Integrated operating room planning and scheduling problem with assistant surgeon dependent surgery durations. (April 2015)

• Record Type: Journal Article
• Authors: Molina-Pariente, Jose M.; Fernandez-Viagas, Victor; Framinan, Jose M.
• Is Part Of: Computers & industrial engineering. Volume 82(2015)
• Page Start: 8
• Page End: 20
• Publication Date: 2015-04
• Subjects: Operations research in health services -- Operating room planning and scheduling -- Assistant surgeon dependent surgery duration -- Approximate methods; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2015.01.006
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We address the integrated operating room planning and scheduling problem. We consider surgical teams composed by one or two surgeons. We consider surgery durations depending on the assistant surgeon's experience and skills. We propose a mixed integer linear programming decision model for the problem. We implement a heuristic and we compare the results with exact methods. Abstract: There is evidence in the literature that most surgeries in hospitals are performed by a team composed of two surgeons, and that their experience largely influences the surgery duration. However, to the best our knowledge, only one contribution has addressed the operating room planning and scheduling problem with surgical teams, but in such case surgery durations did not depend on the experience of surgeons. In this paper we address an integrated operating room planning and scheduling problem with surgical teams composed by one or two surgeons where surgery durations depend on their experience and skills. We propose a mixed integer linear programming (MILP) model to optimally solve this problem. Given the high computation requirements of our MILP model, we also propose an iterative constructive method. In order to evaluate the performance of both exact and approximate methods, an extensive test bed is generated. The computational experience shows that the proposed algorithm is able to find feasible solution for all problems requiring shorter CPU time and average relative percentage deviation than the MILP model. Finally, the robustness of the so-obtained surgical schedules is analyzed using simulation.