Design of a mathematical model and a simulation-optimisation approach for master surgical scheduling considering uncertainty in length of stay, demands and duration of surgery

Mohammad Ebrahimi and Arezoo Atighehchian

International Journal of Industrial and Systems Engineering, 2023, vol. 43, issue 4, 435-463

Abstract: In this research a master surgical scheduling problem in conditions of uncertainty of demand, duration of surgery and length of patients' stay is studied. First, an MIP model is developed in which the length of patients' stay is considered probabilistic. Then, allowing for uncertainty in demand, a robust model is presented. Finally, a simulation-optimisation approach is developed in which three parameters are considered as uncertain. In this approach, the Grey Wolf and genetic algorithms are designed as the optimisation, and the Mont Carlo simulation is used in the simulation module. The results show that the maximum gap in the comparison of the simulation-optimisation algorithms and the lower-bound solution of the mathematical models in small-scale problems is only 9.36% while the algorithms are much faster. In larger-scale problems, the average improvement percentage of the proposed approach with the Grey Wolf optimisation module as compared to the genetic algorithm module is 2.93%.

Keywords: master surgical scheduling; MSS; simulation-optimisation approach; mixed integer programming; uncertainty; robust optimisation. (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:43:y:2023:i:4:p:435-463

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