Robust Optimization of an Automotive Valvetrain Using a Multiobjective Genetic Algorithm

2003 ◽  
Author(s):  
Emre Kazancioglu ◽  
Guangquan Wu ◽  
Jeonghan Ko ◽  
Stanislav Bohac ◽  
Zoran Filipi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Robust Optimization ◽  
Production Cost ◽  
Pareto Front ◽  
Total Production ◽  
Multi Objective ◽  
Engine Model ◽  
Multi Objective Genetic Algorithm ◽  
Current Design ◽  
Alternative Designs

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.

scholarly journals Construction Planning and Scheduling of a Renovation Project Using BIM-Based Multi-Objective Genetic Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 4716
Author(s):  
Pornpote Nusen ◽  
Wanarut Boonyung ◽  
Sunita Nusen ◽  
Kriengsak Panuwatwanich ◽  
Paskorn Champrasert ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Information Modeling ◽  
Construction Planning ◽  
Planning And Scheduling ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Building Information ◽  
Current Availability ◽  
Important Challenge

Renovation is known to be a complicated type of construction project and prone to errors compared to new constructions. The need to carry out renovation work while keeping normal business activities running, coupled with strict governmental building renovation regulations, presents an important challenge affecting construction performance. Given the current availability of robust hardware and software, building information modeling (BIM) and optimization tools have become essential tools in improving construction planning, scheduling, and resource management. This study explored opportunities to develop a multi-objective genetic algorithm (MOGA) on existing BIM. The data were retrieved from a renovation project over the 2018–2020 period. Direct and indirect project costs, actual schedule, and resource usage were tracked and retrieved to create a BIM-based MOGA model. After 500 generations, optimal results were provided as a Pareto front with 70 combinations among total cost, time usage, and resource allocation. The BIM-MOGA can be used as an efficient tool for construction planning and scheduling using a combination of existing BIM along with MOGA into professional practices. This approach would help improve decision-making during the construction process based on the Pareto front data provided.

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

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.

Sequencing the Reconfigurable Assembly Line with a Hybrid Multi-Objective Genetic Algorithm

2010 ◽  
Vol 160-162 ◽  
pp. 1545-1550
Author(s):  
Ming Hai Yuan ◽  
Huan Min Xu
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Rate Variation ◽  
Total Production ◽  
Setup Cost ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Scheduling Method ◽  
Adaptive Crossover ◽  
Reconfigurable Assembly

In order to solve the reconfigurable assembly line sequencing problem, a multi-objective optimization mathematical model is presented, which includes three practically important objectives. Such as minimizing the total utility work cost, minimizing the total production rate variation and minimizing reconfigurable setup cost are considered. A scheduling method for reconfigurable assembly line is proposed based on Pareto multi-objective genetic algorithm, In order to ensure the group’s variety, prevent the premature convergence problem and enhance the globe-optimization capability, some key technologies such as population ranking method, Niche technique are applied. The adaptive crossover and mutation probabilities methods are developed. The computational results show that the proposed hybrid algorithm finds solutions with better quality especially in the case of large-sized problems.

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