971 resultados para mathematical programming


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The use of QoS parameters to evaluate the quality of service in a mesh network is essential mainly when providing multimedia services. This paper proposes an algorithm for planning wireless mesh networks in order to satisfy some QoS parameters, given a set of test points (TPs) and potential access points (APs). Examples of QoS parameters include: probability of packet loss and mean delay in responding to a request. The proposed algorithm uses a Mathematical Programming model to determine an adequate topology for the network and Monte Carlo simulation to verify whether the QoS parameters are being satisfied. The results obtained show that the proposed algorithm is able to find satisfactory solutions.

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The present paper proposes a new hybrid multi-population genetic algorithm (HMPGA) as an approach to solve the multi-level capacitated lot sizing problem with backlogging. This method combines a multi-population based metaheuristic using fix-and-optimize heuristic and mathematical programming techniques. A total of four test sets from the MULTILSB (Multi-Item Lot-Sizing with Backlogging) library are solved and the results are compared with those reached by two other methods recently published. The results have shown that HMPGA had a better performance for most of the test sets solved, specially when longer computing time is given. © 2012 Elsevier Ltd.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Engenharia Elétrica - FEIS

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Pós-graduação em Engenharia Elétrica - FEIS

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Pós-graduação em Engenharia Elétrica - FEIS

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Pós-graduação em Engenharia Elétrica - FEIS

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Pós-graduação em Engenharia Elétrica - FEIS

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Pós-graduação em Engenharia Elétrica - FEIS

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This paper presents a study to determine the optimal support positions in bridges, as well as the optimal relation between the spans of beams, aiming both the reduction and the unifirmization of the efforts in these structures. The mathematical problem is formulated in order to minimize the maximum bending moments, taking the coordinate of each internal support as design variable. To take into account possible location limitations, size constraints are also considered in the formulation. The software developed based on this study determines the efforts in each support for load shifting along the structure by the usage of the Displacement Method, and the optimization is performed with a mathematical programming technique. The structures analyzed from the computational implementation of the idealized model point to the validation and efficiency of the proposed procedure.

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This work aimed to develop an optimization methodology for reservoir sizing in rainwater harvesting systems in order to increase the economic viability of projects in this area. For this, concepts of Operations Research were used so as to develop mathematical programming problems related to minimizing the life cycle cost and maximizing efficiency. The results obtained for different sizing methods were presented based on a case study, emphasizing the importance of tools that are able to provide a more accurate analysis and tend to significantly increase the economic viability of rainwater harvesting systems.

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This article evaluates the efficiency of Brazil's industrial sectors from 1996 to 2009, taking into account energy consumption and respective contributions to the country's economic and social aspects. This analysis used a mathematical programming method called Data Envelopment Analysis (DEA), which enabled, from the SBM model and the window analysis, to evaluate the ability of industries to reduce energy consumption and fossil-fuel CO2 emissions (inputs), as well as to increase the Gross Domestic Product (GDP) by sectors, the persons employed and personnel expenses (outputs). The results of this study indicated that the Textile sector is the most efficient industrial sector in Brazil, according to the variables used, followed by these sectors: Foods and Beverages, Chemical, Mining, Paper and Pulp, Nonmetallic and Metallurgical.

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In this work we introduce a relaxed version of the constant positive linear dependence constraint qualification (CPLD) that we call RCPLD. This development is inspired by a recent generalization of the constant rank constraint qualification by Minchenko and Stakhovski that was called RCRCQ. We show that RCPLD is enough to ensure the convergence of an augmented Lagrangian algorithm and that it asserts the validity of an error bound. We also provide proofs and counter-examples that show the relations of RCRCQ and RCPLD with other known constraint qualifications. In particular, RCPLD is strictly weaker than CPLD and RCRCQ, while still stronger than Abadie's constraint qualification. We also verify that the second order necessary optimality condition holds under RCRCQ.