Genetic Algorithm Based Scheduling of Radiotherapy Treatments for Cancer Patients

Petrov́ić, Dobrila; Morshed, Mohammad Sarwar; Petrović, Sanja

doi:10.1007/978-3-642-02976-9_14

Cited by 20 publications

(25 citation statements)

References 3 publications

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“…Optimizing the overall RT chain using both constructive heuristics and metaheuristics, Petrovic et al [32] achieved considerable reductions in waiting times for palliative (34%) and radical patients (41%). Focusing on the pre-treatment stage, Petrovic et al [33] explored similarities between radiotherapy and job-shop scheduling problems commonly encountered in industrial processes, using genetic algorithms to minimize both the average waiting times and the average delays in the start of treatment. Results showed that these indicators were reduced by 35% and 20%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Operations research for resource planning and -use in radiotherapy: a literature review

Vieira

Hans

Vliet-Vroegindeweij

et al. 2016

BMC Med Inform Decis Mak

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BackgroundThe delivery of radiotherapy (RT) involves the use of rather expensive resources and multi-disciplinary staff. As the number of cancer patients receiving RT increases, timely delivery becomes increasingly difficult due to the complexities related to, among others, variable patient inflow, complex patient routing, and the joint planning of multiple resources. Operations research (OR) methods have been successfully applied to solve many logistics problems through the development of advanced analytical models for improved decision making. This paper presents the state of the art in the application of OR methods for logistics optimization in RT, at various managerial levels.MethodsA literature search was performed in six databases covering several disciplines, from the medical to the technical field. Papers included in the review were published in peer-reviewed journals from 2000 to 2015. Data extraction includes the subject of research, the OR methods used in the study, the extent of implementation according to a six-stage model and the (potential) impact of the results in practice.ResultsFrom the 33 papers included in the review, 18 addressed problems related to patient scheduling (of which 12 focus on scheduling patients on linear accelerators), 8 focus on strategic decision making, 5 on resource capacity planning, and 2 on patient prioritization. Although calculating promising results, none of the papers reported a full implementation of the model with at least a thorough pre-post performance evaluation, indicating that, apart from possible reporting bias, implementation rates of OR models in RT are probably low.ConclusionsThe literature on OR applications in RT covers a wide range of approaches from strategic capacity management to operational scheduling levels, and shows that considerable benefits in terms of both waiting times and resource utilization are likely to be achieved. Various fields can be further developed, for instance optimizing the coordination between the available capacity of different imaging devices or developing scheduling models that consider the RT chain of operations as a whole rather than the treatment machines alone.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0390-4) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Operations research for resource planning and -use in radiotherapy: a literature review

Vieira

Hans

Vliet-Vroegindeweij

et al. 2016

BMC Med Inform Decis Mak

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“…Several further heuristic as well as exact approaches followed. Heuristic techniques range from a greedy randomized adaptive search procedure (GRASP) (Petrovic and Leite‐Rocha, ) and steepest hill climbing methods (Kapamara and Petrovic, ; Riff et al., ) to more advanced techniques using genetic algorithms (GAs) (Petrovic et al., , ). Exact methods are based on mixed integer linear programming (MILP) models and consider different levels of granularity (Conforti et al., ; Burke et al., ).…”

Section: Related Workmentioning

confidence: 99%

Particle therapy patient scheduling with limited starting time variations of daily treatments

Maschler

Raidl

2018

Int Trans Operational Res

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The particle therapy patient scheduling problem (PTPSP) arises in modern cancer treatment facilities that provide particle therapy. It consists of scheduling a set of therapies within a planning horizon of several months. A particularity of PTSP compared with classical radiotherapy scheduling is that therapies need not only be assigned to days but also scheduled within each day to account for the more complicated operational scenario. In an earlier work, we introduced this novel problem setting and provided first algorithms including an iterated greedy (IG) metaheuristic. In this work, we consider an important extension to the PTPSP emerging from practice in which the therapies should be provided on treatment days roughly at the same time. To be more specific, the variation between the starting times of the therapies' individual treatments should not exceed the given limits, and needs otherwise to be minimized. This additional constraint implies that the sequencing parts within each day can no longer be treated independently. To tackle this variant of PTPSP, we revise our previous IG and exchange its main components: the part of the applied construction heuristic for scheduling within the days and the local search algorithm. The resulting metaheuristic provides promising results for the proposed extension of the PTPSP and further enhances the existing approach for the original problem.

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“…Optimizing the overall RT chain using both constructive heuristics and metaheuristics, Petrovic et al [32] achieved considerable reductions in waiting times for palliative (34%) and radical patients (41%). Focusing on the pre-treatment stage, Petrovic et al [33] explored similarities between radiotherapy and jobshop scheduling problems commonly encountered in industrial processes, using genetic algorithms to minimize both the average waiting times and the average delays in the start of treatment. Results showed that these indicators were reduced by 35% and 20%, respectively.…”

Section: Schedulingmentioning

confidence: 99%

Operations research for resource planning and -use in radiotherapy: a literature review

Vieira¹,

Hans²,

Vliet-Vroegindeweij³

et al. 2017

European Journal of Cancer

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Background: The delivery of radiotherapy (RT) involves the use of rather expensive resources and multi-disciplinary staff. As the number of cancer patients receiving RT increases, timely delivery becomes increasingly difficult due to the complexities related to, among others, variable patient inflow, complex patient routing, and the joint planning of multiple resources. Operations research (OR) methods have been successfully applied to solve many logistics problems through the development of advanced analytical models for improved decision making. This paper presents the state of the art in the application of OR methods for logistics optimization in RT, at various managerial levels.

show abstract

Genetic Algorithm Based Scheduling of Radiotherapy Treatments for Cancer Patients

Cited by 20 publications

References 3 publications

Operations research for resource planning and -use in radiotherapy: a literature review

Operations research for resource planning and -use in radiotherapy: a literature review

Particle therapy patient scheduling with limited starting time variations of daily treatments

Operations research for resource planning and -use in radiotherapy: a literature review

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