Navegando por Autor "Souza, Sergio Ricardo de"
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Item Algorithms based on VNS for solving the Single Machine Scheduling Problem with Earliness and Tardiness Penalties.(2018) Rosa, Bruno Ferreira; Souza, Marcone Jamilson Freitas; Souza, Sergio Ricardo deThis work implements and compares four algorithms based on Variable Neighborhood Search (VNS), named RVNS, GVNSf, GVNSr and GVNSrf, for solving the Single Machine Scheduling Problem with Earliness and Tardiness Penalties (SM-SPETP). Computational experiments showed that the algorithm GVNSf obtained better-quality solutions compared with the other algorithms, including an algorithm found in the literature. The algorithms GVNSr and GVNSrf obtained solutions close to the GVNSf, and outperformed the algorithm of the literature, both with respect to the quality of the solutions and the computational times.Item An MO-GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem.(2019) Siqueira, Eduardo Camargo de; Souza, Marcone Jamilson Freitas; Souza, Sergio Ricardo deThis paper addresses the multiobjective hybrid flow shop (MOHFS) scheduling problem. In the MOHFS problem considered here, we have a set of jobs that must be performed in a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. The evaluation criteria are the minimizations of makespan, the weighted sum of the tardiness, and the weighted sum of the earliness. For solving it, an algorithm based on the multiobjective general variable neighborhood search (MO-GVNS) metaheuristic, named adapted MO-GVNS, is proposed. This work also presents and compares the results obtained by the adapted MO-GVNS with those of four other algorithms: multiobjective reduced variable neighborhood search, nondominated sorting genetic algorithm II (NSGA-II), and NSGA-III, and another MO-GVNS from the literature. The results were evaluated based on the Hypervolume, Epsilon, and Spacing metrics, and statistically validated by the Levene test and confidence interval charts. The results showed the efficiency of the proposed algorithm for solving the MOHFS problem.Item A general VNS for the multi‐depot open vehicle routing problem with time windows.(2023) Bezerra, Sinaide Nunes; Souza, Sergio Ricardo de; Souza, Marcone Jamilson FreitasThis paper presents an algorithm based on the variable neighborhood search (VNS) metaheuristic, called smart general VNS (SGVNS), to solve the multi-depot open vehicle routing problem with time windows (MDOVRPTW). For the problem, two single-objective approaches are proposed for cost assessment: one for reducing the total distance covered and the other for reducing the total number of vehicles used and, after, the total distance covered. SGVNS involves the perturbation and local search phases. In the perturbation phase, gradual changes are carried out in the neighborhoods to expand the diversifcation of solutions and escape from local optima. The random combination of specifc neighborhood structures is used in the local search to refne the solution generated in the previous phase. As no instances are known in the literature for MDOVRPTW, the computational tests are executed in two groups of classic MDVRPTW instances, involving up to 960 customers, 12 depots, and 120 vehicles. The present study made it possible to investigate cost improvements through the use of the MDOVRPTW model when compared to the MDVRPTW. There was a reduction in the distance covered in all instances evalu- ated. The total distance covered decreased by 12.07% in one of the reference groups and 10.43% in the other. For the frst group, the feet reduction occurred in 75% of the instances. In the second group, there was a reduction in all instances. It corre- sponds to −10.42% and −24.13% of the total vehicles used in each group, respec- tively. The SGVNS algorithm proved efective for the two problems for which it was applied, either in reducing the total traveled distance or in reducing the feet.Item Hybrid metaheuristics and multi-agent systems for solving optimization problems : a review of frameworks and a comparative analysis.(2018) Silva, Maria Amélia Lopes; Souza, Sergio Ricardo de; Souza, Marcone Jamilson Freitas; França Filho, Moacir Felizardo deThis article presents a review and a comparative analysis between frameworks for solving optimization problems using metaheuristics. The aim is to identify both the desirable characteristics as the existing gaps in the current state of the art, with a special focus on the use of multi-agent structures in the development of hybrid metaheuristics. A literature review of existing frameworks is introduced, with emphasis on their characteristics of hybridization, cooperation, and parallelism, particularly focusing on issues related to the use of multi-agents. For the comparative analysis, a set of twenty-two characteristics was listed, according to four categories: basics, advanced, multi-agent approach and support to the optimization process. Strategies used in hybridization, such as parallelism, cooperation, decomposition of the search space, hyper-heuristic and multi-agent systems are assessed in respect to their use in the various analyzed frameworks. Specific features of multi-agent systems, such as learning and interaction between agents, are also analyzed. The comparative analysis shows that the hybridization is not a strong feature in existing frameworks. On the other hand, proposals using multi-agent systems stand out in the implementation of hybrid methods, as they allow the interaction between metaheuristics. It also notes that the concept of hyper-heuristic is little explored by the analyzed frameworks, as well as there is a lack of tools that offer support to the optimization process, such as statistical analysis, self-tuning of parameters and graphical interfaces. Based on the presented analysis, it can be said that there are important gaps to be filled in the development of Frameworks for Optimization using metaheuristics, which open important possibilities for future works, particularly by implementing the approach of multi-agent systems.Item ILS-based algorithms for the profit maximizing uncapacitated hub network design problem with multiple allocation.(2023) Oliveira, Fabricio Alves; Sá, Elisangela Martins de; Souza, Sergio Ricardo de; Souza, Marcone Jamilson FreitasThis study addresses a hub network design problem to maximize net profit. This problem considers an incomplete hub network with multiple allocation that does not impose capacity constraints, does not allow direct connections between non-hub nodes, and accepts the demand to be partially met, being satisfied only when profitable. To tackle this problem, which is NP-hard, we propose two heuristic algorithms based on the Iterated Local Search (ILS) metaheuristic, a standard ILS algorithm, and an Enhanced ILS algorithm, which increases the perturbation level only after a few unsuccessful attempts at improvement. Both algorithms use Random Variable Neighborhood Descent in the local search. Computational experiments were performed using benchmark instances for hub location problems, and statistical analyzes of the algorithms were presented. Numerical results confirm that both algorithms yield good-quality solutions with an acceptable runtime. In particular, the proposed algorithms obtain the optimal solution for most instances with up to 150 nodes, which have known optimal solutions. Furthermore, the proposed algorithms were able to handle instances with up to 500 nodes.Item A Multi-objective Variable Neighborhood Search algorithm for solving the Hybrid Flow Shop Problem.(2018) Siqueira, Eduardo Camargo de; Souza, Marcone Jamilson Freitas; Souza, Sergio Ricardo deThis paper addresses the Hybrid Flow Shop Problem (HFSP) through the Multi-objective Variable Neighborhood Search metaheuristic (MOVNS). In this problem, we have a set of jobs that must be performed on a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. In this paper we considere two evaluation criteria under simultaneous analysis: the minimization of the makespan and the minimization of the weighted sum of tardiness. Instances of the HFSP from literature are solved by four versions of the MOVNS algorithm. The results are evaluated using the Hypervolume, Epsilon, Spacing and Sphere counting metrics.Item Problema de movimentação do carro tripper : uma abordagem via programação dinâmica aproximada.(2021) Santos, Mayra Cristina Silva; Silva, Thiago Augusto de Oliveira; Souza, Maurício Cardoso de; Silva, Thiago Augusto de Oliveira; Souza, Maurício Cardoso de; Martins, Alexandre Xavier; Souza, Sergio Ricardo deDevido à sua importância, o setor mineral é alvo de estudos constantes, visando aprimoramentos ao longo de sua cadeia produtiva. Nesse sentido, o presente trabalho aborda o problema de movimentação do carro tripper, um problema de sequenciamento que visa determinar os movimentos que o equipamento deve realizar para descarregar o minério sobre os silos. Foram propostos métodos de solução para o problema determinístico proposto por Caldas e Martins (2018) e para uma versão estocástica desenvolvida para representar a natureza dinâmica do problema. Para a realização dos testes, foram utilizadas adaptações de uma instância presente na literatura. A partir dos resultados obtidos, verifica-se que, tanto no problema determinístico quanto no estocástico, alguns métodos apresentaram resultados satisfatórios em relação ao tempo de execução e à performance do algoritmo, sendo a performance dependente da combinação de funções utilizada no método de aproximação de programação dinâmica. Ademais, ainda no que diz respeito ao método de aproximação de programação dinâmica, o desempenho do modelo estocástico também se mostrou dependente do estado inicial utilizado e da realização do treinamento para cada novo estado.Item A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems.(2019) Silva, Maria Amélia Lopes; Souza, Sergio Ricardo de; Souza, Marcone Jamilson Freitas; Bazzan, Ana Lucia CetertichThis article presents a multi-agent framework for optimization using metaheuristics, called AMAM. In this proposal, each agent acts independently in the search space of a combinatorial optimization problem. Agents share information and collaborate with each other through the environment. The goal is to enable the agent to modify their actions based on experiences gained in interacting with the other agents and the environment using the concepts of Reinforcement Learning. For better introduction and validation of the AMAM framework, this article uses the instantiation of the Vehicle Routing Problem with Time Windows (VRPTW) and the Unrelated Parallel Machine Scheduling Problem with Sequence-Dependent Setup Times (UPMSP-ST), i.e., two classic combinatorial optimization problems. The main objective of the experiments is to evaluate the performance of the proposed adaptive agents. The experiments confirm that the ability to learn attributed to the agent directly influences the quality of solutions, both from the individual point of view and from the point of view of teamwork. In this way, the framework presented here is a step forward in relation to the other frameworks of the literature regarding to the adaptation to the particular aspects of the problems. Additionally, the cooperation between agents and their ability to influence the quality of the solutions of the agents involved in the search of the solution is confirmed. The results also strengthen the issue of the scalability of the framework, since, with the addition of new agents, there is an improvement of the solutions obtained.