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Home Healthcare Planning in a Multi-Service Healthcare Network Considering Dynamic Demands and Synchronized Visits

Bahojb Mohammadi, Mirsadra | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56477 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Radman, Maryam
  7. Abstract:
  8. The global rise in the elderly population, coupled with shortages in medical equipment and healthcare professionals, along with the profound impact of epidemic outbreaks like COVID-19, has underscored the critical significance of home healthcare on a global scale. Currently in Iran, some hospitals and private companies provide home healthcare services to patients. This research focuses on optimizing the scheduling and routing of nurses providing home care services to patients. In the face of daily planning challenges, which include both anticipated and uncertain emergency demands, we propose a method to efficiently allocate nursing resources. To address the unpredictability of emergency demands, each day is segmented into equal time intervals. Within each interval, a static model is formulated to address unmet regular demands and incorporate the occurrence of emergency demands from the preceding interval. This two-stage mathematical model aims to minimize uncovered visits, violations of time windows, and part-time nurses' wage costs. Key innovations of this study encompass the consideration of multiple service types at each demand node, accounting for interdependencies between services, the incorporation of two categories of nurses official and part-time, with distinct working hours and geographic coverage constraints, and the synchronization of visits while allowing for a permissible delay in nurses' arrival times. The proposed model is a mixed integer non-linear program, which has been efficiently solved for small-scale instances using the CPLEX solver after linearization. Given its NP-hard complexity, a genetic algorithm has been developed to tackle medium and large-scale instances. To assess the algorithm’s performance, three problem categories of varying dimensions (small, medium, and large) were constructed. In small-scale instances, the genetic algorithm was compared with the CPLEX solver on problems with known optimal solutions. The results indicate that, on average, the genetic algorithm achieved solutions within one percent of the optimal solution
  9. Keywords:
  10. Home Health Care ; Scheduling ; Routing ; Genetic Algorithm ; Dynamic Demand ; Synchronized Visits ; Multi-Service Health Centers ; Service Scheduling

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