Robust Optimization of Hub-and-Spoke Airline Network Design Based on Multi-Objective Genetic Algorithm

A robust optimization of an automobile valvetrain is presented where the variation of engine performances due to the component dimensional variations is minimized subject to the constraints on mean engine performances. The dimensional variations of valvetrain components are statistically characterized based on the measurements of the actual components. Monte Carlo simulation is used on a neural network model built from an integrated high fidelity valvetrain-engine model, to obtain the mean and standard deviation of horsepower, torque and fuel consumption. Assuming the component production cost is inversely proportional to the coefficient of variation of its dimensions, a multi-objective optimization problem minimizing the variation in engine performances and the total production cost of components is solved by a multi-objective genetic algorithm (MOGA). The comparisons using the newly developed Pareto front quality index (PFQI) indicate that MOGA generates the Pareto fronts of substantially higher quality, than SQP with varying weights on the objectives. The current design of the valvetrain is compared with two alternative designs on the obtained Pareto front, which suggested potential improvements.

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Robust Optimization to Reduce Dose Delivery Uncertainty by Potential Applicator Displacements for a Cervix Brachytherapy

10.21203/rs.2.22610/v1 ◽

2020 ◽

Author(s):

Byungdu Jo ◽

Kyeongyun Park ◽

Kwang Hyeon Kim ◽

Dongho Shin ◽

Young Kyung Lim ◽

...

Keyword(s):

Genetic Algorithm ◽

Cervical Cancer ◽

Robust Optimization ◽

External Beam Radiation ◽

Patient Specific ◽

Target Coverage ◽

Dose Delivery ◽

Beam Radiation ◽

Multi Objective ◽

Multi Objective Genetic Algorithm

Abstract Background Applicator displacement during brachytherapy treatment for cervical cancer leads to a drastic change in dose distribution. Hence, applicator displacement uncertainty is of significant relevance within the distribution of dose prescription. To minimize applicator displacement from patient movement during cervical cancer brachytherapy treatment, a multi-objective genetic algorithm was combined with a median absolute deviation (MAD) constrained robust optimization concept. Materials and methods To evaluate the feasibility of the robust optimization algorithm on applicator displacements, the clinically applied treatment plans of six tandem and ring (T&R) applicator cases for cervical cancer were included. All patients underwent magnetic resonance imaging (MRI) after the placement of the T&R applicator. The method considered multiple random scenarios reflecting the uncertainties in the dose delivered. For simplicity, the uncertainties in this proof-of-concept study were limited to potential applicator displacements. This problem is optimized by MAD-constrained robust optimization using a patient-specific multi-objective genetic algorithm. The proposed approach is then compared against the nominal (manual) plan strategies. Results All generated plans fulfilled EMBRACE protocol guidelines for all targets and organs at risk (OAR). MAD-constrained robust optimization provided not only excellent target coverage but also minimized doses to OAR. The nominal and robust plan equivalent dose in 2 Gy fractions (EQD2) of D98 for high-risk clinical target volume (HR-CTV) and rectum were 88.59, 55.29, and 84.84, 54.09, respectively. Furthermore, each standard deviation of D98 for HR-CTV and rectum reduced from ±1.0177 to ±0.9085 and ±0.4927 to ±0.4052, respectively. Conclusions Definitive dwell times and positions by the use of robust planned external beam radiation therapy plus brachytherapy (EBRT-BT) boost for cervical cancer were well tolerated. Using this robust strategy, the generated plans showed an increase in target coverage and minimized applicator displacement impact uncertainty on dose delivery.

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