Improved Multi-Objective Beluga Whale Optimization Algorithm for Truck Scheduling in Open-Pit Mines

Big data and artificial intelligence have promoted mining innovation and sustainable development, and the transportation used in open-pit mining has increasingly incorporated unmanned driving, real-time information sharing, and intelligent algorithm applications. However, the traditional manual sche...

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Vydáno v:Sustainability Ročník 16; číslo 16; s. 6939
Hlavní autoři: Zhang, Pengchao, Liu, Xiang, Yi, Zebang, He, Qiuzhi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.08.2024
Témata:
Algorithms
Artificial intelligence
Automobile safety
Carbon
Case studies
Efficiency
Environmental protection
Equipment and supplies
Genetic algorithms
Integer programming
Linear programming
Management
Mathematical optimization
Mineral industry
Mines
Mining
Mining industry
Operating costs
Optimization algorithms
Scheduling
Scheduling (Management)
Technology application
Traffic congestion
Trucks
Working hours
China
ISSN:2071-1050, 2071-1050
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Shrnutí:Big data and artificial intelligence have promoted mining innovation and sustainable development, and the transportation used in open-pit mining has increasingly incorporated unmanned driving, real-time information sharing, and intelligent algorithm applications. However, the traditional manual scheduling used for mining transportation often prioritizes output over efficiency and quality, resulting in high operational expenses, traffic jams, and long lines. In this study, a novel scheduling model with multi-objective optimization was created to overcome these problems. Production, demand, ore grade, and vehicle count were the model’s constraints. The optimization goals were to minimize the shipping cost, total waiting time, and ore grade deviation. An enhanced multi-objective beluga whale optimization (IMOBWO) algorithm was implemented in the model. The algorithm’s superior performance was demonstrated in ten test functions, as well as the IEEE 30-bus system. It was enhanced by optimizing the population initialization, improving the adaptive factor, and adding dynamic domain perturbation. The case analysis showed that, in comparison to the other three conventional multi-objective algorithms, IMOBWO reduced the shipping cost from 7.65 to 0.84%, the total waiting time from 35.7 to 7.54%, and the ore grade deviation from 14.8 to 3.73%. The implementation of this algorithm for truck scheduling in open-pit mines increased operational efficiency, decreased operating costs, and advanced intelligent mine construction and transportation systems. These factors play a significant role in the safety, profitability, and sustainability of open-pit mines.
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content type line 14
ISSN:2071-1050
2071-1050
DOI:10.3390/su16166939