Table of Contents Author Guidelines Submit a Manuscript
Scientific Programming
Volume 2016 (2016), Article ID 4795101, 14 pages
Research Article

Bilevel Fuzzy Chance Constrained Hospital Outpatient Appointment Scheduling Model

1Institute of Cross-Process Perception and Control, Shaanxi Normal University, Xi’an 710119, China
2International Business School, Shaanxi Normal University, Xi’an 710062, China
3School of Economics, Renmin University of China, Beijing 100872, China
4LeBow College of Business, Drexel University, Philadelphia, PA 19104, USA
5School of Economics and Management, Xidian University, Xi’an 710071, China
6Department of Industrial Engineering, University of Toronto, Toronto, ON, Canada

Received 20 May 2016; Accepted 13 July 2016

Academic Editor: Dan Ralescu

Copyright © 2016 Xiaoyang Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.