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Genetic Algorithm Based Scheduling of Radiotherapy Treatments for Cancer Patients

  • Dobrila Petrovic
  • Mohammad Morshed
  • Sanja Petrovic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)

Abstract

This paper presents a multi-objective model for scheduling of radiotherapy treatments for cancer patients based on genetic algorithms (GA). The model is developed and implemented considering real life radiotherapy treatment processes at Arden Cancer Centre, Coventry, UK. Two objectives are defined: minimisation of the Average patient waiting times and minimisation of Average tardiness of the patient first treatment fractions. Two scenarios are analysed considering the availability of the doctors to approve treatment plans. The schedules generated by the GA using real data collected from the collaborating Cancer Centre have good performance. It is demonstrated that enabling doctors to approve treatment plans instantly has a great impact on Average waiting time and Average tardiness for all patient categories.

Keywords

Radiotherapy Genetic Algorithms Scheduling Waiting Times 

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References

  1. 1.
    Pinedo, M.: Scheduling: Theory, Algorithms and Systems. Prentice-Hall, New Jersey (2002)Google Scholar
  2. 2.
    Conforti, D., Guerriero, F., Guido, R.: Optimisation Models for Radiotherapy Patient Scheduling. 4OR: Quart J. Ops. Res. 6(3), 263–278 (2008)CrossRefGoogle Scholar
  3. 3.
    Kapamara, T., Sheibani, K., Petrovic, D., Haas, O., Reeves, C.R.: A Simulation of a Radiotherapy Treatment Systems: A Case Study of a Local Cancer Centre. In: Proc. of ORP3 Conference, Guimaraes, Portugal, pp. 29–35 (2007)Google Scholar
  4. 4.
    Larsson, S.N.: Radiotherapy Patient Scheduling Using a Desktop Personal Comp. J. Clini-cal Oncology 5, 98–101 (1993)CrossRefGoogle Scholar
  5. 5.
    Petrovic, S., Leung, W., Song, X., Sundar, S.: Algorithms for Radiotherapy Treatment Booking. In: 25th Workshop of the UK Planning and Scheduling Special Interest Group, Nottingham, UK, pp. 105–112 (2006)Google Scholar
  6. 6.
    Joint Collegiate Council for Oncology (JCCO). Reducing Delays in Cancer Treatment: Some Targets. Technical report, Royal College of Physicians, London (1993)Google Scholar
  7. 7.
    Deb, K.: Multiobjective Optimisation using Evolutionary Algorithms. John Wiley & Sons, New York (2001)Google Scholar
  8. 8.
    Goldberg, D.: Genetic Algorithms in Search, Optimisation & Machine Learning. Addison Wesley, Reading (1989)Google Scholar
  9. 9.
    Gen, M., Tsujimura, Y., Kubota, E.: Solving Job-shop Scheduling Problems by Genetic Algorithm. In: Proc. IEEE International Conference on Systems, Man, and Cybernetics, Texas, vol. 2, pp. 1577–1582 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dobrila Petrovic
    • 1
  • Mohammad Morshed
    • 1
  • Sanja Petrovic
    • 2
  1. 1.Faculty of Engineering and ComputingCoventry UniversityCoventryUK
  2. 2.School of Computer ScienceUniversity of NottinghamNottinghamUK

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