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- ItemAcesso aberto (Open Access)Influence of periodic lift and drag on the orbit of rectangular parallelepiped satellites(Shahriar Afkhami, 2023-12-07) de Moraes, Rodolpho [UNIFESP]; Murcia-Piñeros, Jhonathan [UNIFESP]; Prado, A.F.B. de Almeida; http://lattes.cnpq.br/5577796502707425Satellites in Low Earth Orbits (LEO) are highly perturbed by the interaction with the atmosphere, the two components of this perturbation are lift and drag, which initially are a function of the satellite’s geometry, materials, atmosphere, and flow conditions. Due to the uncertainty in these values, the traditional model is to fix the coefficients along the orbit lifetime, without considering the changes in flow direction, product of the satellite attitude, and rotational dynamics. The purpose of the present paper is to analyse the influence of periodic variations of lift and drag on the satellite’s orbital elements, due to the satellite’s rotation at a constant angular velocity. Three parallelepiped solids were selected to model the satellite geometry, which is similar to the CubeSats standard. The aerodynamic coefficients are modelled using the panel method on free molecular flow, as a function of the angle of attack and the roll angle. The effects of periodic variations in drag result in secular perturbations in the orbital semi-major axis and eccentricity, with large differences at the lowest angular velocity. Lift, applied orthogonal to the plane orbit, affects the inclination at lower angular velocities because it keeps the magnitude and direction of this perturbation for a longer time. The novelty of the paper is the detection and quantification of the effects of periodic lift and drag in LEO on parallelepiped satellites.
- ItemEmbargoA parallel branch-and-cut and an adaptive metaheuristic to solve the family traveling salesman problem(Elsevier, 2023) Chaves, Antonio Augusto [UNIFESP]; Vianna, Barbara Lessa [UNIFESP]; Silva, Tiago Tibúrcio da [UNIFESP]; Schenekemberg, Cleder Marcos [UNIFESP]; http://lattes.cnpq.br/4973949421738244This paper addresses the Family Traveling Salesman Problem (FTSP), a variant of the Traveling Salesman Problem that group nodes into families. The goal is to select the best route by visiting only a subset of nodes from each family. We developed two methods to solve the FTSP: (i) a parallel branch- and-cut algorithm with an efficient local search procedure (P-B&C) to obtain an optimal solution, and (ii) an adaptive metaheuristic that combines the Biased Random-key Genetic Algorithm (BRKGA) with a reinforcement learning algorithm. In this case, the Q-Learning algorithm controls the parameters of the BRKGA during the evolutionary process. We perform computational experiments on a well-known benchmark dataset with 185 instances. Our P-B&C proves the optimal value for 179 instances, improving the best upper bounds in 19 open instances. The new local search component of the P-B&C finds the best upper bounds for 50% of instances. The BRKGA-QL finds the optimal solution in 131 instances, improving the best upper bounds in 21 open instances. Finally, we compare our results with the best results in the literature, and both methods show robustness and efficiency in solving the FTSP.
- ItemEmbargoHybrid metaheuristics to solve a multi-product two-stage capacitated facility location problem(Wiley, 2021) Chaves, Antonio Augusto [UNIFESP]; Mauri, Geraldo Regis; Biajoli, Fabricio Lacerda [UNIFESP]; Rabello, Rômulo Louzada; Ribeiro, Glaydston Mattos; Lorena, Luiz Antônio Nogueira [UNIFESP]; http://lattes.cnpq.br/4973949421738244This paper presents two hybrid metaheuristics to solve a multi-product two-stage capacitated facility location prob- lem (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 until 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 single-product problem (SP-TSCFLP). Therefore, in this paper we propose adaptations and implementations of these methods for handling with a multi-product 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.
- ItemEmbargoA new multicommodity flow model for the job sequencing and tool switching problem(Taylor and Francis Online, 2021) Chaves, Antonio Augusto [UNIFESP]; Silva, Tiago Tiburcio da [UNIFESP]; Yanasse, Horacio Hideki [UNIFESP]; http://lattes.cnpq.br/4973949421738244In this paper a new multicommodity flow mathematical model for the Job Sequencing and Tool Switching Problem (SSP) is presented. The proposed model has a LP relaxation lower bound equal to the number of tools minus the tool machine’s capacity. Computational tests were performed comparing the new model with the models of the literature. The proposed model performed better, both in execution time and in the number of instances solved to optimality.
- ItemEmbargoThe Dial-A-Ride Problem With Private Fleet And Common Carrier(Elsevier, 2022) Chaves, Antonio Augusto [UNIFESP]; http://lattes.cnpq.br/4973949421738244Dial-a-ride problems aim to design the least-costly door-to-door vehicle routes for transporting individual users, subject to several service constraints like time windows, service and route durations, and ride-time. In some cases, providers cannot meet the demand and may outsource some requests. In this paper, we introduce, model, and solve the dial-a-ride problem with private fleet and common carrier (DARP-PFCC) that makes it possible to transfer the demand unmet by the provider to mobility-on-demand services and taxis. All outsourced vehicles are assumed to be available at any instant of the day and have unlimited capacity, enabling to satisfy all user requests, particularly during peak times. We implement a branch-and- cut (B&C) algorithm based on an exact method from the literature to solve the DARP-PFCC, and we develop a near parameter-free parallel metaheuristic to handle large instances. Our metaheuristic combines the Biased Random-key Genetic Algorithm (BRKGA) and the Q-learning (QL) method into the same framework (BRKGA-QL), in which an agent helps to use feedback information to dynamically choose the parameters of BRKGA during the search to select the most appropriate configuration to solve a specific problem instance. Both algorithms are flexible enough to solve the classical DARP, and extensive computational experiments demonstrate the efficiency of our methods. For the DARP instances, the B&C proved optimality for 41 of the 42 instances tested in a reasonable computational time, and the BRKGA-QL found the best-known solution for these instances within a matter of seconds. These results indicate that our metaheuristic performs equally well than state-of-the-art DARP algorithms. In the DARP-PFCC experiments on a set of 504 small-size instances, B&C proved optimality for 497 instances, while BRKGA-QL found 452 optimal solutions, totalling 90.94% of the instances solved to optimality. Finally, we present the results for a real case study for the DARP-PFCC, where BRKGA-QL solved very large problem instances containing up to 713 transportation requests. We also derive some managerial analyses to assess the effects of vehicle capacity reduction, for example due to the COVID-19 pandemic, on shared transportation. The results point to the benefits of combining the private fleet and common carriers in dial-a-ride problems, both for the provider and for the users.