Informatica

When handling engineering problems associated with optimal alternative selection a researcher often deals with not sufficiently accurate data. The alternatives are usually assessed by applying several different criteria. A method takes advantage of the relationship between fuzzy sets and matrix game theories can be offered for multicriteria decision-making. Practical investigations have already been discussed for selecting the variants water supply systems.

Fuzzy sets can be used in many old-fashioned aspects of our lives in order to reach better performance and make fairer judgments. Evaluation through examination is typically conducted by educational centers, and multiple choice question (MCQ) exams are widely applied to score the examinees. Since scoring is potentially a difficult process to judge, we propose to evaluate examinees by fuzzy evaluation method. This method can overcome the main shortcoming of the classical MCQs, i.e. the random selection of the choices. The evaluation of the proposed fuzzy MCQ is more accurate and its ranking of examinees is fairer than classical MCQ.

Grey numbers facilitate the representation of uncertainty not only for elements of a set, but also the set itself as a whole. This paper utilizes the notion of possibility degree from grey system theory coupled with the idea of dominance relation and partial order set (poset) from rough theory to represent uncertain information in a manner that maintains the degree of uncertainty of information for each tuple of the original data. Concept lattices of grey information system are constructed and a decision-making algorithm that combines with grey relational grade is described. A case study is used to demonstrate the supplier selection problem applying the proposed method. The research has concluded that the method is appropriate to use.

The asynchronous techniques that exist within the programming with distributed constraints are characterized by the occurrence of the nogood values during the search for the solution. The nogood type messages are sent among the agents with the purpose of realizing an intelligent backtrack and of ensuring the algorithm's completion.

In general terms some situations are described which require the exploitation of heuristics either to solve a mathematical optimization problem or to analyse results. A possibility to implement heuristic knowledge for selecting a suitable algorithm depending on available problem data and information retrieved from the user, is investigated in detail. We describe some inference strategies and knowledge representations that can be used in this case, and the rule-based implementation within the EMP system for nonlinear programming. Case studies are presented which outline on the one hand the heuristic recommendation of an optimization code and the achieved numerical results on the other hand.