Knowledge Modelling and
ヒュン研究室 HUYNH Laboratory
准教授：ヒュン ナム ヤン（Huynh Nam Van）
Decision science/Operations research, Computational inteligence, Data mining
Knowledge-driven decision making, Knowledge-based decision systems, Uncertainty management, Applications
Our lab welcomes students with a bachelor&aposs or master&aposs degree in a quantitative subject such as mathematics, statistics, informatics, engineering, management science, or economics.
Students are expected to develop valuable knowledge and problem-solving skills that can be applied in the areas of practical applications of operations research and decision science. These problem-solving skills include the ability of 1) identifying and formulating decision problems; 2) acquiring and modelling knowledge for solving them; and 3) generating, evaluating, validating and implementing solutions to these problems.
【就職先企業・職種】 Managerial consulting, Operations management and IT management
Our laboratory research aims to combine foundational work on knowledge engineering and decision science with practical applications in engineering management and decision support systems. Such an approach helps to guide theoretical research, ensuring that it is relevant, effective and reliable, while providing novel methods to solve complex decision problems.
Solving real-world decision problems requires the integration of knowledge and information from different relevant sources and further takes uncertainty and impreciseness into account. The latter are often both present in many types of decision problem and may be due to, for instance, a lack of information, error in measurements, human’s subjective judgments, or ambiguous meanings in criteria and assessments. Our research therefore focuses on the development of knowledge modelling methodologies that not only have the capability of rationally handling different types of imperfection in knowledge, but also allow us to integrate effectively different knowledge sources. We are also concerned with developing evaluation methodologies that integrate tools and methods in AI into the well-established decision making paradigms so as to be able to deal effectively with complex decision problems.
Our research will find applications in such areas as quality management, supply chain collaboration, product and service evaluation, safety and risk assessment, product innovations, engineering design areas, and personalized recommendation in e-commerce. Currently, the following applications that serve as a guide for the methodological research are studied and explored in our laboratory:
- Integrating social sentiment data into the evaluation process for personalized recommendation
- Customer-oriented evaluation of traditional products.
- Screening product innovations.
- Supplier evaluation and selection.
- Partner evaluation for collaboration in tourism services
- V.-N. Huynh, V. Kreinovich, S. Sriboonchitta, S. Komsan (Eds.), Econometrics of Risk, Studies in Computational Intelligence 583, Springer-Verlag, January 2015.
- V.-N. Huynh, Y. Nakamori. A linguistic screening evaluation model in new product development, IEEE Transactions on Engineering Management 58 (2011), 165-175.
- V.-N. Huynh, H. Yan, Y. Nakamori. A target-based decision-making approach to consumer-oriented evaluation model for Japanese traditional crafts, IEEE Transactions on Engineering Management 57 (2010), 575-588.
Our education and research strategy is trying to balance the advancement of theoretical research and its applicability in practice. Students will be exposed to relevant practical problems during development and improvement of their systems thinking and modelling competencies, which in turn helping them get insights into problems, inspired and guided theoretical research (making it relevant, innovative, effective and significant) in knowledge modelling and decision making. This strategy is done through regularly-held lab meetings.