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Industrial Optimization

last modified Mar 24, 2015 01:11 PM

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DeCoMo - Decision support for complex multiobjective optimization problems

      Research profile

      Real-life industrial problems typically need to be considered from very different perspectives. This leads to the need of optimizing several conflicting objectives simultaneously. In the light of this, it is quite natural that one of the main driving forces behind the research of the group is multiobjective optimization.

      In multiobjective settings with continuous variables, there typically are infinitely many Pareto optimal solutions and the ultimate task of the decision maker is to determine the best, that is, the most preferred Pareto optimal solution which is to be implemented and tested in practice. However, it is very important that before the actual decision about the final solution takes place the decision maker should gain a good understanding about the trade-offs between the solution alternatives. The final decision should be firmly grounded.

      Benefits of multiobjective optimization include that the conflicting objectives are taken into account simultaneously leading to an overall insight of the problem. Therefore, multiobjective optimization can bring about a significant competitive advantage when compared to widely used simplistic approaches where e.g. only some primary objective is optimized and other, although important, objectives are left without a special attention. In different fields of industry, there is a lot of need for multiobjective treatment but not yet enough awareness about it and, thus, the group also faces the challenge of disseminating information about the potential of multiobjective optimization.

      One of the main research interests in the group is interactive multiobjective optimization. It supports the decision maker actively in finding the 'best' Pareto optimal solution by continuously involving him/her and his/her preferences in the solution process to guide the search. The continuous involvement enables the decision maker to learn about one's preferences and the problem/phenomenon considered as well as interdependencies between the objectives.

      In addition to MCDM and especially interactive multiobjective optimization, evolutionary multiobjective optimization, and different hybrid methods (incorporating benefits of different types of approaches), our group shares also interest in general mathematical programming, global optimization (e.g. evolutionary algorithms and memetic approaches) and optimization software development including, in particular, usability issues. Actually, the group is one of the few groups actively working with implementations of interactive multiobjective optimization methods.

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