Cooperative Coevolutionary Optimization Method for Product Family Design

被引:0
作者
Schulze, L. [1 ]
Li, L. [1 ]
机构
[1] Leibniz Univ Hannover, Dept Planning & Controlling Warehouse & Transport, D-30167 Hannover, Germany
来源
2009 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-4 | 2009年
关键词
Mass customization; Product family; Multi-level commonality; Cooperative coevolutionary algorithm; NSGA-II; PLATFORM DESIGN;
D O I
10.1109/IEEM.2009.5373357
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Under the mass customization paradigm, an increasing number of companies design a family of product variants simultaneously, instead of designing one product at a time. Product family design is a way to share resources through commonality among the products so as to shortening product lead-times while introduce adequate product variety in the competitive market in a cost-effective way. The key to using this approach successfully is to achieve the right trade-offs between commonality and performance product family. A product family design model is proposed as an optimization problem with two objectives. One represents the average performance of the product variants and the other is the measure of commonality of the product family in the presence of multiple levels. Optimal decisions include which design variables should be common among which product variants, and the values of each variable of each product variant in the family. A multiobjective cooperative coevolutionary algorithm is developed for simultaneous design of a family of product variants. Computational experiments are conducted using the design of a family of welded beams to demonstrate the effectiveness of the product family design method proposed in this paper.
引用
收藏
页码:291 / 295
页数:5
相关论文
共 14 条
[1]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197
[2]  
Hernandez G., 2001, Platform Design for Customizable Products as a Problem of Access in Geometric Space
[3]  
HERNANDEZ G, 2002, P ASME DES ENG TECHN
[4]   Genetic algorithm-based optimisation method for product family design with multi-level commonality [J].
Huang, George Q. ;
Li, Li ;
Schulze, Lothar .
JOURNAL OF ENGINEERING DESIGN, 2008, 19 (05) :401-416
[5]  
Khajavirad A., 2007, P 3 AIAA MULT DES OP
[6]  
MESSAC A, 2002, ASME, V124, P164, DOI DOI 10.1115/1.1467602
[7]   Forming Neural Networks Through Efficient and Adaptive Coevolution [J].
Moriarty, David E. ;
Miikkulainen, Risto .
EVOLUTIONARY COMPUTATION, 1997, 5 (04) :373-399
[8]   A variation-based method for product family design [J].
Nayak, RU ;
Chen, W ;
Simpson, TW .
ENGINEERING OPTIMIZATION, 2002, 34 (01) :65-81
[9]   Multicriteria optimization in product platform design [J].
Nelson, SA ;
Parkinson, MB ;
Papalambros, PY .
JOURNAL OF MECHANICAL DESIGN, 2001, 123 (02) :199-204
[10]  
Ravindran A., 2006, Engineering optimization-Methods and aplications