Marcos A ntonio Pereira

Learn More
The Lagrangean/surrogate relaxation was explored recently as a faster computational alternative to traditional Lagrangean heuristics. We combine Lagrangean/surrogate relaxation and traditional column generation approaches to accelerate and stabilize primal and dual bounds, through an improved reduced cost selection. The Lagrangean/surrogate multiplier(More)
This paper investigates the creation of efficiency measurements structures of decision-making units (DMUs) called Neuro-DEA, by using high-speed optimization modules called Neuro-LP, inspired in the "philosophy" of an unconventional artificial neural network (ANN) and numerical methods. In addition, the linear programming problem LPP is transformed into an(More)
Congestion cost allocation is an important issue in congestion management. This paper presents a genetic algorithm (GA) to determine the optimal generation levels in a deregulated market. The main issue is congestion in lines, which limits transfer capability of a system with available generation capacity. Nodal pricing method is used to determine(More)
This work presents a linear quadratic model predictive controller (MPC) implemented in an industrial programable logic controller (PLC). The control law is calculated by solving on-line the quadratic programming problem derived from the optimization control problem of MPC. Nesterov's fast gradient algorithm has been used to solve the corresponding linear(More)
In this paper we consider periodic optimal operation of constrained periodic linear systems. We propose an economic model predictive controller based on a single layer that unites dynamic real time optimization and control. The proposed controller guarantees closed-loop convergence to the optimal periodic trajectory that minimizes the average operation cost(More)
  • 1