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A Mathematical Model to Locate Multi-Level Multi-Service Health Facility Under Uncertainty

Motallebi Nasrabadi, Alireza | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 47645 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Najafi, Mehdi
  7. Abstract:
  8. In this study, a mathematical model for health-care facility location in two level and multi-services has been described. The facilities has two levels of clinic and hospital that has inclusive hierarchy property. In clinics, only outpatient services delivered. But, in hospitals in addition to handle outpatient services, inpatient services and emergency services are provided. In this research, we practice on queuing theory in order to consider the serious uncertainties in the health service, for instance, random demand and random service time, and by the help of which the criteria for considering the service level is calculated. Then by using applicable change variable and service level functions property, service constraints are converted to maximum permissive entrance rate, therefore nonlinear model turn out to be linear model. On the other hands, population in region are changing over time. Therefore, for considering this long-term uncertainty, Robust model than the mean random demand for services is presented. According to the real word, assumes that that demand for multiple services concern with and correlate with each other and changes in accordance with changes in their population. For that reason, the generated model in relation to future changes and uncertainty are stable and robust. Also because of the increasing difficulty of computational models in order to linearization of service level constraints, number of binary variables increased and as a result computational difficulty increased. Therefore, we presented an approximated for the model according to the properties of maximum permissive entrance rate functions, then by using this approximate, the approximated of the upper and lower bounds obtained for the problem. Considering that problem described in this thesis is NP-hard, we described the solution method based on genetic algorithm. Eventually by using a set of problems, logical behavior of robust and non-robust model, performance of approximate model and solution efficiency has been demonstrated
  9. Keywords:
  10. Uncertainty ; Genetic Algorithm ; Health Care System ; Queuing Theory ; Robust Optimization ; Hierarchical Facility Location

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