This project deals with the development of a complete system to manage the covenants and projects of the Federal University of Ouro Preto. It includes the project and team registration, budget approval, and other evaluation cycles.

This project developed a portal for all students and professors of the Federal University of Ouro Preto. This portal allows the university community to access all available systems. Besides, it is a channel to send messages to the community.

This work presents an evolutionary multi-agent system applied to the query optimization phase of Relational Database Management Systems (RDBMS) in a non-distributed environment. The query optimization phase deals with a known problem called query join ordering, which has a direct impact on the performance of such systems. The proposed optimizer was programmed in the optimization core of the H2 Database Engine. The experimental section was designed according to a factorial design of fixed effects and the analysis based on the Permutations Test for an Analysis of Variance Design. The evaluation methodology is based on synthetic benchmarks and the tests are divided into three different experiments: calibration of the algorithm, validation with an exhaustive method and a general comparison with different database systems, namely Apache Derby, HSQLDB and PostgreSQL. The results show that the proposed evolutionary multi-agent system was able to generate solutions associated with lower cost plans and faster execution times in the majority of the cases.

This work deals with the problem of scheduling a single machine to minimize earliness and tardiness, with distinct due windows and sequence dependent setup times. Due the computational complexity of solving the problem, a heuristic approach based on GRASP, Variable Neighborhood Descent and Tabu Search with Path Relinking, so-called GTSPR, is proposed to solve it. The GRASP metaheuristic is used to construct an initial solution. In this phase of the method, the local search is done by the Variable Neighborhood Descent heuristic. In the second phase, the resulting solution of GRASP is refined by the Tabu Search metaheuristic. The third phase consists in applying Path Relinking as a mechanism of post-optimization. The proposed algorithm explores the space solutions by using swap moves of jobs and reassignment moves of a job or a block of jobs. For each job sequence generated by the algorithm, an optimal timing algorithm is used to determine the completion time for each job in the job sequence. Computational experiments realized with instances of the literature show that the method is able to find the optimal solution quickly in small instances (8 to 12 jobs) and, in instances with 15 to 75 jobs, is able to improve previous algorithm of the literature, with respect to the quality of the final solution and the average gap.