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Multi-objective genetic algorithms for scheduling of radiotherapy treatments for categorised cancer patients

Authors :
Sanja Petrovic
Mohammad Morshed
Dobrila Petrovic
Source :
Expert Systems with Applications. 38:6994-7002
Publication Year :
2011
Publisher :
Elsevier BV, 2011.

Abstract

Research highlights? We developed and implemented three genetic algorithms (GAs) for scheduling radiotherapy treatments for different patient categories: (1) Standard-GA, which considers all patient categories equally, (2) KB-GA, which has an embedded knowledge on the scheduling of emergency patient category and (3) Weighted-GA, which operates with different weights given to the patient categories. Performances of generated schedules were compared using real life data and statistical analysis. KB-GA generated the schedules with best performance considering emergency patients and KB-GA and Weighted-GA generated better performance schedules for emergency and palliative patients simultaneously than Standard-GA. This paper presents a multi-objective optimisation model and algorithms for scheduling of radiotherapy treatments for categorised cancer patients. The model is developed considering real life radiotherapy treatment processes at Arden Cancer Centre, in the UK. The scheduling model considers various real life constraints, such as doctors' rota, machine availability, patient's category, waiting time targets (i.e., the time when a patient should receive the first treatment fraction), and so on. Two objectives are defined: minimisation of the Average patient's waiting time and minimisation of Average length of breaches of waiting time targets. Three genetic algorithms (GAs) are developed and implemented which treat radiotherapy patient categories, namely emergency, palliative and radical patients in different ways: (1) Standard-GA, which considers all patient categories equally, (2) KB-GA, which has an embedded knowledge on the scheduling of emergency patient category and (3) Weighted-GA, which operates with different weights given to the patient categories. The performance of schedules generated by using the three GAs is compared using the statistical analyses. The results show that KB-GA generated the schedules with best performance considering emergency patients and slightly outperforms the other two GAs when all patient categories are considered simultaneously. KB-GA and Weighted-GA generated better performance schedules for emergency and palliative patients than Standard-GA.

Details

ISSN :
09574174
Volume :
38
Database :
OpenAIRE
Journal :
Expert Systems with Applications
Accession number :
edsair.doi...........a94217e7ce94b319ef8ed33d5ff079a0
Full Text :
https://doi.org/10.1016/j.eswa.2010.12.015