Heuristics to solve appointment scheduling in chemotherapy

Le, Minh-Duc; Nguyen, Minh Hong; Baril, Chantal; Gascon, Viviane; Dinh, Tien Ba

doi:10.1109/rivf.2015.7049875

Cited by 4 publications

(6 citation statements)

References 11 publications

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“…The most common area of outpatient scheduling is in the field of oncology and cancer research [45,47,48,50,51]. It is the leading cause of death in Canada with over 233,900 cases in 2021 and 85,100 deaths [52].…”

Section: Papersmentioning

confidence: 99%

“…Efficient scheduling of cancer outpatients could improve mortality and allow more patients to be seen. Le et al [45] focus on the problem of appointment scheduling for chemotherapy. This type of schedule is unique as there are over 115 different types of treatments that can be done on a patient, each with its own duration, frequency, etc.…”

Section: Papersmentioning

confidence: 99%

“…Each implementation used slightly different tools each with its own advantages. When coding their model, Bourdais, Galinier, Saremi et al [35], Le et al [45], Hooshangi-Tabrizi et al [51], Tohidi et al [32], and Pesant [13] used C++. White et al [18] and Saremi et al [36] implemented theirs in C#.…”

Section: Toolsmentioning

confidence: 99%

“…As Naderi et al [21] has demonstrated, there are other paradigms to consider that can lead to faster and more effective models. Le et al [45] and Bourdais, Galinier, and Pesant [13] also touch on this idea by suggesting a horizontal scheduling method (per week or schedule) and comparing it to a vertical (per day) method. Each version of the data has different benefits.…”

Section: Future Workmentioning

confidence: 99%

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A Review of the Scheduling Problem within Canadian Healthcare Centres

Little

Choudhury

2022

Applied Sciences

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In this paper, the current literature regarding nurse scheduling and physician scheduling in Canada is reviewed. Staff scheduling is a vital aspect of healthcare which has immediate positive benefits when optimized. It is also a very complex optimization problem, often involving conflicts, human evaluation and time constraints. Four categories of problems are reviewed: staff scheduling, physician scheduling, operating room scheduling, and outpatient scheduling, each focusing on a different aspect of resource scheduling and involving unique considerations. Numerous different heuristics and algorithms have been implemented and tested in dozens of hospitals across Canada with nearly universal positive results. Despite the obvious benefits, continued implementations of the optimization software is uncommon.

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Section: Papersmentioning

confidence: 99%

Section: Papersmentioning

confidence: 99%

Section: Toolsmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

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A Review of the Scheduling Problem within Canadian Healthcare Centres

Little

Choudhury

2022

Applied Sciences

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“…A vast literature on approximation methods has been developed in the last decade, especially for scheduling problems, Shahnawaz [36] proposes an efficient algorithm using a TS to schedule the identical parallel machines with single resource. Minh-Duc Le et al [25] paper studies meta-heuristic methods in solving an appointmentscheduling problem in a hospital in Canada. Their initial solution was generated with a Greedy Algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-stage appointment scheduling for outpatient chemotherapy unit: a case study

et al. 2021

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This paper deals with a multi stage hybrid flow-shop problem (HFSP) that arises in a privately Chemotherapy clinic. It aims to optimize the makespan of the daily chemotherapy activity. Each patient must respect the cyclic nature of chemotherapy treatment plans made by his referent oncologist while taking into account the high variability in resource requirements (treatment time, nurse time, pharmacy time). The problem requires the assignment of chemotherapy patients to oncologists, pharmacists, chemotherapy beds or chairs and nurses over a 1-day period. We provided a Mixed Integer Program (MIP) to model this issue, which can be considered as a five-stage hybrid flow-shop scheduling problem with additional resources, dedicated machines, and no-wait constraints. Since this problem is known to be NP-hard, we provided a lower bound expression and developed an approximated solving algorithm: a tabu search inspired metaheuristic based on a constructive heuristic that can quickly reach satisfying results. To assess the empirical performance of the proposed approach, we conducted experiments on randomly generated instances based on real-world data of a Tunisian private clinic: Clinique Ennasr. Computational experiments show the efficiency of the proposed procedures: The mathematical model provided optimal solutions in reasonable computational time only for small instances (up to 10 patients). Meta-heuristic’s results demonstrate, also, that the proposed approach offers good results in terms of solution quality and computational times with an average relative gap to the MIP solution equal to 3.13% and to the lower bound equal to 5.37% for small instances (up to 15 patients). The same gap to the lower bound increases to 25% for medium and large size instances (20-50 patients).

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