Simulation-Driven Multiobjective Optimization of Carbon Emissions, Duration, and Cost of Earthwork Construction

Abstract Decarbonizing construction is vital to achieving global net-zero goals, yet research on the carbon footprint of earthmoving operations is limited. Current estimation methods rely on crude approximations and overlook key operational factors, hindering efforts to optimize these activities environmentally. The goal of this research is to develop and test an advanced modeling framework that quantifies the carbon footprint of complex earthmoving operations, alongside their associated cost and duration, while identifying optimal operational parameters (such as hauling route, equipment fleet size, and equipment capacity) to minimize emissions, duration, and cost. The model is developed on a discrete-event simulation platform with geographic information system (GIS) integration to ensure that real-world operational complexities (such as stochastic travel times and spatial considerations) are accurately represented. The model was tested on a case study of excavation and dumping activities for a construction project, where different route scenarios, equipment capacities, and equipment spread arrangements were tested. The model demonstrated substantial quantitative improvements compared to the baseline, with optimized scenarios achieving up to 54% cost reduction, 60% duration reduction, and nearly 50% CO 2 emission reduction. Notably, the findings reveal strong synergy between cost and emissions reduction, where economically optimal solutions frequently coincide with minimum-emission configurations due to reduced fuel consumption and idle time. The research advances existing knowledge by replacing simplified CO 2 estimation methods with higher fidelity simulation of earthmoving operations that accounts for equipment characteristics, idle time, stochastic behavior, and real hauling routes. By integrating discrete-event simulation with GIS, it introduces a novel approach that captures temporal uncertainty and spatial complexity, extending construction simulation from cost and productivity analysis to accurate operational carbon assessment. The resulting model is generalizable and adaptable across projects, positioning the research as a practical link between academic modeling and real-world sustainable construction planning.