ISEP – DEI – Comunicações em eventos científicos
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Percorrer ISEP – DEI – Comunicações em eventos científicos por autor "Neves, José"
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- Constraint programming approach to solve facility layout design problemsPublication . Tavares, José António; Neves, José; Ramos, CarlosWe propose a methodology to solve the facility layout design problem by using constraint logic programming (CLP), which has proved to be a technology that gives good results when applied to a combinatorial problem optimisation. Methods to solve facility layout problems have to deal with a large set of factors, namely sales and production estimations, manufacturing process compatibilities, delivery dates, quality, spatial requirements, economics, management, human resources and environment. These factors make the facility layout design an extremely complex problem to solve. This complexity motivated the development of a methodology to solve the facility layout design problem using CLP.
- Defining a model for agent-based participant support in group decision meetingPublication . Marreiros, Goreti; Ramos, Carlos; Neves, José; Ramos, Carlos; Vale, ZitaThis paper aim to present a multi-agent model for a simulation system, whose goal is to help one specific participant of multi-criteria group decision making process. This model has five main intervenients types: the human participant, who is using the simulation and argumentation support system; the participant agents, one associated to the human participant and the others simulating the others members of the decision meeting group; the directory agent; the proposal agents, representing the different alternatives for a decision (the alternatives are evaluated based on criteria); and the voting agent responsible for all the voting mechanisms. At this stage it is proposed a two phase algorithm. In the first phase each participant agent makes his own evaluation of the proposals under discussion, and the voting agent proposes a simulation of a voting. In second phase, after the dissemination of the voting results, each one of the participant agents will argue to convince the others to choose one of the possible alternatives. The arguments used to convince a specific participant are dependent on agent knowledge about that participant. The two-phase algorithm is applied iteratively.
- 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.