چكيده به لاتين
Operating room (OR) is one of the most expensive resources in hospitals. As a result, it is very important to use them in an efficient manner. Increasing operating room efficiency may lead to reduction in hospital costs and increased service quality for patients.
In this research, root causes to inefficient OR scheduling are recognized by field research and then it is tried to resolve them using industrial engineering techniques. In a surgical process, so many resource types are utilized except for operating rooms. Although these resource types are not as critical as operating rooms, lack of coordinatation between resources may result in OR scheduling inefficiency, additional costs and loss of service quality.
Inconsistency in different resource types utilization is one of the root causes for OR scheduling inefficiency which is emphasized in filed research either. Encountering this cause, in this research integrated surgical process scheduling is investigated. The other root cause is environmental uncertainty which effects hospital accountability in face of uncertain demands. In order to resolve it, in this research predictive / reactive scheduling process which is introduced as a future direction in a review article, is conducted. Generating primary schedule in view of future uncertainty results in geart impact on implemented schedule efficiency.
Demand uncertainty is considered which is appeared as emergency patient arrival or arrival uncertainty. Predictive / reactive scheduling process consists of two phases: 1) predictive phase; and 2) reactive phases. In predictive phase, stochastic programming moels are used in order to generate stable and robust primary schedules and in reactive phase the primary schedule stability and robustness is preserved by means of using new criteria in reactive programming model.
Finally, focusing on disadvantages of classic stochastic programming models, a new approach encountering sctochastic uncertainty is extended. The new approach is extended based on the assumption that nature consistency between the problem and solution (or modeling) approach will result in more efficiency. Experimental results show that the extended approach is competitive in comparison with classic approaches and is even superior considering some espetial criteria.
