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Welcome to the ORCS Lab
Welcome to the Laboratory of Operational Research and Complex Systems. Feel free to explore the website and get to know our projects and the people behind the algorithms.
ORCS Lab - Optimisation, Data Science and Complex Systems
Research
Research in the ORCS Lab focuses mainly on the development and application of optimisation methods to complex systems; on data science and statistical modeling; and on the applications of computational intelligence to large-scale power and energy systems.

Latest News

ORCS Lab's Prof. Eduardo Carrano has received excellent news today, with two of his papers being accepted for publication on the same day!

The first one, accepted for publication in the IET Generation, Transmission & Distribution journal (IF: 1.576) is titled Robust Coordination of Directional Overcurrent Relays Using a Matheuristic Algorithm, by Mateus Costa, Rodney Saldanha, Martín Ravetti and Eduardo Carrano, is a result of Mateus' Ph.D. work (under prof. Carrano) in collaboration with UFMG professors Saldanha (Dept. Electrical Engineering) and Ravetti (Dept. Production Engineering). 

The second, to appear in the Transactions of the Institute of Measurement and Control (IF: 0.820) is the manuscript Fixed Time Traffic Signal Optimization Using a Multiobjective Evolutionary Algorithm and Microsimulation of Urban Networks, by Breno Costa, Samara Leal and Paulo Almeida (all from CEFET-MG) together with Eduardo Carrano.

You can find the abstracts by clicking the "Read More" button below.

Robust Coordination of Directional Overcurrent Relays Using a Matheuristic Algorithm
Abstract:
Directional overcurrent relays are often used in the protection of electrical power transmission and distribution systems. These devices are installed along the network, and they must be coordinated to operate as fast as possible and in the adequate sequence. However, the coordination of relays is a complex problem, due to its discrete non-linear nature and its hard constraint structure. A matheuristic algorithm to provide coordination of directional overcurrent is proposed in this work. The algorithm combines a Differential Evolution strategy and Linear Programming formulations. In addition, two local search procedures are proposed to improve convergence and to handle continuous and discrete relay setups. Results for five instances show that the proposed method is able to obtain identical or better solutions than those already reported in the literature with considerably lower computational cost. Furthermore, the algorithm can directly handle robust coordination by considering possible changes in the systems conditions.


Fixed Time Traffic Signal Optimization Using a Multiobjective Evolutionary Algorithm and Microsimulation of Urban Networks
Abstract: Large cities have been facing serious problems in the management of traffic, due to the increasing number of vehicles and pedestrians. Traffic engineering is essential to manage traffic and to improve urban mobility. This manuscript deals with the problem of fixed time signal programming on traffic networks. A new bi-objective optimization model is proposed: to maximize the average and to minimize the variance of the vehicle speeds in the network. Although the first objective function is common in the literature, the second one is novel, and it intents to provide flow balance along the network. This combination of functions is optimized by the Memory-Based Variable Length Nondominated Sorting Genetic Algorithm 2 (MBVL-NSGA2), which avoids the revaluation of candidate solutions. This approach was validated through experiments using the microscopic simulator GISSIM, in a multi-intersectionreal network, using measured data from Belo Horizonte traffic engineering company (BHTRANS). The practical results of MBVL-NSGA2 algorithm were compared with four approaches: (1) BHTRANS current solutions; (2) genetic algorithm optimizing the first function; (3) genetic algorithm optimizing the second function, and; (4) the traditional NSGA2. The analysis showed that this proposal is able to generate better traffic signal plans, at the same time that it generates a diversified set of efficient candidate solutions.