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Stochastic goal programming and metaheuristics for the master surgical scheduling problem

Author

Listed:
  • Justin Britt
  • Xiangyong Li
  • Ahmed Azab
  • Mohammed Fazle Baki

Abstract

Planning and scheduling in a hospital require the consideration of several competing objectives, stakeholders, and resources. In this paper, methods for the master surgical scheduling problem (MSSP), which involves assigning surgeons to time blocks in operating rooms (ORs), are proposed. A stochastic weighted goal programming model (WGPM) with four goals and metaheuristics are used to perform elective surgery scheduling under uncertainty of both surgical durations and patient lengths of stay. In addition, discrete event simulation (DES) models and a decision support system (DSS) are developed. Computational experiments are used to evaluate the WGPM, validate the DES models, assess the relationships between the goals, and to tune and evaluate the metaheuristics. Results show that even though there are trade-offs between the goals that must be considered, it is possible to attain a high level of OR utilisation while meeting strategic targets and optimising recovery ward (RW) utilisation.

Suggested Citation

  • Justin Britt & Xiangyong Li & Ahmed Azab & Mohammed Fazle Baki, 2022. "Stochastic goal programming and metaheuristics for the master surgical scheduling problem," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 43(1/2), pages 5-41.
    • Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:5-41
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