Simulation Optimization for Operating Room Scheduling

This paper presents a case study on operating room scheduling in a small hospital in Chile. Patient flow was represented using a discrete-event simulation model that considered the randomness associated with the primary activities of the entire process, which includes preand posthospitalisation, surgery, surgery setup and recovery. A simulated annealing algorithm was implemented and connected to the simulation model to search for better patient schedules. Additionally, three dispatching rules, Shortest Processing Time (SPT), Longest Processing Time (LPT) and First-In, First-Out (FIFO) were used. The results showed that the simulated annealing approach, based on the Cmax objective function, obtained schedules that were 18 % better than the hospital’s scheduling practices. The utilisation of dispatching rules also has a significant effect in the Cmax indicator. The SPT rule performed better than the hospital schedule in two of the three experiments considered in the study. (Received in March 2014, accepted in January 2015. This paper was with the authors 1 month for 1 revision.)

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