The use of discrete event simulation optimisation methods is a tool commonly used as a decisionmaking support system in industrial problems, concerning management and resource allocation in order to maximise a set of values regarding costs, revenues and other enterprise interests. The present study has proposed and tested an optimisation algorithm developed on Python, with different wall clock time reduction strategies including parallelism, the Greedy Randomized Adaptive Search Procedure (GRASP) population-based metaheuristic, and ten machine learning methods. With the selected best machine learning method (Decision Trees Regressor) 6 optimisation scenarios were generated and then applied to an economic lot-size problem for a theoretical shop floor. The results showed improvements in the reduction of the processing time of 95.0 % comparing the serial GRASP with the parallel machine learning GRASP, obtaining a solution of 94.0 % of the best local optimum.
Background Many management tools, such as Discrete Event Simulation (DES) and Lean Healthcare, are efficient to support and assist health care quality. In this sense, the study aims at using Lean Thinking (LT) principles combined with DES to plan a Canadian emergency department (ED) expansion and at meeting the demand that comes from small care centers closed. The project‘s purpose is reducing the patients’ Length of Stay (LOS) in the ED. Additionally, they must be assisted as soon as possible after the triage process. Furthermore, the study aims at determining the ideal number of beds in the Short Stay Unit (SSU). The patients must not wait more than 180 min to be transferred. Methods For this purpose, the hospital decision-makers have suggested planning the expansion, and it was carried out by the simulation and modeling method. The emergency department was simulated by the software FlexSim Healthcare®, and, with the Design of Experiments (DoE), the optimal number of beds, seats, and resources for each shift was determined. Data collection and modeling were executed based on historical data (patients’ arrival) and from some databases that are in use by the hospital, from April 1st, 2017 to March 31st, 2018. The experiments were carried out by running 30 replicates for each scenario. Results The results show that the emergency department cannot meet expected demand in the initial planning scenario. Only 17.2% of the patients were completed treated, and LOS was 2213.7 (average), with a confidence interval of (2131.8–2295.6) min. However, after changing decision variables and applying LT techniques, the treated patients’ number increased to 95.7% (approximately 600%). Average LOS decreased to 461.2, with a confidence interval of (453.7–468.7) min, about 79.0%. The time to be attended after the triage decrease from 404.3 min to 20.8 (19.8–21.8) min, around 95.0%, while the time to be transferred from bed to the SSU decreased by 60.0%. Moreover, the ED reduced human resources downtime, according to Lean Thinking principles.
Objective: Evaluate the effect of essential oil in odor reduction for intestinal ostomy bags. Method: Primary study, semi-experimental, prospective clinical trial with quantitative approach. A product prepared with Melaleuca armillaris leaves was tested in ostomy pouches, with and without effluents, for adhesion and odor control. Instrument: Labeled Magnitude Scale. Results: Colostomized participants, with a mean age of 73 ± 14.94, predominantly males; and informal caregivers, with a mean age of 44 ± 8.98, predominantly females. In the visual evaluation of the oil, 100% of the participants perceived its adherence. Regarding the evaluation of effluent odor by the colostomized, five reported “strong odor” before oil use and, six reported “weak” odor after use (p = 0.005). Five informal caregivers reported “very strong” effluent odor before oil use; and one reported “weak” and nine reported “moderate” after use (p = 0.0025). Conclusion: There was a reduction in the odor of effluents in the ostomy pouches with the essential oil of Melaleuca armillaris at 10%. Application for patent registration in the Brazilian National Institute of Industrial Property (INPI) under no. BR 10 2020 026987 9.
O principal objetivo do presente artigo é a aplicação da ferramenta gerencial de engenharia value stream mapping (vsm) e a elaboração de um mapa de future state em uma lavanderia hospitalar, buscando uma melhor utilização dos recursos existentes e uma redução na complexidade do fluxo processual, visto que este setor pode ser comparado a um setor industrial por apresentar entradas e saídas específicas e ciclos processuais padronizados. A metodologia utilizada para o desenvolvimento do presente trabalho foi de natureza aplicada, os objetivos foram definidos como descritivos e a abordagem foi qualitativa, por meio do método de estudo de caso, sendo o cenário estudado no presente artigo o setor de lavanderia de um hospital regional do sul de minas gerais. O cenário atual do setor foi mapeado por meio do vsm e esperou-se que a aplicação do mapeamento da cadeia de valor (vsm) trouxesse benefícios analíticos sobre questões processuais, e que o uso dessa técnica ajudasse a identificar desperdícios, perdas processuais e a levantar sugestões de melhorias, as quais possibilitam uma tomada de decisão muito mais efetiva e direcionada às metas buscadas. Os objetivos da aplicação do vsm foram alcançados no presente estudo. Palavras-chave: lavanderia hospitalar; value stream mapping; mapa de estado futuro.
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