A two-phase approach for the Radiotherapy Scheduling Problem.
The Radiotherapy Scheduling Problem (RTSP) focuses on optimizing the planning of radiotherapy treatment sessions for cancer patients. In this paper, we propose a two-phase approach for the RTSP. In the first phase, radiotherapy sessions are assigned to specific linear accelerators (linacs) and days....
| Publicado en: | Health Care Management Science Vol. 25; no. 2; pp. 191 - 208 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157024159&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157024159 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Jun2022 vid: 25 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157024159 152363304 157024159 NLM34505969 10.1007/s10729-021-09579-9 NLM34505969 157024159 ppf: 191 ppct: 17 formats: tig: atl: A two-phase approach for the Radiotherapy Scheduling Problem. aug: au: Pham, Tu-San Rousseau, Louis-Martin De Causmaecker, Patrick affil: Polytechnique Montréal, Montréal, Canada sug: subj: Appointments and Schedules Systems Analysis Particle Accelerators Computer Simulation Impact of Events Scale ab: The Radiotherapy Scheduling Problem (RTSP) focuses on optimizing the planning of radiotherapy treatment sessions for cancer patients. In this paper, we propose a two-phase approach for the RTSP. In the first phase, radiotherapy sessions are assigned to specific linear accelerators (linacs) and days. The second phase then decides the sequence of patients on each day/linac and the specific appointment times. For the first phase, an Integer Linear Programming (IP) model is proposed and solved using CPLEX. For the second phase, a Mixed Integer Linear Programming (MIP) and a Constraint Programming (CP) model are proposed. The test data is generated based on real data from CHUM, a large cancer center in Montréal, Canada, with an average of 3,500 new patients and 40,000 radiotherapy treatments per year. The results show that in the second phase, CP is better at finding good solutions quickly while MIP is better at closing optimality gaps with more run time. Lastly, a simulation is conducted to evaluate the impact of different scheduling strategies on the outcome of the scheduling. Preliminary results show that batch scheduling reduces patients' waiting time and overdue time. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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