Title

Mining Pareto-optimal modules for delayed product differentiation

Document Type

Article

Peer Reviewed

1

Publication Date

1-1-2010

Journal/Book/Conference Title

European Journal of Operational Research

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. 2009 Elsevier B.V. All rights reserved.

Keywords

Sustainability

Published Article/Book Citation

European Journal of Operational Research, 201:1 (2010) pp.123-128.

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URL

https://ir.uiowa.edu/mie_pubs/125