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represent technical restrictions, restrictions related to economic aspects, and conditions related to production processes. The result of a configuration process is a product configuration (concrete configuration), i.e., a list of instances and in some cases also connections between these instances. Examples of such configurations are computers to be delivered or financial service portfolio offers (e.g., a combination of loan and corresponding risk insurance).
142:(BOM) are major tasks to be supported by a configurator. Configuration knowledge bases are often built using proprietary languages. In most cases knowledge bases are developed by knowledge engineers who elicit product, marketing and sales knowledge from domain experts. Configuration knowledge bases are composed of a formal description of the structure of the product and further constraints restricting the possible feature and component combinations.
122:(ASP) representations. There are two commonly cited conceptualizations of configuration knowledge. The most important concepts in these are components, ports, resources and functions. This separation of product domain knowledge and problem solving knowledge increased the effectiveness of configuration application development and maintenance, since changes in the product domain knowledge do not affect search strategies and vice versa.
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toolkits", i.e., tools that support customers in the product identification phase. In this context customers are innovators who articulate their requirements leading to new innovative products. "Mass
Confusion" – the overwhelming of customers by a large number of possible solution alternatives
58:
application area, see, e.g. Informally, configuration can be defined as a "special case of design activity, where the artifact being configured is assembled from instances of a fixed set of well-defined component types which can be composed conforming to a set of constraints". Such constraints
130:(choices) – is a phenomenon that often comes with the application of configuration technologies. This phenomenon motivated the creation of personalized configuration environments taking into account a customer's knowledge and preferences.
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K. C. Kang, S. G. Cohen, J. A. Hess, W. E. Novak, and A. S. Peterson, Feature-oriented domain analysis (FODA) feasibility study, Technical Report CMU/SEI-90-TR-21 ESD-90-TR-222, Software
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Recently, knowledge-based configuration has been extended to service and software configuration. Modeling software configuration has been based on two main approaches: feature modeling, and component-connectors. Kumbang
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C. Forza and F. Salvador, Managing for variety in the order acquisition and fulfillment process: The contribution of product configuration systems, International
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110:, model-based representations of knowledge (in contrast to rule-based representations) have been developed that strictly separate product domain knowledge from problem solving knowledge—examples thereof are the
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G. Fleischanderl, G. Friedrich, A. Haselboeck, H. Schreiner, and M. Stumptner, Configuring Large
Systems Using Generative Constraint Satisfaction, IEEE Intelligent Systems, vol. 13, no. 4, pp. 59–68, 1998.
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T. Soininen, J. Tiihonen, T. Männistö, and R. Sulonen, Towards a
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46:(AI), and it is based on modelling of the configurations in a manner that allows the utilisation of AI techniques for searching for a valid configuration to meet the needs of a particular customer.
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a product to meet the needs of a particular customer. The product in question may consist of mechanical parts, services, and software. Knowledge-based configuration is a major application area for
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Y. Wang, and M. Tseng, Adaptive
Attribute Selection for Configurator Design via Shapley Value. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 25 (1): 189–199, 2011.
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Core configuration, i.e., guiding the user and checking the consistency of user requirements with the knowledge base, solution presentation and translation of configuration results into
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Numerous practical configuration problems can be analyzed by the theoretical framework of Najmann and Stein, an early axiomatic approach that does not presuppose any particular
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technologies. Examples are the automotive industry, the telecommunication industry, the computer industry, and power electric transformers. Starting with
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use sets of discrete variables that are either binary or have one of several values, and these variables define every possible product variation.
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E. Juengst and M. Heinrich, Using Resource Balancing to Configure Modular Systems, IEEE Intelligent Systems, vol. 13, no. 4, pp. 50–58, 1998.
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formalism. One important result of this methodology is that typical optimization problems (e.g. finding a cost-minimal configuration) are
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combines the previous approaches building on the tradition of knowledge-based configuration.
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M. Stumptner, An Overview of Knowledge-Based Configuration. AI Commun. 10(2): 111–125, 1997.
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configuration algorithms the preferred choice for complex artifacts (products, services).
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Knowledge-based configuration (of complex products and services) has a long history as an
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U. Blumöhr, M. Münch, M. Ukalovic, Variant Configuration with SAP, Galileo Press, 2012.
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Special Issue on Configuration in the International Journal of Mass Customization 2006
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Kumbang: A domain ontology for modelling variability in software product families
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and beyond, Communications of the ACM, vol. 32, no. 3, pp. 298–318, 1989.
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Knowledge-based Configuration: From Research to Business Cases
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IEEE Intelligent Systems Special Issue on Configuration 2007
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20+ years of International Workshops on Configuration
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539:T. Asikainen, T. Männistö, and T. Soininen,
125:Configurators are also often considered as "
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545:Advanced Engineering Informatics
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116:Boolean satisfiability problem
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217:Software product line
182:Configure price quote
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