Patient scheduling with deteriorating treatment duration and maintenance activity

Shao, Kaining; Fan, Wenjuan; Yang, Zishu; Yang, Shanlin; Pardalos, Pãnos M.

doi:10.1007/s00500-020-05156-4

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…Gocgun (2018) developed a simulation-based approximate dynamic programming approach to solve the dynamic patient scheduling problem with the consideration of cancellation of treatments. Shao et al (2020) first investigated the radiotherapy scheduling for brachytherapy patients considering maintenance activity and a GCSA algorithm was designed to minimize the maximum completion time. In this paper, we further consider patients setup time with the objective to maximize the overall treatment value and a column generation approach is proposed to solve the problem effectively.…”

Section: Radiotherapy Schedulingmentioning

confidence: 99%

A column generation approach for patient scheduling with setup time and deteriorating treatment duration

et al. 2021

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Nowadays most cancer patients are treated by radiotherapy. The treatment duration of patients will grow longer over time because of the half-life decaying effect of the radioactive source, which can be regarded as a continuous non-linear deteriorating effect. How to sequence the treatment of the patients before the radioactivity is reduced to the lowest available intensity is an important and complex problem. Meanwhile, the treatment sequence of cancer patients should not only be based on the waiting time but also on the severity of their illness. Therefore, the dual factors which reflect the severity of patients' illness as well as waiting time should be considered. The dual factors are denoted as the treatment value of patients in this paper and we determine patients in the waiting list to be selected, assigned and sorted for treatment, so as to maximize the overall treatment value of all patients. We also consider the setup time for each time of treatment, which cannot be ignored in reality. The original problem model is difficult to solve directly, so we reformulate the original problem to a set covering problem and it is solved by a column generation approach we develop. The master problem of selecting plans for treatment blocks and subproblems of generating plans are solved by GUROBI and dynamic programming, respectively. Numerical experiments are conducted to demonstrate the efficiency of the proposed column generation approach.

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Section: Radiotherapy Schedulingmentioning

confidence: 99%

A column generation approach for patient scheduling with setup time and deteriorating treatment duration

et al. 2021

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“…Two main partially overlapping groups of papers can be distinguished within it. The first group discusses optimization problems and algorithms including local, global, and multi-criteria optimization (see Franchini et al 2020;Ž ilinskas and Litvinas 2020;Nesterov 2020;Posypkin et al 2020;Shao et al 2020;De Leone et al 2020;Capuano et al 2020;Lančinskas et al 2020;Sergeyev et al 2020;Crisci et al 2020;Cavallaro et al 2020;Astorino and Fuduli 2020;Candelieri et al 2020). The second group of papers (see D'Alotto 2020; Pepelyshev and Zhigljavsky 2020;Falcone et al 2020;Amodio et al 2020;Gangle et al 2020;De Leone et al 2020;Astorino and Fuduli 2020) deals with problems and algorithms using the already mentioned recent computational framework allowing one to work with different infinities and infinitesimals numerically.…”

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confidence: 99%

To the special issue dedicated to the 3rd international conference “Numerical Computations: Theory and Algorithms—NUMTA 2019” June 15–21, 2019, Isola Capo Rizzuto, Italy

2020

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Customizing Treatment Scheduling Windows with a Time Margin Recipe: A Single-institutional Study

Chauhan

Munshi

Pradhan

2023

Journal of Medical Physics

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Purpose: Rising cancer incidences, complex treatment techniques, and workflows have all impacted the radiotherapy scheduling process. Intelligent appointment scheduling is needed to help radiotherapy users adapt to new practices. Materials and Methods: We utilized van Herk’s safety margin formula to determine the radiotherapy department’s treatment scheduling window (TSW). In addition, we examined the influence of in-room imaging on linac occupancy time (LOT). Varian Aria™ software version 15.1 was used to collect retrospective data on LOT, treatment site, intent, techniques, special protocol, and in-room imaging. Results: Treatment scheduling windows varied across treatment sites. The mean TSW using van Herk’s formalism was 31.5 min, significantly longer than the current TSW of 15 min (P = 0.036), with the pelvic site having the longest (43.8 min) and the brain site having the shortest (12 min). 28% of patients exceeded the in-practice TSW of 15 min. 46.2% of patients had multiple images per fraction, with the proportion being highest in pelvic patients (33%). Patients treated with palliative intent, intensity-modulated radiotherapy, special protocols (bladder protocol and gating), and multiple in-room images per fraction had significantly higher LOT. High treatment time uncertainty was observed in the pelvic and thorax sites, indicating the impact of in-room imaging frequency and on-couch treatment decisions on overall treatment time and indicating that current treatment practices should be reviewed and modified if necessary. Conclusions: The time margin recipe can customize the treatment scheduling window and improve treatment practices. This formalism can help manage the radiotherapy department’s workload and reduce patient wait times.

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Patient scheduling with deteriorating treatment duration and maintenance activity

Cited by 3 publications

References 52 publications

A column generation approach for patient scheduling with setup time and deteriorating treatment duration

A column generation approach for patient scheduling with setup time and deteriorating treatment duration

To the special issue dedicated to the 3rd international conference “Numerical Computations: Theory and Algorithms—NUMTA 2019” June 15–21, 2019, Isola Capo Rizzuto, Italy

Customizing Treatment Scheduling Windows with a Time Margin Recipe: A Single-institutional Study

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