dc.rights.license | Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) | spa |
dc.contributor.advisor | Coronado-Hernandez, Jairo R. | |
dc.contributor.advisor | Pornoy De la ossa, Iván Dario | |
dc.contributor.author | Calderón Ochoa, Andrés Felipe | |
dc.date.accessioned | 2022-11-28T13:19:12Z | |
dc.date.available | 2022-11-28T13:19:12Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://hdl.handle.net/11323/9640 | |
dc.description.abstract | El rendimiento operativo ha sido ampliamente estudiado en la literatura como herramienta para la administración y toma de decisiones en los supermercados. A través de la teoría de colas es posible calcular de manera cuantitativa las medidas de rendimiento operativo en estos establecimientos, con el objeto de tomar decisiones que incidan sobre la eficiencia operacional. A partir del año 2020, con el desafío global debido a la aparición de la pandemia del COVID-19 en los establecimientos comerciales de todo el mundo, incluidos los supermercados, estos se vieron forzados a ajustar la dinámica de su operación y a tomar medidas orientadas a minimizar el riesgo de infección por parte de los clientes, ajustándose a las políticas gubernamentales y de salud pública. Entre una de las medidas adoptadas se encuentra el ajuste del tamaño de aforo máximo permisible en el establecimiento, limitando así el número máximo de clientes que pueden permanecer en estos establecimientos, generando como consecuencia una reducción en sus ventas. Este trabajo presenta el modelamiento de los diferentes sistemas de colas presentes en dos tipos de supermercados: Amazon Go y un supermercado de tipo tradicional, para escenarios con y sin restricciones de aforo por COVID-19. Seguidamente el análisis comparativo de tres indicadores de rendimiento operativo: la Tasa de producción (Throughput), el tiempo de ciclo (Cicle Time) y el Trabajo en Proceso (Work In Process), a través de la implementación de algoritmos programados en el software R y el uso de redes de Jackson. Los resultados de la investigación exhiben los sistemas de colas que presentan mejor desempeño operativo bajo condición de restricción de aforo por COVID19 como sin este, así como las consideraciones especiales que manifiestan estos sistemas. | spa |
dc.description.abstract | Operational performance has been widely studied in the literature as a tool for management and decision making in supermarkets. Through queuing theory, it is possible to quantitatively calculate the measures of operational performance in these establishments, to make decisions that affect operational efficiency. Starting in 2020, with the global challenge of the pandemic due to the appearance of the COVID-19 virus, commercial establishments around the world, including supermarkets, were forced to adjust the dynamics of their operation and take measures aimed at minimizing the risk of infection to the virus by customers due to government and public health policies. One of the measures adopted is the adjustment of the maximum permissible capacity size in the establishment, thus limiting the maximum number of customers that can stay in these establishments, generating consequently a reduction in their sales. This paper presents the modeling of the different queuing systems present in two types of supermarkets: Amazon Go and a traditional supermarket, for scenarios with and without capacity restrictions due to COVID-19. Next, the comparative analysis of three operational performance indicators: throughput, cycle time and work in process, through the implementation of algorithms programmed in the R software and the use of Jackson networks. The results of the research show the queuing systems that present the best operational performance under conditions of capacity restriction by COVID-19 as well as without it, as well as the special considerations that these systems manifest. | eng |
dc.description.tableofcontents | Lista de tablas y figuras 12 -- Capítulo I 17 -- Consideraciones Generales 17 -- Introducción 17 -- Planteamiento del problema 19 -- Justificación 21 -- Objetivos 22 -- Objetivo General 22 -- Objetivos Específicos 22 -- Metodología 23 -- Alcance y limitaciones 26 -- Capítulo II 27 -- Rendimiento Operativo en sistemas de colas de Supermercados 27 -- Introducción 27 -- Clasificación de los supermercados 27 -- Teoría de colas 33 -- Clasificación de los sistemas de colas 34 -- Medidas de rendimiento 35 -- Redes de colas 35 -- Clasificación de las redes de colas 35 -- Redes de Jackson 35 -- Medidas de rendimiento 36 – Aplicaciones de la teoría de colas 38 -- Restricciones de aforo por COVID-19 39 -- Capítulo III 41 -- Modelamiento de sistemas de colas en Supermercados sin restricciones de aforo por COVID-19 41 -- Introducción 41 -- Recopilación y tabulación de datos 41 -- Descripción de los modelos de colas 42 -- Introducción 42 -- Modelo Amazon Go: (M/M/∞) 43 -- Supermercado Tradicional con múltiples colas: (M/M/∞ -> S:M/M/1) 44 -- Supermercado Tradicional con múltiples colas en paralelo: (M/M/∞ -> M/M/S) 47 -- Cálculo de los Indicadores de rendimiento operativo 49 -- Conclusiones 50 -- Capítulo IV 51 -- Modelamiento de sistemas de colas en Supermercados con restriccionesde aforo por COVID-19 51 -- Introducción 51 -- Modelos propuestos 51 -- Supermercado Amazon Go: (M/M/∞/𝑲��) 51 -- Supermercado Tradicional: (M/M/∞ -> S:M/M/1) 53 -- Supermercado Tradicional: (M/M/∞ -> M/M/S) 55 -- Cálculo de indicadores de rendimiento operativo 56 -- Supermercado Amazon Go: (M/M/∞/𝑲��) 58 -- Conclusiones 67 -- Capítulo V 68 -- Análisis de resultados de sistemas de colas en Supermercados 68 -- Introducción 68 -- Resultados obtenidos para los modelos sin restricciones de aforo por COVID-19 68 -- Resultados obtenidos para los modelos con restricciones de aforo por COVID-19 75 -- Conclusiones 94 -- Conclusiones generales y trabajos futuros 97 -- Referencias 96 -- Anexos 107 -- Anexo 1: Código programado en R para el supermercado Amazon Go conrestricciones de bioseguridad por COVID-19 108 -- Anexo 2: Código programado en R para el supermercado Tradicional con múltiplesservidores M/M/1 con restricciones de bioseguridad por COVID-19 110 -- Anexo 3: Código programado en R del supermercado Tradicional con servidores M/M/S con restricciones de bioseguridad por COVID-19 113 | spa |
dc.format.extent | 115 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | spa | spa |
dc.publisher | Corporación Universidad de la Costa | spa |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/4.0/ | spa |
dc.title | Análisis del rendimiento operativo de sistemas de colas en supermercados considerando restricción de aforo por COVID-19 | spa |
dc.type | Trabajo de grado - Maestría | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.description.degreename | Magíster en Ingeniería | spa |
dc.identifier.instname | Corporación Universidad de la Costa | spa |
dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
dc.publisher.department | Productividad e innovación | spa |
dc.publisher.place | Barranquilla, Colombia | spa |
dc.publisher.program | Maestría en Ingeniería | spa |
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dc.subject.proposal | Rendimiento operativo | spa |
dc.subject.proposal | Teoría de colas | spa |
dc.subject.proposal | Supermercados | spa |
dc.subject.proposal | Amazon-Go | eng |
dc.subject.proposal | Throughput | eng |
dc.subject.proposal | COVID-19 | zho |
dc.subject.proposal | Operational performance | eng |
dc.subject.proposal | Queueing theory | fra |
dc.subject.proposal | Supermarket | eng |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/TM | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
dc.contributor.jury | Salas Navarro, Katherinne | |
dc.contributor.jury | Cárdenas Escorcia, Yulineth | |
dc.description.degreelevel | Maestría | spa |