configurable products
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2018 ◽  
Vol 69 (11) ◽  
pp. 1834-1853 ◽  
Author(s):  
Edward Lawrence Umpfenbach ◽  
Evrim Dalkiran ◽  
Ratna Babu Chinnam ◽  
Alper Ekrem Murat

2014 ◽  
pp. 101-107
Author(s):  
Alexander Felfernig ◽  
Gerhard Friedrich ◽  
Dietmar Jannach ◽  
Christian Russ ◽  
Markus Zanker

Knowledge­based product configurators support their users in tailoring configurable products according to their specific demands and these systems have been successfully applied in many industrial sectors over the last decades. However, within today’s networked economy, the complex solutions of fered to the customers are in many cases assembled from configurable sub­products themselves. Within this paper we describe a business case where due to organisational and confidentiality reasons a single­configurator approach is not applicable and several configurators along the supply chain must cooperate in finding correct product configurations and in presenting them to an online customer. We present an algorithm based on Constraint Satisfaction that takes the specific characteristics of the problem domain into account and compare our approach to other work in the field of Distributed Problem Solving. The implementation framework for distributed configuration which is currently developed in the EU­funded project CAWICOMS1 is discussed in the final sections.


Author(s):  
Martin Große-Rhode ◽  
Robert Hilbrich ◽  
Stefan Mann ◽  
Stephan Weißleder

2013 ◽  
Vol 118 ◽  
pp. 1-7 ◽  
Author(s):  
Yiliu Liu ◽  
Zixian Liu ◽  
Yukun Wang

AI Magazine ◽  
2011 ◽  
Vol 32 (3) ◽  
pp. 99 ◽  
Author(s):  
Andreas Falkner ◽  
Alexander Felfernig ◽  
Albert Haag

State of the art recommender systems support users in the selection of items from a predefined assortment (for example, movies, books, and songs). In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are repre¬sented in the form of a configuration knowledge base that describes the properties of allowed instances. Although the knowledge representation used is different compared to non-confi¬gurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this article we show how recommendation technologies can be applied for supporting the configuration of products. In addition to existing approaches we discuss relevant issues for future research.


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