Mining Pareto-optimal modules for delayed product differentiation |
| |
Authors: | Zhe Song Andrew Kusiak |
| |
Institution: | Intelligent Systems Laboratory, MIE, 3131 Seamans Center, The University of Iowa, Iowa City, IA 52242-1527, USA |
| |
Abstract: | This paper presents a framework for finding optimal modules in a delayed product differentiation scenario. Historical product sales data is utilized to estimate demand probability and customer preferences. Then this information is used by a multiple-objective optimization model to form modules. An evolutionary computation approach is applied to solve the optimization model and find the Pareto-optimal solutions. An industrial case study illustrates the ideas presented in the paper. The mean number of assembly operations and expected pre-assembly costs are the two competing objectives that are optimized in the case study. The mean number of assembly operations can be significantly reduced while incurring relatively small increases in the expected pre-assembly cost. |
| |
Keywords: | Data mining Evolutionary computations Mass customization Modularity Delayed product differentiation Multi-objective optimization |
本文献已被 ScienceDirect 等数据库收录! |