A new approach for the problem of optimal task scheduling in flexible manufacturing systems is proposed in this work, as a combination of metaheuristic optimization techniques with the supervisory control theory of discrete-event systems. A specific encoding, the word-shuffling encoding, which avoids the generation of a large number of infeasible sequences, is employed. A metaheuristic method based on a Variable Neighborhood Search is then built using such an encoding. The optimization algorithm performs the search for the optimal schedules, while the supervisory control has the role of codifying all the problem constraints, allowing an efficient feasibility correction procedure, and avoiding schedules that are sensitive to uncertainties in the execution times associated with the plant operation. In this way, the proposed methodology achieves a system performance which is typical from model-predictive scheduling, combined with the robustness which is required from a structural control.

The Multi-Depot Vehicle Routing Problem (MDVRP) is an important variant of the classical Vehicle Routing Problem (VRP), where the customers can be served from a number of depots. This paper introduces a cooperative coevolutionary algorithm to minimize the total route cost of the MDVRP. Coevolutionary algorithms are inspired by the simultaneous evolution process involving two or more species. In this approach, the problem is decomposed into smaller subproblems and individuals from different populations are combined to create a complete solution to the original problem. This paper presents a problem decomposition approach for the MDVRP in which each subproblem becomes a single depot VRP and evolves independently in its domain space. Customers are distributed among the depots based on their distance from the depots and their distance from their closest neighbor. A population is associated with each depot where the individuals represent partial solutions to the problem, that is, sets of routes over customers assigned to the corresponding depot. The fitness of a partial solution depends on its ability to cooperate with partial solutions from other populations to form a complete solution to the MDVRP. As the problem is decomposed and each part evolves separately, this approach is strongly suitable to parallel environments. Therefore, a parallel evolution strategy environment with a variable length genotype coupled with local search operators is proposed. A large number of experiments have been conducted to assess the performance of this approach. The results suggest that the proposed coevolutionary algorithm in a parallel environment is able to produce high-quality solutions to the MDVRP in low computational time.

This work presents a local search approach to the High School Timetabling Problem. The addressed timetabling model is the one stated in the Third International Timetabling Competition (ITC 2011), which considered many instances from educational institutions around the world and attracted seventeen competitors. Our team, named GOAL (Group of Optimization and Algorithms), developed a solver built upon the Kingston High School Timetabling Engine. Several neighborhood structures were developed and used in a hybrid metaheuristic based on Simulated Annealing and Iterated Local Search. The developed algorithm was the winner of the competition and produced the best known solutions for almost all instances.

The application of the Late Acceptance Hill-Climbing (LAHC) to solve the High School Timetabling Problem is the subject of this manuscript. The original algorithm and two variants proposed here are tested jointly with other state-of-art methods to solve the instances proposed in the Third International Timetabling Competition. Following the same rules of the competition, the LAHC-based algorithms noticeably outperformed the winning methods. These results, and reports from the literature, suggest that the LAHC is a reliable method that can compete with the most employed local search algorithms.

A soja é um dos principais produtos agrícolas brasileiros. Do total colhido, aproximadamente 40% é destinado à exportação. Como as lavouras estão concentradas principalmente na região central no país, essa soja percorre, em geral, grandes distâncias até os portos marítimos. Nesse caso, o transporte intermodal pode tornar mais eficiente e econômica a movimentação de grandes volumes por longas distâncias. Analisa-se, neste estudo, a aplicação de um modelo de programação matemática para apoiar decisões de fluxo de soja e localização de terminais intermodais. Com base em dados secundários, foram propostas três redes alternativas de escoamento, com diferentes áreas de abrangência e possibilidade de utilização de ferrovias ainda em construção ou projeto. Foram considerados os principais estados produtores e movimentadores da soja. Experimentos computacionais resultaram em fluxos de escoamento e localização de terminais condizentes com a realidade. Cenários alternativos da rede também foram analisados, evidenciando o potencial de análise dessa ferramenta de otimização.

The High School Timetabling Problem requires the assignment of times and resources to events, while sets of required and desirable constraints must be considered. The most common approach for this problem is to employ metaheuristic methods. This work presents a matheuristic approach that combines a Variable Neighbourhood Search algorithm with mathematical programming-based neighbourhoods for high school timetabling. Computational experiments on well-known benchmark instances demonstrate the success of the proposed hybrid approach, which outperforms the standalone Variable Neighbourhood Search algorithm by far. Additionally, the proposed algorithm was able to improve 15 out of 17 current best known solutions in a very famous benchmark set.