Abstract
In radiotherapy, treatment needs to be delivered in time. Long waiting times can result in patient anxiety and growth of tumors. They are often caused by inefficient use of radiotherapy equipment, the linear accelerators (LINACs). However, making an efficient schedule is very challenging, especially when we have multiple types of patients, having different service requirements and waiting time constraints. Moreover, in radiotherapy a patient needs to go through a LINAC multiple times over multiple days, to complete the treatment. In this paper we model the radiotherapy treatment process as a queueing system with multiple queues, and we propose a new class of scheduling policies that are simple, flexible and fair to patients. Numerical experiments show that our new policy outperforms the commonly used policies. We also extend the policy to an adaptive one to deal with unknown and fluctuating arrival rates. Our adaptive policy turns out to be quite efficient in absorbing the effects caused by these changes. Due to the complexity of our problem, we select the parameters of the policies through simulation-based optimization heuristics. Our work may also have important implications for managers in other service systems such as call centers.
Similar content being viewed by others
References
Burke EK, Leite-Rocha P, Petrovic S (2011) An integer linear programming model for the radiotherapy treatment scheduling problem. arXiv preprint arXiv:11033391
Chan W, Koole G, L’Ecuyer P (2014) Dynamic call center routing policies using call waiting and agent idle times. Manuf Serv Oper Manag 16(4):544–560
Chen Z, King W, Pearcey R, Kerba M, Mackillop WJ (2008) The relationship between waiting time for radiotherapy and clinical outcomes: a systematic review of the literature. Radiother Oncol 87(1):3–16
Conforti D, Guerriero F, Guido R (2008) Optimization models for radiotherapy patient scheduling. 4OR 6(3):263–278
Gurvich I, Whitt W (2010) Service-level differentiation in many-server service systems via queue-ratio routing. Oper Res 58(2):316–328
Legrain A, Fortin MA, Lahrichi N, Rousseau LM (2015) Online stochastic optimization of radiotherapy patient scheduling. Health Care Manag Sci 18(2):110–123
Legros B, Jouini O, Koole G (2015) Adaptive threshold policies for multi-channel call centers. IIE Trans 47(4):414–430
Li S, Geng N, Xie X (2015) Radiation queue: meeting patient waiting time targets. IEEE Robot Autom Mag 22(2):51–63
Organization WH (2017) World’s health ministers renew commitment to cancer prevention and control. Cancer report WHO 2017 http://www.who.int/cancer/media/news/cancer-prevention-resolution/en/. Accessed 30 May 2017
Petrovic S, Leite-Rocha P (2008) Constructive and grasp approaches to radiotherapy treatment scheduling. In: World congress on engineering and computer science 2008, WCECS’08. IEEE, pp 192–200
Saure A, Patrick J, Tyldesley S, Puterman ML (2012) Dynamic multi-appointment patient scheduling for radiation therapy. Eur J Oper Res 223(2):573–584
Tezcan T, Dai J (2010) Dynamic control of n-systems with many servers: asymptotic optimality of a static priority policy in heavy traffic. Oper Res 58(1):94–110
Ward AR, Armony M (2013) Blind fair routing in large-scale service systems with heterogeneous customers and servers. Oper Res 61(1):228–243
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, S., Koole, G. & Xie, X. An adaptive priority policy for radiotherapy scheduling. Flex Serv Manuf J 32, 154–180 (2020). https://doi.org/10.1007/s10696-019-09373-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10696-019-09373-4