TY - GEN
T1 - Brute-Force Optimization Workflow with Parallel Computing for Building Lifecycle Analysis
T2 - 2025 Annual Modeling and Simulation Conference, ANNSIM 2025
AU - Yang, Yang
AU - Cimillo, Marco
N1 - Publisher Copyright:
© 2025 Society for Modeling & Simulation International (SCS).
PY - 2025
Y1 - 2025
N2 - To address the limitations of traditional optimization workflows in lifecycle analysis (LCA) during early design, this study proposes a Brute-Force Optimization (BFO) workflow with parallel simulation (PS) and data management. Using Python scripting and Grasshopper, the workflow automates design generation and PS and integrates carbon data. A comparative analysis with Multi-Objective Optimization (MOO) using Evolutionary Algorithms (EA) shows that within the same time frame, the BFO workflow optimized 2,187 design combinations, compared to 871 by MOO. The proposed workflow reduced total carbon emissions by 12% Energy Use Intensity (EUI) by 17%, and cost by 17%, offering greater flexibility in design tradeoffs. These results demonstrate the workflow's potential for more comprehensive insights into sustainable building design optimization.
AB - To address the limitations of traditional optimization workflows in lifecycle analysis (LCA) during early design, this study proposes a Brute-Force Optimization (BFO) workflow with parallel simulation (PS) and data management. Using Python scripting and Grasshopper, the workflow automates design generation and PS and integrates carbon data. A comparative analysis with Multi-Objective Optimization (MOO) using Evolutionary Algorithms (EA) shows that within the same time frame, the BFO workflow optimized 2,187 design combinations, compared to 871 by MOO. The proposed workflow reduced total carbon emissions by 12% Energy Use Intensity (EUI) by 17%, and cost by 17%, offering greater flexibility in design tradeoffs. These results demonstrate the workflow's potential for more comprehensive insights into sustainable building design optimization.
KW - brute-force optimization
KW - evolutionary algorithm
KW - lifecycle analysis
KW - multi-objective optimization
KW - parallel simulation
UR - https://www.scopus.com/pages/publications/105015995543
M3 - Conference Proceeding
AN - SCOPUS:105015995543
T3 - ANNSIM 2025 - Annual Modeling and Simulation Conference 2025
BT - ANNSIM 2025 - Annual Modeling and Simulation Conference 2025
A2 - Ferrero-Losada, Samuel
A2 - Abdelnabi, Ahmad Bany
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 May 2025 through 29 May 2025
ER -