No abstract

O Problema do Caixeiro Viajante com Coleta de Prêmios (PCVCP) pode ser associado a um caixeiro que coleta um prêmio em cada cidade visitada e paga uma penalidade para cada cidade não visitada, com um custo de deslocamento entre as cidades. O problema encontra-se em minimizar o somatório dos custos da viagem e penalidades, enquanto inclui na sua rota um número suficiente de cidades que lhe permita coletar um prêmio mínimo preestabelecido. Este trabalho contribui com o desenvolvimento de metaheurísticas híbridas para o PCVCP, baseadas em GRASP e métodos de busca em vizinhança variável (VNS/VND) para solucionar aproximadamente o PCVCP. De forma a validar as soluções obtidas, propõe-se uma formulação matemática a ser resolvida por um solver comercial, objetivando encontrar a solução ótima para o problema, sendo este solver aplicado a problemas de pequeno porte. Resultados computacionais demonstram a eficiência da abordagem híbrida proposta, tanto em relação à qualidade da solução final obtida quanto em relação ao tempo de execução.

This paper presents two hybrid metaheuristics to solve a multiproduct two-stage capacitated facility location problem (MP-TSCFLP). In this problem, a set of different products must be transported from a set of plants to a set of intermediate depots (first stage) and from these depots to a set of customers (second stage). The objective is to minimize the cost related to open plants and depots plus the cost for transporting the products from the plants to the customers satisfying demand and capacity constraints. Recently, the methods clustering search (CS) and biased random-key genetic algorithm (BRKGA) were successfully applied to solve a singleproduct problem (SP-TSCFLP). Therefore, in this paper we propose adaptations and implementations of these methods for handling with a multiproduct approach. To the best of our knowledge, CS and BRKGA presented the best results for the SP-TSCFLP and both have not yet been applied to solve the problem with multiple products. Four sets of large-sized instances with different characteristics are proposed and computational experiments compare the obtained results to those from a commercial solver.

Abstract. The Traveling Tournament Problem (TTP) is an optimization problem that represents some types of sports timetabling, where the objective is to minimize the total distance traveled by the teams. This work proposes the use of a hybrid heuristic to solve the mirrored TTP (mTTP), called Clustering Search (*CS), that consists in detecting supposed promising search areas based on clustering. The validation of the results will be done in benchmark problems available in literature and real benchmark problems, e.g. Brazilian Soccer Championship.

Abstract. The Mirrored Traveling Tournament Problem (mTTP) is an optimization problem that represents certain types of sports timetabling, where the objective is to minimize the total distance traveled by the teams. This work proposes the use of hybrid heuristic to solve the mTTP, using an evolutionary algorithm in association with the metaheuristic Simulated Annealing. It suggests the use of Genetic Algorithm with a compact genetic codification in conjunction with an algorithm to expand the code. The validation of the results will be done in benchmark problems available in literature and real benchmark problems, e.g. Brazilian Soccer Championship.