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This paper studied the permutation flowshop with sequence dependent setup times and makespan minimization. An ant colony algorithm which turns the original problem into an asymmetric TSP (Traveling Salesman Problem) structure is presented, and applied to problems proposed in the literature and is compared with an adaptation of the NEH heuristic. Subsequently a neighborhood search was applied to the solution obtained by the ACO algorithm and the NEH heuristic. [ABSTRACT FROM AUTHOR]

ACO is a metaheuristic inspired in the behavior of natural ant colonies to solve combinatorial optimization problems, based on simple agents that work cooperatively communicating by artificial pheromone trails. In this paper a model to solve the municipal waste collection problem by containers is presented, which applies a concept of partial collection sequences that must be joined to minimize the total collection distance. The problem to join the partial collection sequences is represented as a TSP, which is solved by an ACO algorithm. Based on the literature, algorithm parameters are experimentally calibrated and range of variations that represents good average solutions are recommended. The model is applied to a waste collection sector of the San Pedro de la Paz commune in Chile, obtaining recollection routes with less total distance with respect to the actual route utilized and to the solution obtained by a previously developed approach. [ABSTRACT FROM AUTHOR]

In this paper, an analysis of evolutionary algorithms for multi objective optimization, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA-II) is presented. For this analysis, is taken as reference an optimization problem in a multicast data network, which has as objective functions the hop count and transmission delay. The algorithms performance is compared in tree different networks. Moreover, the model for two of this topologies using GAMS tool is resolved and results are compared with the NSGA-II and SPEA-II algorithms proposed. Problem results show the algorithms performance in their solution. [ABSTRACT FROM AUTHOR]

A custom processor based on reconfigurable hardware technology is proposed in order to compute the figure of merit used to measure the quality of the fit of X-ray diffraction peaks. As the experimental X-ray profiles can present many peaks severely overlapped, it is necessary to select the best model among a large set of reasonably good solutions. Determining the best solution is computationally intensive, because this is a hard combinatorial optimization problem. The proposed processors, working in parallel, increase the performance relative to a software implementation. [ABSTRACT FROM AUTHOR]