ABSTRACT One of the strategies used to optimize production processes is to define the best layout. For this, the relative positioning of the various equipment, areas, or functional activities inside the company is studied. Proper arrangement of facilities will result in shorter process times and higher productivity. In general, the objective function of the facility layout problem (FLP) is to reduce the total material handling cost. Although over six decades have been passed since the first work on FLP modeling was published, research on many aspects of this problem is still in an early stage and needs to be further explored, which motivated this study. In this paper, the unequal area of rectangular blocks with fixed dimensions and input/output points are considered for FLPs. Four new mixed-integer programming (MIP) models based on previous research formulations are developed. Then, a mathematical optimization approach based on the linearization of the models is applied. An algorithm that solves the linearized MIP model by CPLEX setting a time limit for the solution obtained excellent results for different test problems when compared to those reported in the literature.

ABSTRACT Maritime shipping is vital to worldwide commerce. Due to the high flow in ports throughout the world, the efficient allocation of vessels in berths has become a problem. A new mathematical model and several algorithms are proposed in this paper to planning the allocation of the vessels in berths and the allocation of resources to the service of each vessel. Those resources, in general, are machines to load or unload vessels. The mathematical model was implemented on Cplex and can solve small scale instances, due to its high complexity. To solve larger instances, a genetic algorithm-based metaheuristic, a first-in first-out heuristic, and a machine allocation algorithm are also proposed in this paper. The model and the algorithms produce very useful and interesting results. Comparing, the results produced by the GA are, on average, 94% better than the results of the Cplex and 26% better than the results of FIFO.

In view of the need to manage and forecast the number of beds in the Intensive Care Units for critically ill patients in Covid-19, the Forecast UTI was developed: an open access application that allows the monitoring of hospital indicators based on historical data from the service health and the temporal dynamics of the epidemic. It is also possible to make short-term forecasts of the number of beds occupied daily by the disease and to establish possible care scenarios. This article presents the functions, mode of access and examples of use of Forecast UTI, a computational tool capable of assisting managers of public and private hospitals in the Unified Health System, since they support decision-making quickly, strategically and efficiently.

Frente à necessidade de gerenciamento e previsão do número de leitos de unidades de terapia intensiva (UTI) para pacientes graves de covid-19, foi desenvolvido o Forecast UTI, um aplicativo de livre acesso e que permite o monitoramento de indicadores hospitalares com base em dados históricos do serviço de saúde e na dinâmica temporal dessa epidemia por coronavírus. O Forecast UTI também possibilita realizar previsões de curto prazo sobre número de leitos ocupados pela doença diariamente, e estabelecer possíveis cenários de atendimento. Este artigo apresenta as funções, modo de acesso e exemplos de uso do Forecast UTI, uma ferramenta computacional destinada a auxiliar gestores de hospitais da rede pública e privada do Sistema Único de Saúde no subsídio à tomada de decisão, de forma rápida, estratégica e eficiente. 

Resumo Frente à necessidade de gerenciamento e previsão do número de leitos de unidades de terapia intensiva (UTIs) para pacientes graves de COVID-19, foi desenvolvido o Forecast UTI, um aplicativo de livre acesso, que permite o monitoramento de indicadores hospitalares com base em dados históricos do serviço de saúde e na dinâmica temporal da epidemia por coronavírus. O Forecast UTI também possibilita realizar previsões de curto prazo do número de leitos ocupados pela doença diariamente, e estabelecer possíveis cenários de atendimento. Este artigo apresenta as funções, modo de acesso e exemplos de uso do Forecast UTI, uma ferramenta computacional destinada a auxiliar gestores de hospitais da rede pública e privada do Sistema Único de Saúde (SUS) no subsídio à tomada de decisão, de forma rápida, estratégica e eficiente.

Abstract In view of the need to manage and forecast the number of Intensive Care Unit (ICU) beds for critically ill COVID-19 patients, the Forecast UTI open access application was developed to enable hospital indicator monitoring based on past health data and the temporal dynamics of the Coronavirus epidemic. Forecast UTI also enables short-term forecasts of the number of beds occupied daily by COVID-19 patients and possible care scenarios to be established. This article presents the functions, mode of access and examples of uses of Forecast UTI, a computational tool intended to assist managers of public and private hospitals within the Brazilian National Health System by supporting quick, strategic and efficient decision-making.

Resumen En vista de la necesidad de administrar y prever el número de camas en la Unidad de Cuidados Intensivos para pacientes graves de COVID-19, se desarrolló Forecast UTI: una aplicación de acceso abierto que permite el monitoreo de indicadores hospitalarios basados en datos históricos del servicio salud y la dinámica temporal de esta epidemia por coronavirus También es posible hacer pronósticos a corto plazo del número de camas ocupadas diariamente por la enfermedad y establecer posibles escenarios de atención. Este artículo presenta las funciones, el modo de acceso y ejemplos de uso de Forecast UTI, una herramienta computacional capaz de ayudar a los gestores de hospitales públicos y privados en el Sistema Único de Salud, ya que apoyan la toma de decisiones de manera rápida, estratégica y eficiente.

Resumo Frente à necessidade de gerenciamento e previsão do número de leitos de unidades de terapia intensiva (UTIs) para pacientes graves de COVID-19, foi desenvolvido o Forecast UTI, um aplicativo de livre acesso, que permite o monitoramento de indicadores hospitalares com base em dados históricos do serviço de saúde e na dinâmica temporal da epidemia por coronavírus. O Forecast UTI também possibilita realizar previsões de curto prazo do número de leitos ocupados pela doença diariamente, e estabelecer possíveis cenários de atendimento. Este artigo apresenta as funções, modo de acesso e exemplos de uso do Forecast UTI, uma ferramenta computacional destinada a auxiliar gestores de hospitais da rede pública e privada do Sistema Único de Saúde (SUS) no subsídio à tomada de decisão, de forma rápida, estratégica e eficiente.

Abstract In view of the need to manage and forecast the number of Intensive Care Unit (ICU) beds for critically ill COVID-19 patients, the Forecast UTI open access application was developed to enable hospital indicator monitoring based on past health data and the temporal dynamics of the Coronavirus epidemic. Forecast UTI also enables short-term forecasts of the number of beds occupied daily by COVID-19 patients and possible care scenarios to be established. This article presents the functions, mode of access and examples of uses of Forecast UTI, a computational tool intended to assist managers of public and private hospitals within the Brazilian National Health System by supporting quick, strategic and efficient decision-making.

Resumen En vista de la necesidad de administrar y prever el número de camas en la Unidad de Cuidados Intensivos para pacientes graves de COVID-19, se desarrolló Forecast UTI: una aplicación de acceso abierto que permite el monitoreo de indicadores hospitalarios basados en datos históricos del servicio salud y la dinámica temporal de esta epidemia por coronavirus También es posible hacer pronósticos a corto plazo del número de camas ocupadas diariamente por la enfermedad y establecer posibles escenarios de atención. Este artículo presenta las funciones, el modo de acceso y ejemplos de uso de Forecast UTI, una herramienta computacional capaz de ayudar a los gestores de hospitales públicos y privados en el Sistema Único de Salud, ya que apoyan la toma de decisiones de manera rápida, estratégica y eficiente.

ABSTRACT In this work we propose a heuristic algorithm for the layout optimization for disks installed in a rotating circular container. This is a unequal circle packing problem with additional balance constraints. It proved to be an NP-hard problem, which justifies heuristics methods for its resolution in larger instances. The main feature of our heuristic is based on the selection of the next circle to be placed inside the container according to the position of the system's center of mass. Our approach has been tested on a series of instances up to 55 circles and compared with the literature. Computational results show good performance in terms of solution quality and computational time for the proposed algorithm.

Neste trabalho desenvolvemos um algoritmo genético simbiótico com objetivo de minimizar o número de objetos processados e o setup num problema de corte unidimensional. Nosso algoritmo genético gera seus próprios padrões em conjunto com soluções para o problema, através de um processo simbiótico entre duas populações distintas, a de soluções e a de padrões. Trabalhando com os dois objetivos na função de aptidão e com a relação simbiótica entre as duas populações, obtivemos resultados competitivos em relação aos métodos descritos na literatura.

This work presents a genetic symbiotic algorithm to minimize the number of objects and the setup in a one-dimensional cutting stock problem. The algorithm implemented can generate combinations of ordered lengths of stock (the cutting pattern) and, at the same time, the frequency of the cutting patterns, through a symbiotic process between two distinct populations, solutions and cutting patterns. Working with two objectives in the fitness function and with a symbiotic relationship between the two populations, we obtained positive results when compared with other methods described in the literature.

This study presents a new mathematical model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic to solve the ordered cutting stock problem. The ordered cutting stock problem was recently introduced in literature. It is appropriate to minimize the raw material used by industries that deal with reduced product inventories, such as industries that use the just-in-time basis for their production. In such cases, classic models for solving the cutting stock problem are useless. Results obtained from computational experiments for a set of random instances demonstrate that the proposed method can be applied to large industries that process cuts on their production lines and do not stock their products.

Este estudio presenta un nuevo modelo matemático y un procedimiento meta-heurístico de búsqueda voraz adaptativa y aleatoria (GRASP, por sus siglas en inglés) para resolver el problema de stock de corte ordenado. Éste problema ha sido introducido recientemente en la literatura. Es apropiado minimizar la materia prima usada por las industrias que manipulan inventarios reducidos de productos, tales como las industrias que usan la base justo a tiempo para su producción. En tales casos, los modelos clásicos para resolver el problema de stock de corte ordenado son inútiles. Los resultados obtenidos, mediante experimentos computacionales para un conjunto de ejemplos aleatorios, demuestran que el método propuesto puede ser aplicado a industrias grandes que procesan cortes en sus líneas de producción y no mantienen en stock sus productos.

In this article we solve a nonlinear cutting stock problem which represents a cutting stock problem that considers the minimization of, both, the number of objects used and setup. We use a linearization of the nonlinear objective function to make possible the generation of good columns with the Gilmore and Gomory procedure. Each time a new column is added to the problem, we solve the original nonlinear problem by an Augmented Lagrangian method. This process is repeated until no more profitable columns is generated by Gilmore and Gomory technique. Finally, we apply a simple heuristic to obtain an integral solution for the original nonlinear integer problem.