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

Abstract

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.

Radiotherapy treatment scheduling considering time window preferences

“…Although the obtained solution for the original problem may not be proven optimal, we believe that our methodology can be effective in generating a (near-)optimal schedule in due time for most real-world RT centers. According to the International Atomic Energy Agency (IAEA), the NKI ranks amongst the largest RT centers in the Netherlands with a total of 10 radiation machines (orthovoltage machines included) registered by 2017 [19], just below the Erasmus University Medical Center (12) and the University Medical Centre Utrecht (11) but above all the other 21 Dutch centers.…”

“…In case some restrictions (e.g. constraints (12) or (15)) are not part of the planning process of the RT center interested in using our methodology, the MILP model can be easily manipulated to account for those differences by relaxing (excluding) or adding constraints before solving the problem. Furthermore, the solution found by our model can be merely used as a basis where adaptations that fit specific needs of individual patients can be integrated to build a more robust, personalized solution.…”

“…However, as discussed above, besides complying with timeliness requirements and technical constraints, RT centers are often faced with the problem of finding a schedule that maximizes patient preferences regarding the starting time of irradiation sessions. In more recent years, two models have been proposed [ 12 , 15 ] to optimize intra-day linac schedules in a way that starting time of irradiation sessions do not deviate from a pre-defined target time by more than a certain threshold (30 min in both [ 12 ] and [ 15 ]). Vogl et al [ 15 ] modeled the problem for an ion beam facility (in which a single particle beam serves multiple treatment rooms).…”

“…Using real-world inspired data, they found that a combination of two stand-alone metaheuristic approaches leads to the best results when compared to a genetic algorithm and iterated local search. Maschler and Raidl [ 12 ], on the other hand, proposed an enhanced iterated greedy (EIG) metaheuristic to solve the patient scheduling problem with limited starting time variation between sessions in particle therapy. Computational experiments using fictitious data showed that the EIG method outperforms two other metaheuristics in 26 out of 30 instances.…”

Multivalued decision diagrams for prize-collecting job sequencing with one common and multiple secondary resources

“…Moreover, each job has to be performed within given time windows. This problem originates from the context of particle therapy for cancer treatment (Maschler et al 2016;Maschler and Raidl 2018a;Riedler et al 2017). In this scenario the common resource corresponds to a particle beam that can be directed into one of multiple treatment rooms which are represented by the secondary resources.…”