Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61402
Title: A modified genetic algorithm for non-emergency outpatient appointment scheduling with highly demanded medical services considering patient priorities
Contributor(s): Alizadeh, Reza (author); Rezaeian, Javad (author); Abedi, Mehdi (author); Chiong, Raymond  (author)orcid 
Publication Date: 2020
DOI: 10.1016/j.cie.2019.106106
Handle Link: https://hdl.handle.net/1959.11/61402
Abstract: 

This paper presents an optimization problem concerning the booking of non-emergency outpatient appointments, where a single machine (device) and limited medical staff are supposed to fulfill the medical requirements of a large number of waiting patients. A new mixed integer linear programming model is formulated, characterized by two main features: (1) the method applied to calculate the duration of every single appointment is based on patients' particular therapy requirements and doctors' operating speeds, where the duration of the appointments may be different" and (2) taking the priorities of patients into consideration as a key factor of scheduling in the form of the times they prefer to be booked. The proposed model is solved and evaluated using an exact solver on several small-scale numerical examples, and the optimal solutions show that the model is welldesigned and accurate. For instances of larger scale, a genetic algorithm (GA) is used to solve them. The performance of the GA is analyzed by comparing it to the exact method using a set of defined examples. Results indicate that the GA performs satisfactorily within reasonable computational time.

Publication Type: Journal Article
Source of Publication: Computers and Industrial Engineering, v.139, p. 1-10
Publisher: Elsevier Ltd
Place of Publication: United Kingdom
ISSN: 1879-0550
0360-8352
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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