Optimization on Electric Construction Machinery Considering Time‐of‐Use Electricity Price Based on the Improved Crested Porcupine Optimizer Algorithm

ABSTRACT Optimization scheduling plays a pivotal role in construction projects, significantly influencing both the overall project schedule and its efficiency. This study focuses on optimizing the scheduling of electric highway engineering projects within roadbed construction. The research considers multiple earthmoving processes and optimizes the working time of each piece of equipment, taking into account its capacity and speed within a limited working week. The study is further contextualized by the use of regional time‐of‐use (TOU) electricity pricing. A sophisticated optimization model is developed to simulate optimal machinery operation, striking a balance between energy consumption and work efficiency. This paper introduces an innovative algorithm, the improved crested porcupine optimizer (ICPO), which incorporates Latin hypercube sampling for population initialization. To enhance algorithmic effectiveness, a combined strategy of parallel and compact processing is employed. This approach reduces the number of iterations required for optimization and consequently lowers energy consumption. Rigorous analysis and comparison with existing algorithms demonstrate that ICPO significantly reduces both iteration count and financial expenditure. Simulation results validate the accuracy and practicality of the proposed model and algorithm, showing a reduction of over 7% in both engineering time and energy consumption.

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Publication Details

Journal
Energy Science & Engineering
Published
2025-03-30
DOI
https://doi.org/10.1002/ese3.70080
Primary Topic
BIM and Construction Integration
Type
article
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article

Optimization on Electric Construction Machinery Considering Time‐of‐Use Electricity Price Based on the Improved Crested Porcupine Optimizer Algorithm

Lei Zhang, Dawei Wang, Bo Gao
Energy Science & Engineering
BIM and Construction Integration
article

Optimization on Electric Construction Machinery Considering Time‐of‐Use Electricity Price Based on the Improved Crested Porcupine Optimizer Algorithm

Lei Zhang, Dawei Wang, Bo Gao
article en

Abstract

ABSTRACT Optimization scheduling plays a pivotal role in construction projects, significantly influencing both the overall project schedule and its efficiency. This study focuses on optimizing the scheduling of electric highway engineering projects within roadbed construction. The research considers multiple earthmoving processes and optimizes the working time of each piece of equipment, taking into account its capacity and speed within a limited working week. The study is further contextualized by the use of regional time‐of‐use (TOU) electricity pricing. A sophisticated optimization model is developed to simulate optimal machinery operation, striking a balance between energy consumption and work efficiency. This paper introduces an innovative algorithm, the improved crested porcupine optimizer (ICPO), which incorporates Latin hypercube sampling for population initialization. To enhance algorithmic effectiveness, a combined strategy of parallel and compact processing is employed. This approach reduces the number of iterations required for optimization and consequently lowers energy consumption. Rigorous analysis and comparison with existing algorithms demonstrate that ICPO significantly reduces both iteration count and financial expenditure. Simulation results validate the accuracy and practicality of the proposed model and algorithm, showing a reduction of over 7% in both engineering time and energy consumption.

Energy Science & EngineeringVol. 13(6)
Wuhan Municipal Engineering Design & Research Institute (CN), CCCC Highway Consultants (China) (CN), Wuhan University of Science and Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 94%
BIM and Construction Integration
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Optimization on Electric Construction Machinery Considering Time‐of‐Use Electricity Price Based on the Improved Crested Porcupine Optimizer Algorithm — Lei Zhang, Dawei Wang, et al. · Energy Science & Engineering (2025) | TGRS Research Map | TGRS