Abstract
With the maturity of the Internet of Things, 5G communication, big data and artificial intelligence technologies, open-pit mine intelligent transportation systems based on unmanned vehicles has become a trend in smart mine construction. Traditional open-pit mine transportation systems rely on human power for command, which often causes vehicle delay and congestion. The operation of unmanned vehicles in an open pit mine relies on many sensors. Using big data from the sensors, we optimize vehicle paths and build an efficient intelligent transportation system. Based on large amounts of data, such as unmanned vehicle GPS data, vehicle equipment information, production plan data, etc., with the goal of reducing vehicle transportation costs, total unmanned vehicle delay time, and ore content fluctuation rate, a multi-objective intelligent scheduling model for open-pit mine unmanned vehicles was established, and it is aligned with actual open pit mine production. Next, we use artificial intelligence algorithms to solve the scheduling problem. To improve the convergence, distribution and diversity of the classical fast non-dominated sorting genetic algorithm (NSGA-II) to solve constrained high-dimensional multi-objective problems, we propose a decomposition-based constrained dominance principle genetic algorithm (DBCDP-NSGA-II), retaining feasible and non-feasible solutions in sparse areas, and compare it with four other commonly-used multi-objective optimization algorithms. Simulation analysis shows our algorithm provides the best overall performance results of the multi-objective models. Furthermore, we apply intelligent scheduling models and optimization algorithms to mining practice and obtain new truck operation routes and schedules, reducing truck operation costs by 18.2%, truck waiting time by 55.5%, and ore content fluctuation by 40.3%. For open-pit mine unmanned transportation, the approach provides a variety of optimized solutions for minimum transportation costs, minimum waiting time, minimum ore content fluctuation rate, and a balance of the three indicators. Through an artificial intelligence algorithm, this study realizes intelligent unmanned vehicle path planning and improves the operation efficiency of open-pit mine intelligent transportation systems.
Highlights
In the first phase, niched based on crowding differential evolution (NCDE) is used,and construct the diversity index based on Gaussian kernel function to maintain the diversity of the population to achieve the balance of convergence and diversity
INTELLIGENT TRANSPORTATION SCHEDULING SYSTEM APPLICATIONS The system we have proposed has been applied at the Luoyang Molybdenum Sandaozhuang Open Pit Mine, which is one of the three largest molybdenum ore fields in the world; it is located in Luoyang City, Henan Province, China
WORK As the core content of mine production management, traditional open-pit mine truck scheduling is fraught with problems such as long waiting time for vehicles in queue and a single optimization goal
Summary
Open pit mines are constrained by factors such as multiple pieces of working face equipment, harsh environments, and. Wan et al [25] proposed an optimization model that minimizes the fuel consumption of dump vehicles and scrapers in open pits to meet the loading and unloading requirements of dump sites These studies face the scheduling problem from a single perspective and establish models to reduce queue time and grade fluctuations. Considering only a single target makes it difficult to dynamically allocate and optimize the vehicles For this problem, using multi-objective optimization, Coelho et al [32] proposed three multi-objective heuristic algorithms: 2PPLS-VNS, MOVNS, and NSGA-II, which can be applied to the open-pit mining dynamic truck allocation problem. The simulation results show that our proposed multi-objective scheduling model and intelligent solving algorithm can help reduce transportation costs, waiting time and ore grade fluctuations.
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