Ayrıntılı Bilimsel Program ve Bildiri Özetleri - YAEM2010

YAEM 2010
YÖNEYLEM ARAÞTIRMASI VE ENDÜSTRÝ MÜHENDÝSLÝGI 30. ULUSAL KONGRESÝ
Robust Reactive Scheduling of Intensive Care Units
Erhan Kozan
Mathematical Sciences, Queensland University of Technology
In this paper, methods for scheduling intensive care units (ICU) are
explored. A general system is proposed that fits this situation and
move on to how this can be applied to the ongoing scheduling of ICU.
Scheduling of patients in the ICU is complicated by the two general
types; elective surgery and emergency arrivals. This complicated
situation is handled by creating a tentative initial schedule and then
reacting to uncertain arrivals as they occur. The model is then
extended to a stochastic model which relaxes the assumption that
emergency patients arrive at known times. Because there is no
efficient algorithm to solve it, heuristics techniques are developed and
examined. Schedules are generated using each, and compared to
measures such as utilization, the proportion of time that the ICU is
full, and the number of patients not admitted.
Dispatching Policies for Doctors and Dimensioning in
Overloaded Emergency Rooms
1 2 2
Tolga Tezcan , Vera Tilson , Greg Dobson
1 Industrial and Enterprise Systems Engineering, University of Illinois
at Urbana-Champaign
2 Simon GSB, University of Rochester
In this research we focus on a queuing model arising in emergency
room operations. The model consists of three stations where the first
and third stations are attended by a doctor and the second station is
attended by nurses or technicians. Patients arrive to the system
endogenously and have to be treated in stations 1, 2 and 3,
consecutively, before they can leave the emergency room (ER). When
the objective is to maximize the steady state throughout and
interruptions by the patients in the ER are ignored, we show that a
push policy (the doctor gives priority to station 1) is optimal in an
overloaded ER. When interruptions are considered we show that a pull
policy (the doctor gives priority to station 3) is asymptotically optimal
in large overloaded ER systems. Using these two results we propose
closed form approximations for determining the appropriate number
of beds needed in an ER.
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