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Optimizing time, cost, and carbon in construction: grasshopper algorithm empowered with tournament selection and

Vu Hong Son Pham¹, Phuoc Vo Duy², Nghiep Trinh Nguyen Dang¹

¹Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM), Ho Chi Minh City, Vietnam.

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|December 14, 2023

Summary

This study introduces eMOGOA, an enhanced optimization algorithm, to effectively manage time, cost, and carbon dioxide emissions in construction projects. The new method demonstrates superior performance in balancing these critical project management factors.

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Area of Science:

Construction Management
Optimization Algorithms
Environmental Engineering

Background:

Construction projects face unique challenges including time and cost constraints.
Environmental impact is a significant concern in construction, yet often overlooked by project managers.
Balancing project time, cost, and environmental emissions is a complex multi-objective problem.

Purpose of the Study:

To introduce an improved multi-objective grasshopper optimization algorithm (eMOGOA) for construction project management.
To address the trade-off problems between time, cost, and carbon dioxide emissions (TCCP).
To enhance the performance of existing optimization algorithms in construction project management.

Main Methods:

Developed an enhanced multi-objective grasshopper optimization algorithm (eMOGOA).
Integrated tournament selection (TS) and opposition-based learning (OBL) into MOGOA.
Applied and evaluated eMOGOA using a case study with 29 construction activities.

Main Results:

eMOGOA demonstrated superior performance compared to MODA, MOSMA, MOALO, and MOGOA.
The algorithm effectively tackled time, cost, and carbon dioxide emission trade-off problems (TCCP).
The case study validated the efficacy of eMOGOA in construction project management.

Conclusions:

eMOGOA is an efficient and relevant novel methodology for construction project management.
The algorithm provides a viable solution for balancing time, cost, and environmental considerations.
This research highlights the potential of advanced optimization techniques in sustainable construction.