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.

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.

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