Examinando por Autor "Rojas, Fernando"

Mostrando 1 - 5 de 5
  • Miniatura
    Ítem
    Desarrollo y evaluación de experiencias didácticas para un aprendizaje significativo
    (Universidad de Valparaíso. Unidad de Gestión Curricular y Desarrollo Docente, 2025) Garrido-Araya, Dominique; Lara Yergues, Eduardo; Salum-Alvarado, Elena; Chahuán-Jiménez, Karime; Hurtado Arenas, Paulina; Fernández Bernal, Soledad; Vidal Ortega, Johana; Ugarte Vera, Macarena; Johnson Castro, María Inés; Guerra Cárdenas, Natalia; Ruiz Tagle, Carolina; González Nahuelquin, Cibeles; Herrera, María de las Mercedes; Godoy, Nadia; Espinoza, Nicolle; Báez, Pamela; González Gárate, Paola; Rodríguez Sepúlveda, Sandra; Vargas Donoso, Carlos; González Véliz, Carolina; Reyes Payacán, Loreto; Soto Droguett, Pamela; Báez, Pamela; Marchant, Guillermo; Galindo, Leopoldo; Miranda, Ramón; Sandoval, Sergio; Zumarán, Francisco; Aravena, Marco; Aguirre, Macarena; Pino, Gloria; Villagra, Jorge; Pérez, Natalia; San Martín, Sebastián; Rivera, Valentina; Zumarán, Francisco; Cueto Galdames, Betzabé; Espinoza Soto, Eduardo; Flores, Pía; Vallejos, Carolina; Arellano, Cristian; Castro Pérez, Cynthia; Báez, José; Leal, Pablo; Gigoux, Juan Pablo; Jofré, Pamela; Oyanedel, Rebecca; Valenzuela, Rodrigo; Vergara, Rodrigo; Flores, Pía; Sandoval, Cristian; Bernal, Andrés; Villegas, Emanuel; Piña, Marlene; Vergara, Carlos; González, Catalina; Rojas, Fernando; Escobar, Marcela; Beyer, María Paz; Bastias, Rossana; Sepúlveda, Silvia; Toro Coddou, Lía; Rodríguez Espinoza, Verónica; Hernández, Diego; Araya, Griselda; Torrijos, Jackeline; Moya, Yanneth; Rau Parot, María Paz; Ríos Binimelis, Carmen Gloria; Calderón, Alejandra; Moller, Alejandra; Córdova, Claudio; López, Daniela; Leiva, Elizabeth; Varas, Juan Francisco; Herrera Peñaloza, Paulo
  • Miniatura
    Ítem
    A forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosis
    (PeerJ Publishing, 2020) Rojas, Fernando; Ibacache-Quiroga, Claudia
    Background. This work presents a forecast model for non-typhoidal salmonellosis outbreaks. Method. This forecast model is based on fitted values of multivariate regression time series that consider diagnosis and estimation of different parameters, through a very flexible statistical treatment called generalized auto-regressive and moving average models (GSARIMA). Results. The forecast model was validated by analyzing the cases of Salmonella enterica serovar Enteritidis in Sydney Australia (2014–2016), the environmental conditions and the consumption of high-risk food as predictive variables. Conclusions. The prediction of cases of Salmonella enterica serovar Enteritidis infections are included in a forecast model based on fitted values of time series modeled by GSARIMA, for an early alert of future outbreaks caused by this pathogen, and associated to high-risk food. In this context, the decision makers in the epidemiology field can led to preventive actions using the proposed model.
  • Miniatura
    Ítem
    A joint replenishment supply model for multi-products grouped by several variables with random and time dependence demand
    (Emerald Publishing, 2020) Rojas, Fernando
    Purpose – This paper aims to propose a supply model of periodic review with joint replenishment for multiproducts grouped by several variables with random and time dependence demand. Design/methodology/approach – The products are grouped by multivariate cluster analysis. The stochastic inventory model describes the random demand of each product, considering the temporal dependency through a generalized autoregressive moving average model. Stochastic programming for the total cost of inventory is obtained considering the expected value of the demand per unit of time. Findings – The total costs for the products grouped with the proposed model are 6% lower than for the individual inventory policy. The expected shortage units decrease significantly in the proposed grouped model with temporary dependence. In addition, the proposal with temporary dependency has lower costs than when the independent and identically distributed demand is considered. Originality/value – The proposed policy is exemplified with real-world data from a Chilean hospital, where the products (drugs) are segmented by grouping variables, forming clusters of drugs with homogeneous behavior within the groups and heterogeneous behavior between groups.
  • Miniatura
    Ítem
    Managing slow-moving item: a zero- inflated truncated normal approach for modeling demand
    (PeerJ Publishing, 2020) Rojas, Fernando; Wanke, Peter; Coluccio, Giuliani; Vega-Vargas, Juan; Huerta-Canepa, Gonzalo F.
    This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zero-inflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages.Weevaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston's method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zeroinflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.
  • Miniatura
    Ítem
    A Methodology for Data-Driven Decision-Making in the Monitoring of Particulate Matter Environmental Contamination in Santiago of Chile
    (Springer International Publishing, 2020) Cavieres, María Fernanda; Leiva, Víctor; Marchant, Carolina; Rojas, Fernando
    Atmospheric pollution derives mainly from anthropogenic activities that use combustion and may lead to adverse effects in exposed populations. It is generally accepted that air contamination causes cardiovascular and pulmonary morbidity in addition to increased mortality after exposure, but other epidemiological associations have also been described, including cancer as well as reproductive and immunological toxicity. Thus the concentration of chemicals in the air must be controlled. We propose that monitoring of air quality may be achieved by employing data analytics to generate information within the context of data-driven decision making to prevent and/or adequately alert the population about possible critical episodes of air contamination. In this paper, we propose a methodology for monitoring particulate matter pollution in Santiago of Chile which is based on bivariate control charts with heavy-tailed asymmetric distributions. This methodology is useful for monitoring environmental risk when the particulate matter concentrations follow bivariate Birnbaum-Saunders or Birnbaum-Saunders-t-Student distributions. A case study with real particulate matter pollution from Santiago is provided, which shows that the methodology is suitable to alert early episodes of extreme air pollution. The results are in agreement with the critical episodes reported with the current model used by the Chilean health authority.