar
- Fujita K, Nasu K, Ito Y, Nomaguchi Y (2012b) Global product family design: multi-objective optimization and design concept exploration. In: Proceedings of the 2012 ASME design engineering technical conferences and computers and information in engineering conference, Paper No. DETC2012-70858Google Scholar
- Obayashi S, Sasaki D (2003) Visualization and data mining of Pareto solutions using self-organizing map. In: Proceedings of 2nd international conference on evolutionary multi-criterion optimization, pp 796–809Google Scholar
- Oyama A, Verburg P, Nonomura T, Fujii K (2009) Flow data mining of Pareto-optimal airfoils using proper orthogonal decomposition. In: Proceedings of annual conference of the Japan Society for computational engineering and science (JSCES), vol 14, pp 123–126 (in Japanese)Google Scholar
- Pine BJ (1993) Mass customization: the New Frontier in business competition. Harvard Business School Press, Boston, MAGoogle Scholar
- Simchi-Levi D, Kaminsky P, Simchi-Levi E (1999) Designing and managing the supply chain: concepts, strategies, and cases. McGraw-Hill/Irwin, New York, NYGoogle Scholar
- Simpson TW, Siddique Z, Jiao J (2005) Product platform and product family design: method and applications. Springer, New York, NYGoogle Scholar
- Takano N (2010) New MARCH that will be mass-produced at an emerging country, Nikkei Monozukuri, Oct 2010 Issue, Nikkei Business Publications (in Japanese)Google Scholar
- Watanabe S, Hiroyasu T, Miki M (2002) Neighborhood cultivation genetic algorithm for multi-objective optimization problems. In: Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning (SEAL-2002), pp 198–202Google Scholar