Robust operating room planning considering upstream and downstream units: A new two-stage heuristic algorithm

• Published in: Computers & industrial engineering 2020-05, Vol.143, p.106387, Article 106387
• Main Authors: Moosavi, Amirhossein, Ebrahimnejad, Sadoullah
• Format: Article
• Language: English
• Subjects: Heuristic algorithm; Operating room; Operational decision-making; Tactical decision-making; Uncertainty
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2020.106387

•Construction of master surgical scheduling and allocation of elective patients.•Studying both upstream and downstream units where their occupancy is leveled.•Introduction of a new operating room management policy.•Development of a new two-stage heuristic algorithm.•Adapting a benchmark algorithm, named partial-mixed integr programming. This paper studies the operating room planning problem at the tactical and operational decision levels considering upstream and downstream units. For this purpose, a multi-objective mathematical programming model is proposed for the construction of master surgical scheduling and the allocation of elective and emergency surgeries. This model encompasses the profits of all stakeholders in the operating theater. A new policy, named complete opening policy, is introduced for the management of operating rooms that has some particular benefits compared to the conventional policy. Then, a scenario-based robust formulation is proposed to consider the uncertainties of surgery duration, length of stay and emergency demands. Because the simpler variants of this problem are known to be NP-complete, a new two-stage heuristic algorithm is developed for solving its large-scale instances. During the first stage, this algorithm generates an initial solution using a greedy constructive algorithm. To improve the initial solution, the algorithm applies eight actions and searches the neighborhoods. In the second stage, the algorithm evaluates whether or not closing an operating room could improve the incumbent solution. A heuristic algorithm, named partial-mixed integer programming, is also adapted as a benchmark algorithm. This algorithm incorporates the CPLEX solver and a very large-scale neighborhood search. Eventually, a hospital in Iran is introduced and evaluated. The computational results demonstrate that the application of the proposed methodology could potentially decrease the waiting cost, the overtime and idleness of operating rooms, and the total deviation from the average beds used in upstream and downstream. The computational results also show that an increase of two beds in the intensive care unit might potentially reduce the waiting cost by 3.6% and the total average of overtime and idleness of operating rooms each day by 20.3%.