Otimization

Este trabalho apresenta um estudo de caso realizado na empresa Ráfia Embalagens, bem como um estudo experimental realizado no laboratório da UNISC, seu tema tem como base o
controle de qualidade na indústria de ráfia, uma padronização e otimização dos processos com foco na extrusão de ráfia, o qual é processado por uma extrusora monorosca do tipo “balão”.
Primeiramente, foi realizada uma análise da situação inicial da indústria e avaliação do problema. Constatou-se que o problema principal era a geração de resíduos no processo e
problemas de controle de qualidade do produto. Com isso, foi realizada uma coleta de dados através de planilhas de controle de processos com foco na formulação da extrusão, considerada fator principal na qualidade do produto e formação de resíduos. Com base na fórmula inicial de extrusão, foram elaboradas outras três amostras onde se acrescentou material reprocessado para verificar a viabilidade de reaproveitamento e redução no custo da indústria. As amostras foram analisadas no laboratório da UNISC. Os ensaios foram realizados no equipamento Emic DL 10000, utilizando a norma ASTM 882, que
demonstraram a viabilidade de acréscimo de material reprocessado de 1,97%, apresentando uma redução do custo em 0,986%. Para o gerenciamento de processos foram utilizadas ferramentas como o método do Grupo GAV e algumas ferramentas de gestão da qualidade. Além disso, também foi introduzido um novo macro-processo no organograma da empresa: o setor de controle de qualidade e treinamento, que tem como objetivo conduzir o controle de
qualidade e avaliação dos processos e promover treinamento dos colaboradores. Destaca-se ainda, um programa de remanufatura e apoio mútuo na cadeia produtiva com o reuso dos resíduos de produção em outros segmentos como matéria-prima.

Residential home automation is typically based on a Smart Home Controller (SHC) that controls the main loads and devices used in a home and helps reduce energy costs. This work presents a multi-objective approach to maximize energy savings and maximize comfort through a SHC with demand control in a variable rate system. The formalization of the problem was initially done through Integer Linear Programming (ILP), in which a weight aggregation function is presented for the two objectives under analysis, and finally a multi-objective meta-heuristic was implemented to treat the bi-objective, based Genetic Algorithm by means of the approximation of a Pareto frontier for automatic definition of the drive of the loads, thus offering the user options of choice, considering the criteria energy consumption and comfort. The differential of this proposal is the use of a new definition for objective function related to comfort, with the activation of loads with more than one daily operating cycle. The results of the algorithm used specify the best times to connect the loads (real household appliances) according to the constraint given by the contracted demand value and the user preferences. The efficacy of the proposed solution is demonstrated using the Weight Based Genetic Algorithm (WBGA) and Pareto Envelope-based Selection Algorithm II (PESA II) optimization techniques in a real smart home controller system in a mid-sized home. The results of the simulations demonstrated the generation of solutions that can reach an economy of more than 51% and comfort of over 96%, compared to the initial user preferences, giving the possibility of choosing the solution that best suits your needs.

– The generation of a hydroelectric power plant is a nonlinear function of its storage and turbined outflow. Nonlinear, mixed-integer or linear programming models have been proposed to approximate this function in the context of hydrothermal coordination, usually with one function for each plant. This paper proposes a new exact " Multi-plant hydro production function (MHPF) " , which depends on the storage and discharge of a given reservoir power plant and comprises not only its generation but also the power output to all subsequent run-of-the-river plants until the next reservoir. Based on this function we build a piecewise linear model that can be embedded in stochastic linear programs for optimal hydrothermal coordination. We illustrate the proposed MHPF approach for real sets of cascaded hydro plants of the Brazilian System.

The outbreak of COVID-19 has posed significant challenges to governments across the world. The increase in hazardous infectious waste (HIW) caused by the pandemic is associated with the risk of transmitting the virus. In this study, hazardous waste includes infectious waste generated both by individuals and by hospitals during the COVID-19 pandemic. To control the outbreak by maintaining social distance and home quarantine protocols, daily necessities and health supplies must be provided to the people affected. Governments play an essential role in the management of the crisis, creating an elaborate plan for collecting HIW and providing necessities and health supplies. This paper proposes a leader-follower approach for hazardous infectious waste collection and government aid distribution to control COVID-19. At the top level of the model, government policies are designed to support people by distributing daily necessities and health supplies, and to support contractors by waste collection. The lower level of the model is related to the operational decisions of contractors with limited capacities. Due to the potential risk of virus transmission via contaminated waste, the proposed model considers the complications imposed on contractors at the lower level. Applying a stochastic programming approach, four possible scenarios are examined, dependent of the severity of the outbreak. As a solution approach, the Benders decomposition method is combined with Karush-Kuhn-Tucker conditions. The results show that government support, in addition to much better management of citizen demand, can control the spread of the virus by implementing quarantine decisions.

The simple algorithm proposed in this paper belongs to the category of unidimensional search by elimination, and may be used, therefore, in the optimization of discontinuous functions. This new method is based on the dichotomous search algorithm and is, in many cases, superior to Fibonacci's algorithm (up to the present considered the most efficient method of elimination), with the advantage of being much simpler.