A chance-constrained programming framework to handle uncertainties in radiation therapy treatment planning

Zaghian, Maryam; Lim, Gino J.; Khabazian, Azin

doi:10.1016/j.ejor.2017.10.018

Cited by 18 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These optimization steps constitute a process usually known as inverse planning optimization. Some examples of different mathematical models and optimization algorithms used can be found in ( Dursun, Ta ¸s kın & Altınel, 2019 ;Lim, Kardar, Ebrahimi & Cao, 2020 ;Lin, Lim & Bard, 2016 ;Rocha, Dias, Ferreira & Lopes, 2013a, 2013bZaghian, Lim & Khabazian, 2018 ). The planner must try different parameters, in a trial-and-error and time-consuming process, since different parameters will originate different treatment plans, with different characteristics and presenting different com promises between existing objectives.…”

Section: Application To Radiotherapy Treatment Planningmentioning

confidence: 99%

Learning target-based preferences through additive models: An application in radiotherapy treatment planning

Dias

Ventura

et al. 2022

European Journal of Operational Research

View full text Add to dashboard Cite

Section: Application To Radiotherapy Treatment Planningmentioning

confidence: 99%

Learning target-based preferences through additive models: An application in radiotherapy treatment planning

Dias

Ventura

et al. 2022

European Journal of Operational Research

View full text Add to dashboard Cite

“…Chance-constrained programming has been carefully studied in the operations research community [8,9,4]. In this domain, chance constraints are used to model problems and relax them into equivalent nonlinear optimization problems which can then be solved by nonlinear programming solvers [14,22,29]. Despite its attention in operations research, chance-constrained optimization has gained comparatively little attention in the area of evolutionary computation [16].…”

Section: Introductionmentioning

confidence: 99%

Runtime Analysis of RLS and the (1+1) EA for the Chance-constrained Knapsack Problem with Correlated Uniform Weights

Xie

Neumann

et al. 2021

Preprint

View full text Add to dashboard Cite

Addressing a complex real-world optimization problem is a challenging task. The chance-constrained knapsack problem with correlated uniform weights plays an important role in the case where dependent stochastic components are considered. We perform a runtime analysis of a randomized search algorithm (RSA) and a basic evolutionary algorithm (EA) for the chance-constrained knapsack problem with correlated uniform weights. We prove bounds for both algorithms for producing a feasible solution. Furthermore, we investigate the behavior of the algorithms and carry out analyses on two settings: uniform profit value and the setting in which every group shares an arbitrary profit profile. We provide insight into the structure of these problems and show how the weight correlations and the different types of profit profiles influence the runtime behavior of both algorithms in the chance-constrained setting.

show abstract

“…Another array of articles have used the chance-constrained programming (CCP) models [3] to address the uncertainty. This approach mitigates the risk of disadvantageous events (e.g., OR overtime, patient waiting time) exceeding the specified thresholds, rather than merely minimizing their expected value [4], [13], [35]. Shylo et al [30] applied chance-constraints to control the OR block overtime in the OR surgery planning problem.…”

Section: Introductionmentioning

confidence: 99%

A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem

Najjarbashi

Lim

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

The required time for surgical interventions in operating rooms (OR) may vary significantly from the predicted values depending on the type of operations being performed, the surgical team, and the patient. These deviations diminish the efficient utilization of OR resources and result in the disruption of projected surgery start times. This paper proposes a two-stage chance-constrained model to solve the OR scheduling problem under uncertainty. The goal is to minimize the costs associated with OR opening and overtime as well as reduce patient waiting times. The risk of OR overtime is controlled using chance constraints. Numerical experiments show that the proposed model provides a better trade-off between minimizing costs and reducing solution variability when compared to two existing models in the literature. It is also shown that the three models converge as the overtime probability threshold approaches one. Moreover, it is observed that the individual chance constraints result in the opening of fewer rooms, lower waiting times, and shorter solution times when compared to that of joint chance constraints. A decomposition algorithm is applied that solves large test instances of the OR scheduling problem, that of which is known to be NP-hard. Strong valid inequalities are derived in order to accelerate the convergence speed. The proposed approach outperformed both a commercial solver and a basic decomposition algorithm after solving all test instances with up to 89 surgeries and 20 ORs in less than 48 minutes. INDEX TERMS Chance constraints, mixed-integer programming, operating room scheduling, two-stage stochastic programming, uncertainty.

show abstract

A chance-constrained programming framework to handle uncertainties in radiation therapy treatment planning

Cited by 18 publications

References 23 publications

Learning target-based preferences through additive models: An application in radiotherapy treatment planning

Learning target-based preferences through additive models: An application in radiotherapy treatment planning

Runtime Analysis of RLS and the (1+1) EA for the Chance-constrained Knapsack Problem with Correlated Uniform Weights

A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem

Contact Info

Product

Resources

About