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Large-scale engineering projects typically involve many different types of professionals who must interact and communicate with one another. This interaction produces conflicts that need to be resolved. A framework is presented in which the rationale used in a collaborative design environment for designing an artifact is also used for conflict mitigation. The framework contains mechanisms for checking interactions and prompting hypotheses about the reasons for the interactions. These hypotheses, once verified by the designers, improve conflict resolution by assisting them in coordinating and negotiating conflicts. This, in turn, enhances communication during the design process and consequently increases productivity in the engineering industry.
This paper presents an approach to solving constraint satisfaction problems using Asynchronous Teams of autonomous agents (ATeams). The focus for the constraint satisfaction problem is derived from an effort to support spatial layout generation in a conceptual design framework. The constraint specification allows a high-level representation and manipulation of qualitative geometric information. We present a computational technique based on ATeams to instantiate solutions to the constraint satisfaction problem. The technique uses a search for a solution in numerical space. This permits us to handle both qualitative relationships and numerical constraints in a unified framework. We show that simple knowledge, about human spatial reasoning and about the nature of arithmetic operators can be hierarchically encapsulated and exploited efficiently in the search. An example illustrates the generality of the approach for conceptual design. We also present empirical studies that contrast the efficiency of ATeams with a search based on genetic algorithms. Based on these preliminary results, we argue that the ATeams approach elegantly handles arbitrary sets of constraints, is computationally efficient, and hence merits further investigation.
Mass customization has been identified as a competitive strategy by
an increasing number of companies. Family-based product design is an
efficient and effective means to realize sufficient product variety,
while satisfying a range of customer demands in support for mass
customization. This paper presents a knowledge decision support
approach to product family design evaluation and selection for mass
customization process. Here, product family design is viewed as a
selection problem with the following stages: product family (design
alternatives) generation, product family design evaluation, and
selection for customization. The fundamental issues underlying product
family design for mass customization are discussed. Then, a knowledge
support framework and its relevant technologies are developed for
module-based product family design for mass customization. A systematic
fuzzy clustering and ranking model is proposed and discussed in detail.
This model supports the imprecision inherent in decision making with
fuzzy customers' preference relations and uses fuzzy analysis
techniques for evaluation and selection. A neural network technique is
also adopted to adjust the membership function to enhance the model.
The focus of this paper is on the development of a knowledge-intensive
support scheme and a comprehensive systematic fuzzy clustering and
ranking methodology for product family design evaluation and selection.
A case study and the scenario of knowledge support for power supply
family evaluation, selection, and customization are provided for
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