It is important to evaluate the application of new technologies in the field of computational science to forest science. The goal of this study was to test a different kind of metaheuristic, namely Clonal Selection Algorithm, in a forest planning problem. In this problem, the total management area is 4.210 ha that is distributed in 120 stands in ages between 1 and 6 years and site indexes of 22 m to 31 m. The problem was modeled considering the maximization of the net present value subject to the constraints: annual harvested volume between 140,000 m³ and 160,000 m³, harvest ages equal to 5, 6 or 7 years, and the impossibility of division of the management unity at harvest time. Different settings for Clonal Selection Algorithm were evaluated to include: varying selection, cloning, hypermutation, and replacement rates beyond the size of the initial population. A generation value equal to 100 was considered as a stopping criteria and 30 repetitions were performed for each setting. The results were compared to those obtained from integer linear programming and linear programming. The integer linear programming, considered to be the best solution, was obtained after 1 hour of processing. The best setting for Clonal Selection Algorithm was 80 individuals in the initial population and selection. Cloning, hypermutation, and replacement rates equal to 0.20, 0.80, 0.20 and 0.50, respectively, were found. The results obtained by Clonal Selection Algorithm were 1.69% better than the integer linear programming and 4.35% worse than the linear programming. It is possible to conclude that the presented metaheuristic can be used in the resolution of forest scheduling problems.

The planning of forest production requires the adoption of mathematical models to optimize the utilization of available resources. Hence, studies involving the improvement of decision-making processes must be performed. Herein, we evaluate an alternative method for improving the performance of metaheuristics when they are applied for identifying solutions to problems in forest production planning. The inclusion of a solution obtained by rounding the optimal solution of linear programming to a relaxed problem is investigated. Such a solution is included in the initial population of the clonal selection algorithm, genetic algorithm, simulated annealing, and variable neighborhood search metaheuristics when it is used to generate harvest and planting plans in an area measuring 4,210 ha comprising 120 management units with ages varying between 1 and 6 years. The same algorithms are executed without including the solutions mentioned in the initial population. Results show that the performance of the clonal selection algorithm, genetic algorithm, and variable neighborhood search algorithms improved significantly. Positive effects on the performance of the simulated annealing metaheuristic are not indicated. Hence, it is concluded that rounding off the solution to a relaxed problem is a good alternative for generating an initial solution for metaheuristics.

Objetivo: Elaborar e validar instrumento de consulta de enfermagem à pessoa com pé diabético na atenção primária à saúde, fundamentado no modelo teórico das Necessidades Humanas Básicas de Wanda de Aguiar Horta.

Objetivo: Elaborar y validar un instrumento de consulta de enfermería para personas con pie diabético en la atención primaria de salud, basado en el modelo teórico de las Necesidades Humanas Básicas de Wanda de Aguiar Horta.

Composite indicators are mathematical tools that assist sociologists, geographers, environmentalists, and urban planners in understanding and analyzing complex phenomena. Composite indicators also attract significant interest among researchers in operations research, who seek to improve and develop new methods for their construction. This study aligns with this latter line of research by proposing a new method for constructing composite indicators. The so-called Subjective Benefit-of-the-Doubt (S-BoD) was developed to address the uncertainty about the conceptual importance of sub-indicators in the data-driven definition of their respective weights. This logic ensures the weights adapt to the maturity of the theoretical-conceptual framework for the multidimensional phenomenon. On the one hand, absolute consensus among experts signals a mature theoretical-conceptual framework that prevents the BoD from modifying the weights. On the other hand, absolute disagreement among experts signals the presence of incipient theoretical-conceptual frameworks, which allow for full weight fluctuations, corresponding to the operationalization of the classic BoD algorithm. The results of applying the method show that the S-BoD outperforms BoD in multidimensional governance by better distinguishing units, extracting information, explaining GDP per capita, and generating scores without outliers, resulting in a composite governance indicator with greater discriminatory, informational, and explanatory power. Simulations confirm this superiority, except for the proportion of outliers, which is specific to governance data.

O Aumento crescente da população mundial e constante processo de urbanização vem provocando o aumento da produção das grandes unidades industriais e o consumo de bens no mercado, promovendo aumento da geração da quantidade de Resíduos sólidos das cidades nos últimos anos. Em consequência deste fenômeno descrito anteriormente, a quantidade de pontos para a coleta dos Resíduos Sólidos Domiciliares aumenta, promovendo a geração de custos e distâncias maiores, consequentemente com impactos no transito urbano e diminuição da eficácia e qualidade dos sistemas logísticos e de transporte das empresas que operam os serviços de limpeza. Como método de suporte aos processos de roteirização para a coleta dos resíduos, utilizou-se o SIG-T software TransCAD versão 4.5. com a aplicação da rotina do Problema de Roteirização de Veículos para o planejamento, execução e análise dos roteiros dos Caminhões nos serviços de coleta de limpeza Urbana – SSU- Secretaria de Serviços urbanos na cidade de Montes Claros.