Otimização de sistemas de distribuição de energia elétrica radiais usando programação cônica de segunda ordem inteira mista

Pós-graduação em Engenharia Elétrica - FEIS

Reconfiguração de sistemas de distribuição de energia elétrica utilizando uma metodologia multipartida

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Resolução do problema de fluxo de potência ótimo pela meta-heurística algoritmo dos fogos de artifício de busca dinâmica com mutação de covariância

Pós-graduação em Engenharia Elétrica - FEIS

Applications of Linear Programming to Coding Theory

Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.

Parallel algorithm for solving integer linear programs

Linear programs, or LPs, are often used in optimization problems, such as improving manufacturing efficiency of maximizing the yield from limited resources. The most common method for solving LPs is the Simplex Method, which will yield a solution, if one exists, but over the real numbers. From a purely numerical standpoint, it will be an optimal solution, but quite often we desire an optimal integer solution. A linear program in which the variables are also constrained to be integers is called an integer linear program or ILP. It is the focus of this report to present a parallel algorithm for solving ILPs. We discuss a serial algorithm using a breadth-first branch-and-bound search to check the feasible solution space, and then extend it into a parallel algorithm using a client-server model. In the parallel mode, the search may not be truly breadth-first, depending on the solution time for each node in the solution tree. Our search takes advantage of pruning, often resulting in super-linear improvements in solution time. Finally, we present results from sample ILPs, describe a few modifications to enhance the algorithm and improve solution time, and offer suggestions for future work.

Setting Inventory Levels of CONWIP Flow Lines via Linear Programming

This paper treats the problem of setting the inventory level and optimizing the buffer allocation of closed-loop flow lines operating under the constant-work-in-process (CONWIP) protocol. We solve a very large but simple linear program that models an entire simulation run of a closed-loop flow line in discrete time to determine a production rate estimate of the system. This approach introduced in Helber, Schimmelpfeng, Stolletz, and Lagershausen (2011) for open flow lines with limited buffer capacities is extended to closed-loop CONWIP flow lines. Via this method, both the CONWIP level and the buffer allocation can be optimized simultaneously. The first part of a numerical study deals with the accuracy of the method. In the second part, we focus on the relationship between the CONWIP inventory level and the short-term profit. The accuracy of the method turns out to be best for such configurations that maximize production rate and/or short-term profit.

Fuzzy Multi-Objective Linear Programming and Simulation Approach to the Development of Valid and Realistic Master Production Schedule

Master production schedule (MPS) plays an important role in an integrated production planning system. It converts the strategic planning defined in a production plan into the tactical operation execution. The MPS is also known as a tool for top management to control over manufacture resources and becomes input of the downstream planning levels such as material requirement planning (MRP) and capacity requirement planning (CRP). Hence, inappropriate decision on the MPS development may lead to infeasible execution, which ultimately causes poor delivery performance. One must ensure that the proposed MPS is valid and realistic for implementation before it is released to real manufacturing system. In practice, where production environment is stochastic in nature, the development of MPS is no longer simple task. The varying processing time, random event such as machine failure is just some of the underlying causes of uncertainty that may be hardly addressed at planning stage so that in the end the valid and realistic MPS is tough to be realized. The MPS creation problem becomes even more sophisticated as decision makers try to consider multi-objectives; minimizing inventory, maximizing customer satisfaction, and maximizing resource utilization. This study attempts to propose a methodology for MPS creation which is able to deal with those obstacles. This approach takes into account uncertainty and makes trade off among conflicting multi-objectives at the same time. It incorporates fuzzy multi-objective linear programming (FMOLP) and discrete event simulation (DES) for MPS development.

Highest return farming systems for Tama and Muscatine soils: an application of linear programming to 240- and 480- acre farms operated by two men/

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Highest return farming systems for Drummer-Flanagan soils : an application of linear programming to farm planning /

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Adaptive linear classifier by linear programming,

Mode of access: Internet.

A linear programming code to determine whether a Boolean function is a threshold function,

Bibliography: p. 14.