Examinando por Autor "Restrepo Sierra, Mateo"

Mostrando 1 - 2 de 2
  • Miniatura
    Ítem
    Agent-Based Model for Studying Diabetes under the Influence of Relationships
    (Universidad Eafit, 2021-04-12) Plazas Escudero, David; Cárdenas-Rodríguez, Juan Sebastian; Restrepo Sierra, Mateo; Universidad Eafit, School of Sciences, Department of Mathematical Sciences
    Diabetes is a disease which affects levels of blood insulin and recently has turned into a public health threat. This disease presents a huge risk for the individual, as it can reduce life expectancy. Furthermore, many models of diabetes just focus on biological or eating habits, without taking into account social or cultural factors. In this work, an agent-based model is proposed for the diabetic population taking into account interpersonal relationships and body mass index. The model is implemented in NetLogo and validated by results under extreme conditions. Then, it is simulated to test the influence that marriage has on the diabetic population. Finally, a sensitivity analysis is made
  • Miniatura
    Ítem
    Improving customer waiting time for medicine-retrieval center
    (Universidad Eafit, 2021-04-12) Plazas Escudero, David; Cárdenas-Rodríguez, Juan Sebastian; Restrepo Sierra, Mateo; Universidad Eafit, School of Sciences, Department of Mathematical Sciences
    Neuromedica is a Colombian pharmacy which provides treatment for people with neurological diseases. Recently, Neuromedica started attending patients from other pharmacy which led to a significant increase in the waiting time. In this pharmacy, people are classified and attended due to certain priorities. The data, given by Neuromedica, is analyzed using boxplots, Kruskal-Wallis and Kolmogorov-Smirnov tests with Python’s library Scipy. The objective of this work is to determine the number of assistants and queue logistic such that the waiting time has a significant reduction, with the purpose to provide a satisfactory level of service. A discrete-event simulation model was created and implemented in Python. A heuristic approach to minimize the waiting time is used. Additionally, a sensitivity analysis is made on the assumed distributions.