Modelamiento y optimización de políticas de mantenimiento del escaner de tomografía computarizada con fallas estocásticas
The definition of maintenance policies in most companies is based on empirical knowledge and depending on the area or place where the equipment is located. Taking into account the budget restrictions and capacity of the equipment that public and private entities have in the administration of medical...
- Autores:
-
Cardona Ortegón, Andrés Felipe
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/948
- Acceso en línea:
- https://catalogo.escuelaing.edu.co/cgi-bin/koha/opac-detail.pl?biblionumber=21822
https://repositorio.escuelaing.edu.co/handle/001/948
- Palabra clave:
- Procesos estocástico
Modelo de optimización
Escáner de tomografía
Stochastic processes
Optimization model
Tomography scanner
- Rights
- openAccess
- License
- Derechos Reservados - Escuela Colombiana de Ingeniería Julio Garavito
| Summary: | The definition of maintenance policies in most companies is based on empirical knowledge and depending on the area or place where the equipment is located. Taking into account the budget restrictions and capacity of the equipment that public and private entities have in the administration of medical equipment, it is crucial to find an adequate path that serves as a tool for the design of maintenance policies. That’s why that the object of this research work is to propose an optimization model that allows making better decisions when preparing maintenance policies. The medical equipment studied is the computerized tomography scanner. This is a tool used in various diagnostic processes, of different specialties, as it is a non-invasive exploration of the body. The equipment is used to take images of the head, chest and extremities. A model of continuous-time markov chains is used to model the state (running, running requiring maintenance, stopped requiring corrective maintenance) of the machines over time. An optimization model is proposed whose objective function is to maximize the benefit generated by the operating equipment and requiring preventive maintenance; without exceeding the budget of the organization. Two methods are used to solve the proposed optimization model: an exhaustive search algorithm to understand the behavior of the solution surface generated by the objective function and a meta-heuristic based on gradient-ascent to find a good solution (close to the optimum) in a reasonable time. |
|---|