زمان‌بندي اتاق عمل براي جراحي الكتيو با درنظرگرفتن واحدهاي مراقبت بعد از عمل به كمك برنامه‌ريزي آرماني

Operating Room Scheduling for Elective Surgeries Considering Downstream Care Units

در اين مقاله، يك مدل برنامه‌ريزي آرماني با متغيرهاي صحيح براي زمان‌بندي اتاق‌هاي عمل به‌صورت بلوكي ارائه شده كه در آن زمان‌هاي مورد نياز بستري بيماران تصادفي درنظر گرفته شده است. هدف براي زمان‌بندي اصلي جراحي، كمينه‌كردن هزينه‌هاي واحدهاي مراقبت پايين‌دستي است. براي كاهش پيچيدگي محدوديت‌هاي ظرفيت، از مقادير مورد انتظار و نيم‌انحراف معيار مثبت تعداد بيماران در واحدهاي مراقبت پايين‌دستي استفاده، و مثال‌ها با كمك نرم‌افزار گمز حل شده است. نوآوري پژوهش در اين است كه اولاً به هر دو معيار كمبود ظرفيت و خالي‌ماندن تخت توجه شده و ثانياً جراحان براي رسيدن به اين اهداف، زمان‌بندي شده‌‌اند. با استفاده از مدل پيشنهادي مي‌توان روز (هاي) مناسب براي هر جراح را به‌نحوي تعيين كرد كه كارايي واحدهاي مراقبت پايين‌دستي در حداكثر سطح ممكن باشد و براي اين كار بايد به سطح خدمت موردنظر و محدوديت بلوك‌هاي تخصيصي به سرويس‌ها توجه شود.

In a hospital, operating rooms produces a large part of the costs, on the one hand, and a large part of the income, on the other hand. One of the most impressive ways to increase the operating rooms efficiency is using effective ways for planning and scheduling. While this department has a close relation with other departments in the hospital, improving it’s efficiency will raise patients satisfaction and the whole hospital efficiency and performance. In this paper, an stochastic integer mathematical model has been developed for operating rooms planning and scheduling, based on Fugener et al model, with the aim of minimizing the cost of underutilization and overcapacity in downstream units, including intensive care unit and wards. The model aims to provide a cyclic master surgery scheduling at the tactical level based on hospital strategic decisions. The model allocates blocks to specialties based on block scheduling strategy, but also determines each surgeon’s surgery schedule. Also, to improve the efficiency and reduce the complexity of the model for the large scale cases, the convolution stochastic distribution parts of the model have been exchanged with the expected values and variances of the capacities needed for different days of the week in a corresponding integer goal programming model. Then, different case studies have been generated by changing some of the parameters to show the objective function sensitivity to the changes. Innovation of this research compare to the Fugener et al study is providing the schedules for the surgeons, instead of the specialties. This saves time, expenses and computational operations. Also, positive half variance of the number of patients in downstream units in each day of the week has been used directly instead of the variance of the number of patients in these units.