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Multi-service Healthcare Network Design Assuming Patients' Choice Behavior and Their Transfers Between Services

Radman, Maryam | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 47489 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Eshghi, Koorosh
  7. Abstract:
  8. Efficient location of health centers is an essential decesion in healthcare strategic planning. Nowadays constructing multi-service health centers is highly paid attention because of multiple nature of most diseases. Concentrating patients' medical services in one center prevents them from traveling to different parts of cities, especially in mega cities. In this research we have developed a mathematical model for the location of multi-service health centers assuming probabilistic demand and service time. Medical services are divided in three groups: normal, emergency and preventive. Since patients may be referred to another service after recieving a service by doctors' order, health system is modeled as Jackson queue networks. Also in every demand area, patients are grouped in two sections based on their income and their utility to get services of health centers is measured by three factors: travel time, variety of services in every center and type of center (private or public). The goal of the proposed mixed integer non-linear programming model is to minimize patients' travel time between demand areas and medical centers, between health centers and sum of patients' entrance rate more than the standard rate for services. Because the proposed model is NP-hard, we have designed several heuristics to solve the large problems. In addition, to compare the proposed solution methods, we have checked 85 examples. The results show that the greedy-type heuristic based on remove-add-exchange procedures acts better than the GA-based heuristic. In addition in order to investigate the impact of patients' behavior on the network design, we have assumed five different senarios on their choice behavior. Using this model, we can predict patients' choice pattern and their arrival rate at current or newly-provided medical services and so we can provide suitable services in well-located health centers with appropriate number of servers
  9. Keywords:
  10. Location ; Queueing Network ; Utility System ; Mixed Integer Nonlinear Programming (MINLP) ; Choice Behavior ; Customer Choice Behavior ; Multi-Service Health Centers

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