A Multi-encoded Genetic Algorithm Approach to Scheduling Recurring Radiotherapy Treatment Activities with Alternative Resources, Optional Activities, and Time Window Constraints

Vogl, Petra; Braune, Roland; Doerner, Karl F.

doi:10.1007/978-3-319-74718-7_45

Cited by 4 publications

(11 citation statements)

References 9 publications

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“…Another scheduling problem related to particle therapy is considered in Vogl et al. (). Although their problem is from a setting similar to ours, it differs in many details.…”

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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“…Another scheduling problem related to particle therapy is considered in Vogl et al. (). Although their problem is from a setting similar to ours, it differs in many details.…”

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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“…We then would aim to schedule activities requiring any other treatment room in between those activities to minimize the idle time on the beam resource. This approach has been proven beneficial in our problem setting (see Vogl et al 2018).…”

Section: Solution Decoding and Solution Evaluationmentioning

confidence: 99%

“…In a preliminary study, we introduced the solution decoding presented in Sect. 5.4 and presented initial tests of different decoding algorithms and GA settings, applied within this research setting (Vogl et al 2018). We enhance this strategy here by introducing a more sophisticated crossover mechanism, in addition to the patient-wise crossover presented in the previous work.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Crossover( p 1 , p 2 ) The multi-encoded solution representation demands specific crossover and mutation operators for the individuals to remain feasible throughout the evolutionary process. We use two crossover operators: (1) The patient-wise crossover operator presented by Vogl et al (2018), where the whole DT assignment as well as the whole PET and WCE lists of one patient p is randomly inherited either by the first or the second parent. This simple crossover only leads to limited variety in the binary encodings, because the combinations of the initial population dominate the search.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…To choose individuals for reproduction [Perform Selection(P i )], we employ the rank selection operator. Because offspring selection (Affenzeller and Wagner 2004) is beneficial in our problem setting (Vogl et al 2018), we again incorporate this strategy into our GA. We aim to build 70% of the offspring population from children that outperform their worse parent (see line 7 and lines 12-17 in Algorithm 3). The number of reproductive steps is limited to five times the population size in each iteration (see line 7).…”

Section: Genetic Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Scheduling recurring radiotherapy appointments in an ion beam facility

Vogl

Braune

Doerner

2018

J Sched

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Ion beam radiotherapy is a modern form of cancer treatment that is offered in specialized facilities. Treatment consists of multiple, almost daily irradiation appointments, followed by optional imaging and control assignments. The corresponding problem of scheduling these recurring radiotherapy treatment appointments can be classified as a complex job shop scheduling problem with custom constraints, such as recurring activities, optional activities, and special time window constraints. The objective is to minimize the operation time of the bottleneck resource, the particle beam, while simultaneously minimizing any penalties arising from violations of time window constraints. The authors model the problem mathematically and introduce various customized constraints. Three metaheuristic solution approaches-namely a genetic algorithm with tailor-made feasibility-preserving crossover operators, an iterated local search, and a combination of the two approaches-all perform well on both small and large problem instances. However, the simple combination of the two stand-alone algorithms leads to best results when applied to real-world inspired problem instances.

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A Variable Neighborhood Search for the Job Sequencing with One Common and Multiple Secondary Resources Problem

Kaufmann

Horn

Raidl

2020

Parallel Problem Solving From Nature – PPSN XVI

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Die approbierte gedruckte Originalversion dieser Diplomarbeit ist an der TU Wien Bibliothek verfügbar.The approved original version of this thesis is available in print at TU Wien Bibliothek. Die approbierte gedruckte Originalversion dieser Diplomarbeit ist an der TU Wien Bibliothek verfügbar.The approved original version of this thesis is available in print at TU Wien Bibliothek.Die approbierte gedruckte Originalversion dieser Diplomarbeit ist an der TU Wien Bibliothek verfügbar.The approved original version of this thesis is available in print at TU Wien Bibliothek. Die approbierte gedruckte Originalversion dieser Diplomarbeit ist an der TU Wien Bibliothek verfügbar.The approved original version of this thesis is available in print at TU Wien Bibliothek.

show abstract

A Multi-encoded Genetic Algorithm Approach to Scheduling Recurring Radiotherapy Treatment Activities with Alternative Resources, Optional Activities, and Time Window Constraints

Cited by 4 publications

References 9 publications

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

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

Scheduling recurring radiotherapy appointments in an ion beam facility

A Variable Neighborhood Search for the Job Sequencing with One Common and Multiple Secondary Resources Problem

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