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Operating Room Scheduling with Data driven Robust Optimization Approach

Shah Hosseini, Yasaman | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53010 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Rafiee, Majid
  7. Abstract:
  8. In this thesis we will focus on operating room scheduling considering open strategy and uncertainty in the duration of operations for elective patients. A mixed integer programming model will be proposed having the expected list of patients that are going to be scheduled in the specific time horizon and working hours of doctors with the purpose of maximizing the number of scheduled elective patients in the determined time horizon. We aim to assign each patient to an operating room and a start time regarding operating rooms and surgeons working hours, care units capacity constraints and sequencing constraints. We will define three possible ways for each patient after their operations, naming ICU, CCU and ward that in our model we will assign each patient to these paths considering beds constraints. In order to solve the model considering uncertainty in the duration of operations we will use data-driven robust optimization approach. In this approach Uncertainty set will be proposed with the help of Dirichlet process mixture model in nonparametric Bayesian. We will use the Milad hospital data to validate our model and propose our uncertainty set. In this thesis, we will focus on operating room scheduling considering open strategy and uncertainty in the duration of the operations for elective patients. A mixed integer programming model will be proposed having the waiting list of patients that are going to be scheduled in the specific time horizon and working hours of doctors with the purpose of maximizing the number of scheduled elective patients in the determined time horizon. We aim to assign each patient to an operating room and a start time regarding operating rooms and surgeons working hours, care units capacity constraints, and sequencing constraints. We will define three possible ways for each patient after their operations, naming ICU, CCU, and ward that in our model we will assign each patient to these paths considering bed constraints. In order to solve the model considering uncertainty in the duration of the operations, we will use a data driven robust optimization approach. In this approach, the Uncertainty set will be proposed with the help of the Dirichlet process mixture model in nonparametric Bayesian. We will use the Milad hospital data to validate our model and propose our uncertainty set
  9. Keywords:
  10. Operating Room Scheduling ; Robust Optimization ; Benders Decomposition ; Operaring Room Planning ; Data Driven Uncertainty Set ; Uncertainty ; Post-Anesthesia Care Unit (PACU)

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