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"Truck system."
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Simulation Optimization of Shovel‐Truck System in Open‐Pit Mines Considering Rockmass Parameters
The shovel‐truck system remains a popular method for overburden removal and mineral excavation in open‐pit mines, needing rigorous logistical management to achieve required productivity levels and maximize resource utilization. Fixed truck assignment (FTA) models represent a prevalent method for truck allocation in open‐pit mining, owing to their simplified fleet operational management. However, existing FTA models often overlook the simultaneous minimization of both trucks’ waiting time and shovels’ idle time. Consequently, these oversights lead to suboptimal allocation of trucks to shovels, resulting in either trucks queuing or shovels idling while awaiting trucks. Such inefficiencies contribute to fleet underutilization and increased fuel costs. To tackle the above issue, this research introduces a novel truck dispatching rule, MFTA, which integrates geotechnical parameters and excavating equipment performance to optimize truck allocation in open‐pit mining. Geotechnical parameters across various rock and soil formations reveal significant variability, influencing shovel performance assessed through the total loading time (TLT) indicator. Utilizing TLT and travel times of loaded and empty trucks, the study determines the optimal number of fixed trucks allocated to each shovel by minimizing the total waiting time (TWT). A case study conducted in an open‐pit coal mine in Thar, Pakistan, validates the approach, demonstrating that adjusting truck allocations based on TLT significantly reduces operational inefficiencies and enhances productivity. The findings highlight the effectiveness of this method in improving overall operational efficiency and economics in open‐pit mining. Integrating real‐time data and advanced simulation techniques, this research enhances the competitiveness and sustainability of mining operations. These outcomes are particularly relevant for mining professionals aiming to optimize mining operations for improved efficiency and sustainability.
Journal Article
A Genetic Algorithm Model for Short-Term Planning and Quality Management in Open-Pit Mining
Operational (short-term) planning in open-pit mining is a critical phase for ensuring grade control and production stability, particularly in complex geological environments. While long-term plans define the strategic goals, they often overlook shift-level variability and operational constraints of a shovel-truck system. This paper presents an optimization model based on a genetic algorithm (GA) for shift-by-shift operational planning. The model integrates real-world technological constraints of the equipment used, including fixed shift capacity (2000 t) and various constraints characteristic of active mining locations. The fitness function is designed to minimize the deviations from the targeted quality range for iron (Fe: 47–50%) and silica (SiO2: ≤11%), while ensuring rational use of mineral reserves. The model was tested on a case study involving eight limonite ore open pits over a period of one production year (1000 shifts). The results show that the GA-generated plan reaches quality requirements in 98.1% of all shifts. This GA approach provides more balanced mining operations and confirms and ensures the achievement of goals from long-term plans, reducing the reliance on large-scale homogenization stockpiles. The developed tool is implemented in Excel/VBA and offers a practical framework for mining engineers to work with.
Journal Article
A new energy-saving and convenient dump truck system
Traditional lift dump trucks complete unloading by raising the center of gravity of the carriage, which often requires too much extra energy input and is inefficient. This paper improves the structure of the traditional dump truck and proposes a new dump truck system that integrates energy saving and convenience. The system as a whole includes conveyor belt system, driving system, automatic turntable system and solar thin film battery system. Based on theoretical calculations and modeling, the transmission efficiency and energy saving of the traditional dump truck and the new system are compared. The results show that the new system has higher transmission efficiency, lower energy consumption, and can achieve different directions through automatic rotation unloading, which also very well verified the superiority of the system.
Journal Article
Impact on yard efficiency of a truck appointment system for a port terminal
Port terminals consist of two interfaces for transferring cargo among transport modes: (1) the seaside or quayside interface and (2) the landside interface. At the seaside interface, cargo is loaded and unloaded from the vessels and stored temporarily at the yard. Landside operations consist of receiving and dispatching cargo from external trucks and rail. The increasing volumes of international trade are demanding more efficient cargo handling throughout the port logistic chain and coordination with the hinterland, hence attracting more attention from both practitioners and researchers on the landside interface of ports. Due to the high variability of truck arrivals with a significant concentration at peak hours, congestion at the access gates of ports and an unbalanced utilization of the resources occur. Truck appointment systems (TAS) have already been implemented in some ports as a coordination mechanism to reduce congestion at ports, balance demand and capacity, and reduce truck turnaround times. Based on the current situation faced by the Port of Arica, Chile, this paper aims to analyze potential configurations of a TAS and evaluate its impacts on yard operations, specifically in the reduction of container rehandles, as well as truck turnaround times. For this, a discrete-event simulation model and a heuristic procedure are proposed and experimentation is performed using historical data from the port terminal. Results indicate that implementing a TAS may significantly benefit yard operations in terms of reducing container rehandles as well as truck waiting times.
Journal Article
Method of Shaping Loading-and-Transportation System in Deep Open Pit Complex Ore Mines
The article presents a procedure to select loading and transportation machines for an open pit complex ore mine. The choice of a shovel–dump truck production system is validated using a statistical testing method (Monte Carlo technique). Stop-watch readings allowed relating the productivity of the production system, degree of ore fragmentation and content of oversizes; the soundness of the choice of the production system based on the revealed criterion was proved. Using the law of the Palm flows, the authors determine the number and sequence of dump trucks for loading in a one-server system.
Journal Article
Improving Container Port Efficiency: A Data‐Driven Model for Optimizing Truck Arrival Appointments Through Distributionally Robust Optimization
The irregular arrival patterns of container trucks at ports have a substantial impact on logistics operations’ efficiency, resulting in congestion during peak hours and unused port capacity during idle times. Implementing a truck appointment system (TAS) is vital to address this issue effectively. This paper suggests enhancing the TAS by adopting a data‐driven approach using terminal gate data to understand the intricate and uncertain relationship between truck arrival patterns and port operational efficiency. Insights gained from these data are utilized to develop a distributionally robust optimization (DRO) model. This model provides an exact solution for optimizing the appointment quota plan of TASs, thereby improving port efficiency and addressing operational challenges. Compared to existing methods, this approach does not heavily rely on theoretical assumptions concerning the cooperation mechanisms among trucks, yard equipment, quayside equipment, and other facilities and fully considers the complex uncertainties in truck arrivals. Furthermore, to examine the effectiveness of the proposed model, a case study is conducted at Yan Port, China, aiming to achieve practical results. The numerical experiments comparing its performance with the conventional robust optimization (RO) model confirm the superiority of the proposed DRO model in minimizing the total truck turnaround time within the terminal and overall time expenses. This superiority stems from its integration of the respective advantages of stochastic optimization (SO) and traditional RO methods. By optimizing the appointment quota plan in this manner, it achieves a balanced distribution of truck arrivals, showcasing its significant potential to enhance port logistics efficiency.
Journal Article
Optimization for a Multi-Constraint Truck Appointment System Considering Morning and Evening Peak Congestion
Gate and yard congestion is a typical type of container port congestion, which prevents trucks from traveling freely and has become the bottleneck that constrains the port productivity. In addition, urban traffic increases the uncertainty of the truck arrival time and additional congestion costs. More and more container terminals are adopting a truck appointment system (TAS), which tries to manage the truck arrivals evenly all day long. Extending the existing research, this work considers morning and evening peak congestion and proposes a novel approach for multi-constraint TAS intended to serve both truck companies and container terminals. A Mixed Integer Nonlinear Programming (MINLP) based multi-constraint TAS model is formulated, which explicitly considers the appointment change cost, queuing cost, and morning and evening peak congestion cost. The aim of the proposed multi-constraint TAS model is to minimize the overall operation cost. The Lingo commercial software is used to solve the exact solutions for small and medium scale problems, and a hybrid genetic algorithm and simulated annealing (HGA-SA) is proposed to obtain the solutions for large-scale problems. Experimental results indicate that the proposed TAS can not only better serve truck companies and container terminals but also more effectively reduce their overall operation cost compared with the traditional TASs.
Journal Article
Optimization of truck appointments in container terminals
Truck appointment has proved to be an efficient tool in reducing congestion at container terminals. To make a reasonable appointment quota plan, it is necessary to take terminal operations into consideration. We develop a novel approach (model) for optimizing a truck appointment system with the objective of decreasing external trucks’ waiting times, at the gate and yard, and internal trucks’ waiting times at the yard. The vacation queuing model is used to describe the coordinated service process of yard cranes. Based on non-stationary queuing theory, truck waiting times are estimated more accurately. Numerical experiments are conducted to illustrate the validity of the model and algorithm. Results show that the model reflects the characteristics of the service process of yard cranes and it improves the calculation accuracy of the truck waiting time.
Journal Article
Open-Pit Mine Truck Dispatching System Based on Dynamic Ore Blending Decisions
In the production process of open-pit mines, trucks are applied in the production process of open-pit mines for transporting ores and rocks. Most open-pit mines are equipped with dozens of trucks. It is important to plan the dispatch of trucks in the production process so that the transportation process can be the shortest in distance, the lowest in cost, and the most efficient. At present, many open-pit mining enterprises have realized the use of dispatching systems to schedule trucks to complete production tasks. However, these methods are mostly designed to deploy trucks to reduce production costs without considering the blending problem of the selected ores, and therefore it cannot meet the dual need of ore blending and dispatching. In order to solve the above technical problems and meet the actual needs of the current open-pit mine for ore blending and dispatching, this paper proposes an open-pit mine truck dispatching system based on dynamic ore blending decisions, supported by a 4G/5G wireless network, Beidou positioning, and Internet of Things technology, which can not only realize the optimized truck dispatching of open-pit mine production, but also meet the requirements of downstream concentrators for ore dressing grade. The system has been applied in the Ansteel Group QIDASHAN mine for one year. The proportion of trucks dispatched through the system reached more than 70%. The trucks’ capacity were upgraded from 3.79 to 4 million ton km per set per year, and the efficiency was improved by 5.5%. The limitations of the proposed system and method mainly include the possibility of inaccurate measurement of ore output and the lack of combination with unmanned driving.
Journal Article