In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.
The classic Traveling Salesman Problem (TSP) only considers the costs involved in the routes and does not differentiate products or customers. Logistic companies face conflict between operational costs, customers with different categories of products, and customer satisfaction, which is directly related to delivery time. This paper presents a new mathematical model for a TSP with variable costs and priority prizes, taking into account the customer's product and preference values. This problem is denoted as the Multicommodity Traveling Salesman Problem with Priority Prizes (MTSPPP). Two versions of the Biased Random-Key Genetic Algorithm (BRKGA) are proposed to solve medium and large instances of the MTSPPP. Computational tests were performed, using modified instances based on classical TSP instances. The proposed methods have proved to be efficient in solving the MTSPPP.
Resumo. Neste trabalhoé discutido o problema integrado de dimensionamento e de sequenciamento de lotes utilizando um modelo matemático que exclui subsequências desconexas através de restrições de fluxo multicommodity. Propomos limites primais a partir de uma heurística gulosa e limites duais baseados na relaxação Lagrangeana e na relaxação Lagrangeana/Surrogate. Os problemas duais associados são resolvidos usando o Algoritmo de Subgradiente e o Algoritmo de Volume. O método que obteve melhor desempenho foi o dual lagrangeano resolvido pelo Algoritmo do Subgradiente.Palavras-chave. dimensionamento, sequenciamento, relaxação Lagrangeana
IntroduçãoAs decisões do planejamento da produção no setor de manufatura envolvem, entre outros, determinar o uso mais eficiente dos recursos para atender a demanda dos itens requeridos pelos clientes. Estas decisões são tomadas geralmente em duas etapas: uma etapa de dimensionamento dos lotes e outra de sequenciamento dos lotes [2]. Na etapa de dimensionamento deseja-se determinar quanto de cada item produzir (tamanho dos lotes) a fim de atender as demandas pré-especificadas, sob as condições e capacidades operacionais existentes. Na etapa de sequenciamento deseja-se ordenar a produção dos lotes [15]. Se essas decisões são tomadas de forma independente, podem ocorrer infactibilidades ou um menor aproveitamento da capacidade de produção, daí o interesse em integrar essas duas decisões. O Problema Integrado de Dimensionamento e Sequenciamento de lotes (PIDS) consiste em determinar quanto de cada item produzir e em que ordem, de modo a atender as demandas previstas com o objetivo de reduzir custos e tempos de produção, ou seja, determinar simultaneamente a quantidade a ser produzida em cada máquina, em cada 1
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.