Percorrer por autor "Mendes, Rui"
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- An heuristic approach for optimal location of gas supply units in transportation systemPublication . Nogueira, Teresa; Mendes, Rui; Vale, Zita; Cardoso, JoséThe best places to locate the Gas Supply Units (GSUs) on a natural gas systems and their optimal allocation to loads are the key factors to organize an efficient upstream gas infrastructure. The number of GSUs and their optimal location in a gas network is a decision problem that can be formulated as a linear programming problem. Our emphasis is on the formulation and use of a suitable location model, reflecting real-world operations and constraints of a natural gas system. This paper presents a heuristic model, based on lagrangean approach, developed for finding the optimal GSUs location on a natural gas network, minimizing expenses and maximizing throughput and security of supply.The location model is applied to the Iberian high pressure natural gas network, a system modelised with 65 demand nodes. These nodes are linked by physical and virtual pipelines – road trucks with gas in liquefied form. The location model result shows the best places to locate, with the optimal demand allocation and the most economical gas transport mode: by pipeline or by road truck.
- A Parallel Architecture for Solving Constraint Satisfaction ProblemsPublication . Mendes, Rui; Pereira, Jorge R.; Neves, José[Introduction] Real world problems can often be described by a set of constraints to be satisfied, where the goal is to find a feasible solution for the problem. The use of constraints allows one to model a wide variety of problems in a straightforward manner. However, finding a solution that either satisfies all constraints or maximizes some benefit is usually difficult, as existing algorithms for both problems are NP-hard. Furthermore, it is not always possible to satisfy all the constraints. In such cases, the goal is to find a solution that satisfies the maximum number of constraints (MMAX-CSPs) or one that satisfies the most important ones (MAX-WCSPs, where each constraint has a weight). Currently, local search is widely used to tackle these difficult problems. However, those methods are usually incomplete (i.e., they do not guarantee the optimum), and are often mislead by local optima. This situation is usually handled by restarting the search from another starting point. It is our goal in this paper to present a parallel architecture for solving constraint satisfaction problems (AntCSP); i.e., a parallel architecture that combines the stigmergetic capabilities of Ant Colony Optimization (ACO) metaheuristics, with local search heuristics to solve MANX-CSPs and Constraint Satisfaction and Optimization Problems (CSOPs). One of the main advantages of this approach is that no auxiliary structure is needed. The structure followed by the ants is the proof tree itself, in which the pheromones are laid. This not. only provides a very efficient implementation of pheromone updating but also a more general approach to solve CSPs. The other advantage is the use of parallelism both to have a number of ant colonies running in parallel and to have function distribution of local search procedures. This architecture thus provides a powerful tool to tackle these difficult problems by using available processing power.