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This paper aims at proposing a Constraint-based Learning Estimation of Distribution Algorithm (EDA) to solve the Multi-Mode Resource Constrained Project Scheduling Problem (MMRCPSP). This problem is considered a combinatorial optimization problem and consists of a set of tasks to be carried out in an ordered way, limited availability of resources, and an execution mode for each task. Generally, traditional EDAs handle the constraints of the problem using strategies such as penalty, Pareto dominance, or others, without taking into account these constraints during the learning process. These traditional approaches usually cause the generation of infeasible solutions in each iteration. In this paper, an approach based on the constraints handling during the learning process of the probabilistic model is proposed. To validate the performance of the algorithms, the new approach in two scenarios is compared. First, in the solution of a set of classical optimization problems, where different EDAs are compared in terms of efficiency and efficacy. The second group of experiments is performed on a set of instances of the PSPLIB database. In this case, the proposed algorithm is compared with a traditional EDA and with other results reported in the bibliography. The experimental results demonstrate that the proposal outperforms both the results of traditional EDA and other EDA reported in the literature. [ABSTRACT FROM AUTHOR]

This paper presents an alternative procedure to solve the Capacitated Vehicle Routing Problem (CVRP) with homogeneous fleet. The paper proposes a two-phase metaheuristic algorithm: routes design and fleet scheduling. The first phase is based on heuristics and metaheuristics procedures in order to build an initial solution that is then improved using tabu search to obtain non-dominated solutions in polynomial computational time. For the second phase, corresponding to fleet scheduling, the problem is approached using an analogy with the identical parallel machine scheduling problem. This procedure looks for the minimization of the fixed cost of using installed capacity as the objective function. The proposed procedure was tested using both a random-generated instance and real data, giving competitive results in comparison with other heuristics tested. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to solve the problem of distribution system planning, using a multi-objective optimization technique. In the proposed model the location and design of new elements and the upgrading of existing elements are considered. In the problem approach, investment and operation costs and network reliability are included in the objective function. This research topic is relevant to the electricity distribution companies, as it allows for new tools that bring the problem to real situations, such as considering multiple objectives, which has a positive impact on the finances and on operation of the system. In the model solution, an elitist non-dominated sorting algorithm (NSGA-II) is used, and to verify its efficiency a case of specialized literature is used, which corresponds to a distribution network of an electrical system. [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]