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Adaptive attribute selection for configurator design via Shapley value

Published online by Cambridge University Press:  20 April 2011

Yue Wang  and
Mitchell M. Tseng
Yue Wang
Affiliation:
Advanced Manufacturing Institute, Hong Kong University of Science and Technology, Hong Kong
Mitchell M. Tseng
Affiliation:
Advanced Manufacturing Institute, Hong Kong University of Science and Technology, Hong Kong
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Abstract

Configurators have been generally accepted as important tools to elicit customers' needs and find the matches between customers' requirements and company's offerings. With product configurators, product design is reduced to a series of selections of attribute values. However, it has been acknowledged that customers are not patient enough to configure a long list of attributes. Therefore, making every round of configuring process productive and hence reducing the number of inputs from customers are of substantial interest to academic and industry alike. In this paper, we present an efficient product configuration approach by incorporating Shapley value, which is a concept used in game theory, to estimate the usefulness of each attribute in the configurator design. This new method iteratively selects the most relevant attribute that can contribute most in terms of information content from the remaining pool of unspecified attributes. As a result from product providers' perspective, each round of configuration can best narrow down the choices with given amount of time. The selection of the next round query is based on the customer's decision on the previous rounds. The interactive process thus runs in an adaptive manner that different customers will have different query sequences. The probability ranking principle is also exploited to give product recommendation to truncate the configuration process so that customers will not be burdened with trivial selection of attributes. Analytical results and numerical examples are also used to exemplify and demonstrate the viability of the method.

Keywords

Attribute SelectionConfiguratorProbability Ranking PrincipleShapley Value

Information

Type
Special Issue Articles
Information
AI EDAM , Volume 25 , Issue 2: Configuration , May 2011 , pp. 185 - 195
Copyright
Copyright © Cambridge University Press 2011

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