scholarly journals Genetic Algorithm–Based Outpatient Appointment Scheduling Systems

2015 ◽  
Author(s):  
Song Chew ◽  
Clariecia Groves
Keyword(s):  
Genetic Algorithm ◽  
Appointment Scheduling ◽  
Outpatient Appointment ◽  
Outpatient Appointment Scheduling

scholarly journals Bilevel Fuzzy Chance Constrained Hospital Outpatient Appointment Scheduling Model

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Xiaoyang Zhou ◽  
Rui Luo ◽  
Canhui Zhao ◽  
Xiaohua Xia ◽  
Benjamin Lev ◽  
...  
Keyword(s):  
Real Life ◽  
Appointment Scheduling ◽  
Full Time ◽  
Outpatient Appointment ◽  
Fuzzy Variables ◽  
Hospital Outpatient ◽  
Scheduling Model ◽  
Interactive Algorithm ◽  
Part Time ◽  
Outpatient Appointment Scheduling

Hospital outpatient departments operate by selling fixed period appointments for different treatments. The challenge being faced is to improve profit by determining the mix of full time and part time doctors and allocating appointments (which involves scheduling a combination of doctors, patients, and treatments to a time period in a department) optimally. In this paper, a bilevel fuzzy chance constrained model is developed to solve the hospital outpatient appointment scheduling problem based on revenue management. In the model, the hospital, the leader in the hierarchy, decides the mix of the hired full time and part time doctors to maximize the total profit; each department, the follower in the hierarchy, makes the decision of the appointment scheduling to maximize its own profit while simultaneously minimizing surplus capacity. Doctor wage and demand are considered as fuzzy variables to better describe the real-life situation. Then we use chance operator to handle the model with fuzzy parameters and equivalently transform the appointment scheduling model into a crisp model. Moreover, interactive algorithm based on satisfaction is employed to convert the bilevel programming into a single level programming, in order to make it solvable. Finally, the numerical experiments were executed to demonstrate the efficiency and effectiveness of the proposed approaches.

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