The decision maker tends to use natural language terms when interrogating a data warehouse, hence the need to integrate these terms into the data warehouse interrogation systems. In this article, we present a panorama of the work that has dealt with the treatment of ambiguity in decision-making queries and propose a solution that uses fuzzy logic and allows the interrogation of the data warehouse with terms derived from the language using fuzzy MDX queries.

This paper deals with a fuzzy genetic algorithm applied to a manufacturing cell formation problem. We discuss the importance of taking into account the dynamic aspect of the problem that has been poorly studied in the related literature. Using a multi-periodic planning horizon modeling, two strategies are considered: passive and active. The first strategy consists of maintaining the same composition of machines during the overall planning horizon, while the second allows performing a different composition for each period. When the decision maker wants to choose the most adequate strategy for its environment, there is a need to control the proposed evolutionary solving approach, due to the complexity of the model. For that purpose, we propose an off-line fuzzy logic enhancement. The results, using this enhancement, are better than those obtained using the GA alone.

A methodology for modeling a system composed of parallel activities with synchronization points is proposed. Specifically, an approach based on a modular state-transition representation of a parallel system called the stochastic automata network (SAN) is developed. The state-space explosion is handled by a decomposition technique. The dynamic behavior of the algorithm is analyzed under Markovian assumptions. The transition matrix of the chain is automatically derived using tensor algebra operators, under a format which involves a very limited storage cost.>