CN117634772A - Strip mine unmanned truck scheduling method considering truck performance - Google Patents
Strip mine unmanned truck scheduling method considering truck performance Download PDFInfo
- Publication number
- CN117634772A CN117634772A CN202311456356.1A CN202311456356A CN117634772A CN 117634772 A CN117634772 A CN 117634772A CN 202311456356 A CN202311456356 A CN 202311456356A CN 117634772 A CN117634772 A CN 117634772A
- Authority
- CN
- China
- Prior art keywords
- truck
- point
- open
- unloading
- particle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0838—Historical data
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Educational Administration (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
本发明公开了一种考虑卡车性能的露天矿无人驾驶卡车调度方法,包括以下步骤;步骤1:露天矿卡车运行状况数据库的建立;步骤2:卡车运行效率预测模型的建立;步骤3:根据露天矿卡车运行状况数据库和卡车运行效率预测模型进行多目标露天矿无人驾驶卡车调度优化模型的建立;步骤4:采用多目标粒子群算法求解所述步骤3建立的调度优化模型,用于最终调度方案的寻优。利用矿山生产的历史数据建立卡车运输效率的预测模型,并将预测结果带入矿车实时调度优化的求解模型,得到更贴近实际生产的调度方案,从而降低生产中的运输成本。
The invention discloses a method for dispatching unmanned trucks in open-pit mines considering truck performance, which includes the following steps; Step 1: Establishment of open-pit mine truck operating status database; Step 2: Establishment of truck operating efficiency prediction model; Step 3: According to The open-pit mine truck operating status database and the truck operating efficiency prediction model are used to establish a multi-objective open-pit mine driverless truck scheduling optimization model; Step 4: Use the multi-objective particle swarm algorithm to solve the scheduling optimization model established in step 3 for the final Optimization of scheduling plans. Use historical data of mine production to establish a prediction model for truck transportation efficiency, and bring the prediction results into the solution model for real-time mine truck scheduling optimization to obtain a scheduling plan that is closer to actual production, thereby reducing transportation costs in production.
Description
技术领域Technical field
本发明涉及露天矿山作业调度方法技术领域,具体涉及一种考虑卡车性能的露天矿无人驾驶卡车调度方法。The present invention relates to the technical field of open-pit mine operation scheduling methods, and in particular to a method for dispatching unmanned trucks in open-pit mines that considers truck performance.
背景技术Background technique
露天矿生产计划实施是通过对运输设备在爆堆和卸矿区之间的调配来完成的。矿车运输的主要任务是将矿石从爆堆运输到卸矿站或储藏场。因此,优化矿车的调度,提高矿车的运输效率对降低生产成本、提高生产效率有着重大意义。矿车调度的优化也是露天矿系统优化中的重要环节。The implementation of the open pit mine production plan is completed by allocating transportation equipment between the blasting and unloading areas. The main task of mine cart transportation is to transport ore from the blast pile to the unloading station or storage yard. Therefore, optimizing the scheduling of mine cars and improving the transportation efficiency of mine cars are of great significance to reducing production costs and improving production efficiency. The optimization of mine car scheduling is also an important link in the optimization of open pit mine systems.
传统的矿车调度优化通常认为矿车的性能及运输效率是处于同一水平且不变的。但是这一点并不符合现实中生产实际情况。在实际生产中,每一台班工作的卡车的型号、载重、公里数、发动机状况、轮胎磨损情况以及维修保养情况都不尽相同。这些因素造成了每一辆卡车的运输效率都不同。因此在矿车的调度优化中应该考虑每辆卡车不同的性能,这样才能使优化结果更接近于实际生产情况。这要求建立一种新的考虑卡车性能的露天矿无人驾驶卡车调度方法。对露天矿生产中有关矿车情况的历史数据进行分析并建立预测模型,这对于建立这种考虑卡车性能的新的调度方法具有建设意义。Traditional mine car scheduling optimization usually assumes that the performance and transportation efficiency of mine cars are at the same level and unchanged. But this is not consistent with the actual production situation in reality. In actual production, the trucks working in each shift have different models, loads, kilometers, engine conditions, tire wear conditions, and maintenance conditions. These factors cause each truck to have different transport efficiency. Therefore, the different performance of each truck should be considered in the scheduling optimization of mine trucks, so that the optimization results can be closer to the actual production situation. This requires establishing a new method for autonomous truck dispatching in open pit mines that considers truck performance. Analyzing historical data about mine truck conditions in open-pit mine production and establishing a predictive model are of constructive significance for establishing this new scheduling method that considers truck performance.
发明内容Contents of the invention
为了克服以上现有技术存在的缺陷,本发明的目的在于提供一种考虑卡车性能的露天矿无人驾驶卡车调度方法,利用矿山生产的历史数据建立卡车运输效率的预测模型,并将预测结果带入矿车实时调度优化的求解模型,得到更贴近实际生产的调度方案,从而降低生产中的运输成本。In order to overcome the shortcomings of the above existing technologies, the purpose of the present invention is to provide a method for dispatching unmanned trucks in open-pit mines that considers truck performance, uses historical data of mine production to establish a prediction model for truck transportation efficiency, and brings the prediction results to the The solution model for real-time scheduling optimization of mine cars can obtain a scheduling plan that is closer to actual production, thereby reducing transportation costs in production.
为了实现上述目的,本发明采用的技术方案是:In order to achieve the above objects, the technical solution adopted by the present invention is:
一种考虑卡车性能的露天矿无人驾驶卡车调度方法,包括以下步骤;A method for dispatching driverless trucks in open pit mines considering truck performance, including the following steps;
步骤1:露天矿卡车运行状况数据库的建立;Step 1: Establishment of open-pit mine truck operating status database;
步骤2:卡车运行效率(速度)预测模型的建立;Step 2: Establishment of truck operating efficiency (speed) prediction model;
步骤3:根据露天矿卡车运行状况数据库和卡车运行效率(速度)预测模型进行多目标露天矿无人驾驶卡车调度优化模型的建立;Step 3: Establish a multi-objective open-pit mine driverless truck scheduling optimization model based on the open-pit mine truck operating status database and truck operating efficiency (speed) prediction model;
步骤4:采用多目标粒子群算法求解所述调度优化模型,用于最终调度方案的寻优。Step 4: Use the multi-objective particle swarm algorithm to solve the scheduling optimization model for optimizing the final scheduling solution.
所述步骤1具体为:收集露天矿生产中每辆卡车的卡车载重、已完成公里数、发动机状况、轮胎磨损情况、维修保养情况以及每台班平均速度的历史数据,建立露天矿卡车运行状况数据库;制定露天矿卡车运行状况数据统计表,统计露天矿卡车运行状况的历史数据。The specific step 1 is: collect the historical data of the truck load, completed kilometers, engine condition, tire wear, repair and maintenance status and average speed of each shift of each truck in the open-pit mine production, and establish the operating status of the open-pit mine trucks. Database; develop a statistical table of operating status data of open-pit mine trucks to collect historical data on the operating status of open-pit mine trucks.
所述步骤2具体为:通过对影响卡车运输性能因素的分析,建立了以卡车载重、已行驶公里数、发动机故障次数、轮胎已使用公里数、日常维修次数五个影响因素为输入参数,以车辆每台班平均速度为输出参数的预测模型。The specific step 2 is: through the analysis of factors affecting truck transportation performance, five influencing factors are established, including truck load, kilometers traveled, number of engine failures, kilometers used by tires, and number of daily maintenance times. A prediction model with the average vehicle speed per shift as the output parameter.
所述步骤3中,根据露天矿实际生产运输过程,以最小化卡车等待时间、最小化卡车等待时间、最小化剩余产量、最小化矿石品位偏差为目标,建立露天矿无人驾驶卡车调度优化模型;所述露天矿无人驾驶卡车调度优化模型包括目标函数和约束条件。In step 3, based on the actual production and transportation process of the open pit mine, with the goals of minimizing truck waiting time, minimizing truck waiting time, minimizing remaining production, and minimizing ore grade deviation, an optimization model for driverless truck dispatching in the open pit mine is established. ; The open-pit mine driverless truck scheduling optimization model includes an objective function and constraints.
目标函数具体为:The objective function is specifically:
最小化卡车运输成本:Minimize trucking costs:
卡车的运输成本由卡车重载从装载点到卸矿点的运输成本,卡车空载从卸载点返回装载点以及空载往返加油点的运输成本构成;The transportation cost of a truck consists of the transportation cost of a heavy truck from the loading point to the unloading point, the transportation cost of an empty truck from the unloading point to the loading point, and the transportation cost of an empty truck to and from the refueling point;
最小化卡车等待时间:Minimize truck waiting time:
卡车的等待时间由卡车班次工作时间减去卡车实际运行时间构成,实际运行时间包括装卸点之间的运行时间、装卸车时间、来往加油站时间以及加油时间;The waiting time of a truck is composed of the truck shift working time minus the actual running time of the truck. The actual running time includes the running time between loading and unloading points, loading and unloading time, time to and from the gas station, and refueling time;
最小化剩余产量:Minimize remaining production:
为保证采矿点一个班次生产的矿石能尽可能的被运输,即安排的运力与产能尽可能相近,要求产量减去运量的绝对值最小;In order to ensure that the ore produced in one shift at the mining site can be transported as much as possible, that is, the arranged transportation capacity is as close as possible to the production capacity, the absolute value of the output minus the transportation volume is required to be minimum;
最小化矿石品位偏差:Minimize ore grade deviation:
各个卸载点处的矿石品位偏差之和最小;The sum of the ore grade deviations at each unloading point is the smallest;
约束条件具体为:The constraints are specifically:
卸载点的生产能力约束:Production capacity constraints at the unloading point:
运往每个卸载点的矿石总量不得超过该卸载点的最大生产能力;The total amount of ore shipped to each unloading point shall not exceed the maximum production capacity of that unloading point;
卸载点的产量计划要求:Production planning requirements for unloading points:
每个卸载点卸载的矿石总量至少要满足该卸载点的生产需求;The total amount of ore unloaded at each unloading point must at least meet the production demand of that unloading point;
装载点的生产能力约束:Production capacity constraints for load points:
每个装载点装载的矿石总量不得超过该装载点的最大生产能力;The total amount of ore loaded at each loading point shall not exceed the maximum production capacity of that loading point;
卸载点的矿石品位要求:Ore grade requirements at the unloading point:
每个卸载点卸载的矿石品位偏差不得超过最大品位偏差;The grade deviation of ore unloaded at each unloading point shall not exceed the maximum grade deviation;
车流连续性约束,保证每个装/卸载点的出入车流量相等;Traffic flow continuity constraints ensure that the incoming and outgoing traffic flow of each loading/unloading point is equal;
剩余油量约束:Remaining fuel constraints:
无人驾驶卡车不再像传统卡车一样,在行驶过程中有司机能够随时关注剩余油量,因此在对无人驾驶卡车进行车流规划调度时,需要监控其剩余油量。其具体表现为每辆卡车的剩余油量不得低于K,以便返回加油点加油。其中,最小剩余油量K=卸载点到装载点最大用油量+装载点到卸载点最大用油量+卸载点到加油点O的最大用油量;Self-driving trucks are no longer like traditional trucks. There is a driver who can keep an eye on the remaining fuel level at any time while driving. Therefore, when planning and dispatching driverless trucks for traffic flow, it is necessary to monitor their remaining fuel levels. Its specific manifestation is that the remaining fuel quantity of each truck must not be less than K in order to return to the refueling point for refueling. Among them, the minimum remaining oil amount K = the maximum oil consumption from the unloading point to the loading point + the maximum oil consumption from the loading point to the unloading point + the maximum oil consumption from the unloading point to the refueling point O;
卡车运输次数要求,卡车运输的次数必须为正整数;The number of truck transportation requirements, the number of truck transportation must be a positive integer;
Xijkl,Yijkl∈{0,1,2,3...}X ijkl ,Y ijkl ∈{0,1,2,3...}
冲突避让状况下的约束条件;Constraints in conflict avoidance situations;
当两辆卡车同时在某个装载点或卸载点时,卡车的路线会出现冲突,对卡车的运行成本和排队时间造成较大的影响。为了尽可能的降低由卡车任务冲突造成的影响,需要在初始状态的约束前提之下,对卡车设定优先级。考虑到满载的卡车会比空载的卡车运行成本更高,因此优先满载卡车先行。此外,优先级的判定同样适用于两辆卡车在同一路口相会时,空载的无人驾驶卡车应当避让。When two trucks are at a certain loading or unloading point at the same time, the truck routes will conflict, which will have a greater impact on the truck's operating costs and queuing time. In order to reduce the impact caused by truck task conflicts as much as possible, it is necessary to set priorities for trucks under the constraints of the initial state. Given that fully loaded trucks are more expensive to operate than empty trucks, priority is given to fully loaded trucks. In addition, the priority determination also applies to when two trucks meet at the same intersection, and the unloaded driverless truck should give way.
卡车故障状况下的约束条件;Constraints in truck failure conditions;
一旦卡车故障无法行驶,则该卡车应及时退出车流规划调度系统。因此,当某辆无人驾驶卡车向总调度中心预警后,应当立即将该卡车排除在系统外,同时降低该卡车所在的路径、装载点或卸载点的优先等级,以便及时调整规划调度方案,完成企业既定的目标;Once a truck fails and cannot move, the truck should exit the traffic flow planning and dispatching system in a timely manner. Therefore, when a driverless truck alerts the general dispatch center, the truck should be immediately excluded from the system and the priority of the path, loading point or unloading point where the truck is located should be reduced so that the planning and dispatching plan can be adjusted in a timely manner. Achieve the company’s established goals;
上式中所用到的参数符号作如下定义:The parameter symbols used in the above formula are defined as follows:
i:装载点的索引号,表示第i个装载点(即挖掘机位置),i=1,2,…,I,个;j:卸载点的索引号,表示第j个卸载点(即破碎站位置),j=1,2,…,J,个;k:卡车车型索引号,表示第k种型号的卡车,k=1,2,…,K,种;l:卡车编号索引号,表示第l辆k型卡车,l=1,2,…,L,辆;Xijkl:编号为l的k型卡车重载从装载点i到卸载点j的次数,次;Yijkl:编号为l的k型卡车空载从卸载点j到装载点i的次数,次;dij:装载点i到卸载点j的距离,km;djo:从卸载点j到加油点O的距离,km;doi,从加油点O到装载点i的最佳距离,km;Ck:k型号卡车的装载容量,t;CEkl:编号为l的k型卡车重载时的单位距离成本,元/km;CLkl:编号为l的k型卡车空载时的单位距离成本,元/km;Ekl:编号为l的k型号卡车的油箱容量,L;EEkl:编号为l的k型卡车重载时的单位距离油耗,L/km;ELkl,编号为l的k型卡车空载时的单位距离油耗,L/km;K:最小剩余油量,L;gi:装载点i的最大生产能力,t;fj:卸载点j的最小生产需求(一个班次内),t;qj:卸载点j的最大生产能力,t;e:矿石品位的限制;αi:装载点i的矿石品位;β:矿石品位允许误差;Gj:卸载点j的目标品位;Tlim:班工作时间;SEkl:编号为l的k型卡车重载时的平均速度,km/h;SLkl:编号为l的k型卡车空载时的平均速度,km/h;TO,卡车加油的平均用时,min;Tz:卡车装载的平均用时,min;Tq:卡车卸载的平均用时,min;KjOkl:编号为l的k型卡车空载时从卸载点j到加油点O的运行次数,次;KOikl:编号为l的k型卡车空载时从加油点O到装载点i的运行次数,次。i: The index number of the loading point, indicating the i-th loading point (i.e., the excavator position), i = 1, 2,...,I,; j: The index number of the unloading point, indicating the j-th unloading point (i.e., crushing station location), j=1,2,...,J,; k: truck model index number, indicating the k-th model of truck, k=1,2,...,K, type; l: truck number index number, Represents the l-th k-type truck, l= 1,2 , ...,L,; The number of times a k-type truck of l is empty from unloading point j to loading point i, times; d ij : the distance from loading point i to unloading point j, km; d jo : the distance from unloading point j to refueling point O, km ; d oi , the optimal distance from refueling point O to loading point i, km; C k : loading capacity of k-type truck, t; CE kl : unit distance cost of k-type truck numbered l when overloaded, yuan /km; CL kl : The unit distance cost of the k-type truck numbered l when empty, yuan/km; E kl : The fuel tank capacity of the k-type truck numbered l, L; EE kl : The k-type truck numbered l Fuel consumption per unit distance when the truck is heavily loaded, L/km; EL kl , fuel consumption per unit distance when the k-type truck numbered l is unloaded, L/km; K: minimum remaining fuel volume, L; g i : loading point i The maximum production capacity, t; f j : the minimum production demand of unloading point j (within one shift), t; q j : the maximum production capacity of unloading point j, t; e: the limit of ore grade; α i : loading point The ore grade of i; β: the allowable error of the ore grade; G j : the target grade of the unloading point j; T lim : the shift working time; SE kl : the average speed of the K-type truck numbered l when heavy load, km/h; SL kl : the average speed of the K-type truck numbered l when empty, km/h; T O , the average time for truck refueling, min; T z : the average time for truck loading, min; T q : the average time for truck unloading Time, min; K jOkl : The number of runs of the k-type truck numbered l from the unloading point j to the refueling point O when empty, times; K Oikl : The k-type truck numbered l from the refueling point O to the loading point when empty The number of runs of point i, times.
所述步骤4具体为:The specific step 4 is:
设置多目标粒子群算法的基本参数如惯性权重ω、飞行速度c1,c2,通过多目标粒子群算法求解露天矿无人驾驶卡车调度优化模型;Set the basic parameters of the multi-objective particle swarm algorithm such as inertia weight ω and flight speed c1, c2, and solve the open-pit mine driverless truck scheduling optimization model through the multi-objective particle swarm algorithm;
判断当前解(方案)是否满足生产能力、品位要求、车流控制三方面的约束,接着更新粒子的位置和速度;上述整个过程完成为一次迭代,当算法迭代完成,则输出结果,结束运算过程,否则继续进行多目标粒子群算法求解;具体操作步骤如下:Determine whether the current solution (scheme) meets the three constraints of production capacity, grade requirements, and traffic flow control, and then update the position and speed of the particles; the above entire process is completed as one iteration. When the algorithm iteration is completed, the result is output and the calculation process ends. Otherwise, continue with the multi-objective particle swarm algorithm solution; the specific steps are as follows:
Step1:初始化粒子群P;对每个粒子,确定其初始位置和速度计算粒子群P中每个粒子的目标向量;Step1: Initialize the particle swarm P; for each particle, determine its initial position and speed and calculate the target vector of each particle in the particle swarm P;
Step2:将粒子群P中部分粒子保存在外部粒子群NP中,这些粒子的位置就是非劣解;Step2: Save some particles in the particle swarm P in the external particle swarm NP, and the positions of these particles are non-inferior solutions;
Step3:确定每个粒子的初始自身最好位置,即每个粒子本身的初始位置;Step3: Determine the best initial position of each particle, that is, the initial position of each particle itself;
Step4:将目标空间分割成很多格子(超立方体),并根据粒子所对应的目标向量确定每个粒子所在的格子;Step 4: Divide the target space into many grids (hypercubes), and determine the grid where each particle is located based on the target vector corresponding to the particle;
Step5:为每个至少包含一个外部粒子群个体的格子定义适应度值(等于或大于1的数与格子内所包含的NP成员个数之比),然后对每个粒子,根据轮盘赌方法选择一个格子,并从中随机选择一个外部粒子群的个体作为粒子的gbest;Step5: Define the fitness value (the ratio of a number equal to or greater than 1 to the number of NP members contained in the grid) for each grid that contains at least one external particle swarm individual, and then for each particle, according to the roulette method Select a grid and randomly select an individual from the external particle group as the gbest of the particle;
Step6:根据PSO的基本公式更新所有粒子的位置和速度;Step6: Update the position and velocity of all particles according to the basic formula of PSO;
Step7:采用如下措施以避免粒子飞出搜索空间:一旦粒子飞出了某个决策变量的边界,该粒子停留在该边界上,同时改变飞行方向;Step7: Use the following measures to prevent particles from flying out of the search space: Once a particle flies out of the boundary of a certain decision variable, the particle stays on the boundary and changes its flight direction at the same time;
Step8:计算粒子群P中每个粒子的目标向量;Step8: Calculate the target vector of each particle in the particle swarm P;
Step9:利用自适应网格法对外部粒子群NP进行更新和维护;Step9: Use the adaptive grid method to update and maintain the external particle swarm NP;
Step10:更新粒子的pbest。根据粒子飞行过程中获得的新解与其自身最好位置pbest比较,若新解支配了pbest,则新解为新的pbest;否则,pbest保持不变;若新解与pbest彼此不受支配,则从两者随机选择一个作为新的自身最好位置;Step10: Update the pbest of the particles. According to the comparison between the new solution obtained during the particle flight and its own best position pbest, if the new solution dominates pbest, the new solution is the new pbest; otherwise, pbest remains unchanged; if the new solution and pbest are not dominated by each other, then Randomly select one of the two as the new best position;
Step11:如果终止条件成立,则停止搜索,判断方案是否符合模型中生产能力、品位限制和车流控制三个约束条件;同时满足上述条件时,输出优化方案;否则,转到Step6。Step11: If the termination condition is established, stop the search and determine whether the plan meets the three constraints of production capacity, grade limit and traffic flow control in the model; when the above conditions are met at the same time, output the optimization plan; otherwise, go to Step6.
本发明的有益效果:Beneficial effects of the present invention:
本发明中构建的调度模型更加贴合矿山实际生产情况,方案求得的调度方案可以有效地降低运输成本,提高运输效率。The dispatch model constructed in the present invention is more in line with the actual production situation of the mine, and the dispatch plan obtained from the plan can effectively reduce transportation costs and improve transportation efficiency.
本发明针对考虑卡车性能的露天矿无人驾驶卡车调度优化问题,以最小化卡车等待时间、最小化卡车等待时间、最小化剩余产量、最小化矿石品位偏差为目标,考虑了卡车不同的运输效率,构建了露天矿无人驾驶卡车调度优化模型。采用多目标粒子群算法进行优化求解,有效地解决了露天矿无人驾驶卡车调度优化问题,满足了实际矿山运输的需要。This invention aims at the optimization problem of unmanned truck dispatching in open-pit mines considering truck performance, with the goal of minimizing truck waiting time, minimizing truck waiting time, minimizing remaining production, and minimizing ore grade deviation, and taking into account the different transportation efficiencies of trucks. , constructed an optimization model for driverless truck dispatching in open-pit mines. The multi-objective particle swarm algorithm is used for optimization and solution, which effectively solves the optimization problem of driverless truck dispatching in open-pit mines and meets the needs of actual mine transportation.
附图说明Description of drawings
图1为卡车生产调度流程图。Figure 1 is a truck production scheduling flow chart.
图2为本发明考虑卡车性能的露天矿无人驾驶卡车调度流程图。Figure 2 is a flow chart of unmanned truck dispatching for open pit mines taking into account truck performance according to the present invention.
具体实施方式Detailed ways
下面结合实施例对本发明作进一步详细说明。The present invention will be further described in detail below with reference to examples.
一种考虑卡车性能的露天矿无人驾驶卡车调度方法,包括以下步骤;A method for dispatching driverless trucks in open pit mines considering truck performance, including the following steps;
首先,根据露天矿生产中收集的关于每辆卡车载重、已完成公里数、发动机状况、轮胎磨损情况、维修保养情况以及每台班平均速度的历史数据,建立露天矿卡车运行状况数据库;First, establish an open-pit mine truck operating status database based on historical data collected during open-pit mine production on each truck's load capacity, completed kilometers, engine condition, tire wear, repair and maintenance status, and average speed of each shift;
其次,根据历史数据训练基于BP神经网络的预测模型,通过预测得到卡车实时的运行速度;Secondly, a prediction model based on BP neural network is trained based on historical data, and the real-time running speed of the truck is obtained through prediction;
最后,建立一个以生产能力、品位要求、车流控制为约束条件,以最小化卡车等待时间、最小化卡车等待时间、最小化剩余产量、最小化矿石品位偏差为目标的露天矿无人驾驶卡车调度模型,并采用多目标粒子群算法进行模型的求解。Finally, a driverless truck dispatching system for open-pit mines was established with production capacity, grade requirements, and traffic flow control as constraints, with the goal of minimizing truck waiting time, minimizing truck waiting time, minimizing remaining production, and minimizing ore grade deviation. model, and the multi-objective particle swarm algorithm is used to solve the model.
如图1所示,为露天矿卡车生产调度流程图。在实际生产中,卡车往返装载点、卸矿点、加油站之间进行运输。对于现场卡车的调度,是提高生产效率的主要途径之一。As shown in Figure 1, it is an open pit mine truck production scheduling flow chart. In actual production, trucks transport to and from loading points, unloading points, and gas stations. On-site truck dispatching is one of the main ways to improve production efficiency.
如图2所示,本发明提出了一种考虑卡车性能的露天矿无人驾驶卡车调度方法,主要包含以下步骤:As shown in Figure 2, the present invention proposes a method for dispatching unmanned trucks in open-pit mines that considers truck performance, which mainly includes the following steps:
步骤1.露天矿卡车运行状况数据库的建立:Step 1. Establishment of open pit truck operating status database:
收集露天矿生产中每辆卡车的卡车载重、已完成公里数、发动机状况、轮胎磨损情况、维修保养情况以及每台班平均速度的历史数据,建立露天矿卡车运行状况数据库;制定露天矿卡车运行状况数据统计表,统计露天矿卡车运行状况的历史数据。本发明中设计的卡车运行状况数据统计表如下:Collect historical data on the truck load, completed kilometers, engine condition, tire wear, repair and maintenance status, and average speed of each shift of each truck in open-pit mine production, and establish an open-pit mine truck operating status database; formulate open-pit mine truck operation The status data statistics table collects historical data on the operating status of open-pit mine trucks. The truck operating status data statistical table designed in the present invention is as follows:
步骤2.卡车运行效率(速度)预测模型的建立:Step 2. Establishment of truck operating efficiency (speed) prediction model:
通过对影响卡车运输性能因素的分析,建立了以卡车载重、已行驶公里数、发动机故障次数、轮胎已使用公里数、日常维修次数五个影响因素为输入参数,以车辆每台班平均速度为输出参数的预测模型。Through the analysis of factors that affect truck transportation performance, we established five influencing factors as input parameters: truck load, kilometers traveled, number of engine failures, kilometers used by tires, and number of daily maintenance times. Based on the average speed of the vehicle per shift, is the prediction model for the output parameters.
本发明中选取2021年1月1日到2021年4月10日的100组历史统计数据对回归模型进行训练,其中90组构成训练数据集,用于训练网络获取计算模型;其余10组为仿真数据,用于验证模型的计算精度。In this invention, 100 groups of historical statistical data from January 1, 2021 to April 10, 2021 are selected to train the regression model, of which 90 groups constitute a training data set, which is used to train the network to obtain the calculation model; the remaining 10 groups are simulation Data used to verify the computational accuracy of the model.
步骤3.多目标露天矿无人驾驶卡车调度优化模型的建立:Step 3. Establishment of multi-objective open-pit mine driverless truck scheduling optimization model:
根据露天矿实际生产运输过程,以最小化卡车等待时间、最小化卡车等待时间、最小化剩余产量、最小化矿石品位偏差为目标,建立露天矿无人驾驶卡车调度优化模型;模型具体表达式如下:Based on the actual production and transportation process of the open-pit mine, with the goals of minimizing truck waiting time, minimizing truck waiting time, minimizing remaining production, and minimizing ore grade deviation, an open-pit mine unmanned truck scheduling optimization model is established; the specific expression of the model is as follows :
(1):目标函数:(1): Objective function:
最小化卡车运输成本:Minimize trucking costs:
卡车的运输成本由卡车重载从装载点到卸矿点的运输成本,卡车空载从卸载点返回装载点以及空载往返加油点的运输成本构成;The transportation cost of a truck consists of the transportation cost of a heavy truck from the loading point to the unloading point, the transportation cost of an empty truck from the unloading point to the loading point, and the transportation cost of an empty truck to and from the refueling point;
最小化卡车等待时间:Minimize truck waiting time:
卡车的等待时间由卡车班次工作时间减去卡车实际运行时间构成,实际运行时间包括装卸点之间的运行时间、装卸车时间、来往加油站时间以及加油时间;The waiting time of a truck is composed of the truck shift working time minus the actual running time of the truck. The actual running time includes the running time between loading and unloading points, loading and unloading time, time to and from the gas station, and refueling time;
最小化剩余产量:Minimize remaining production:
为保证采矿点一个班次生产的矿石能尽可能的被运输,即安排的运力与产能尽可能相近,要求产量减去运量的绝对值最小;In order to ensure that the ore produced in one shift at the mining site can be transported as much as possible, that is, the arranged transportation capacity is as close as possible to the production capacity, the absolute value of the output minus the transportation volume is required to be minimum;
最小化矿石品位偏差:Minimize ore grade deviation:
各个卸载点处的矿石品位偏差之和最小;The sum of the ore grade deviations at each unloading point is the smallest;
(2)约束条件:(2) Constraints:
卸载点的生产能力约束:Production capacity constraints at the unloading point:
运往每个卸载点的矿石总量不得超过该卸载点的最大生产能力;The total amount of ore shipped to each unloading point shall not exceed the maximum production capacity of that unloading point;
卸载点的产量计划要求:Production planning requirements for unloading points:
每个卸载点卸载的矿石总量至少要满足该卸载点的生产需求;The total amount of ore unloaded at each unloading point must at least meet the production demand of that unloading point;
装载点的生产能力约束:Production capacity constraints for load points:
每个装载点装载的矿石总量不得超过该装载点的最大生产能力;The total amount of ore loaded at each loading point shall not exceed the maximum production capacity of that loading point;
卸载点的矿石品位要求:Ore grade requirements at the unloading point:
每个卸载点卸载的矿石品位偏差不得超过最大品位偏差;The grade deviation of ore unloaded at each unloading point shall not exceed the maximum grade deviation;
车流连续性约束,保证每个装/卸载点的出入车流量相等;Traffic flow continuity constraints ensure that the incoming and outgoing traffic flow of each loading/unloading point is equal;
剩余油量约束:Remaining fuel constraints:
无人驾驶卡车不再像传统卡车一样,在行驶过程中有司机能够随时关注剩余油量,因此在对无人驾驶卡车进行车流规划调度时,需要监控其剩余油量。其具体表现为每辆卡车的剩余油量不得低于K,以便返回加油点加油。其中,最小剩余油量K=卸载点到装载点最大用油量+装载点到卸载点最大用油量+卸载点到加油点O的最大用油量;Self-driving trucks are no longer like traditional trucks. There is a driver who can keep an eye on the remaining fuel level at any time while driving. Therefore, when planning and dispatching driverless trucks for traffic flow, it is necessary to monitor their remaining fuel levels. Its specific manifestation is that the remaining fuel quantity of each truck must not be less than K in order to return to the refueling point for refueling. Among them, the minimum remaining oil amount K = the maximum oil consumption from the unloading point to the loading point + the maximum oil consumption from the loading point to the unloading point + the maximum oil consumption from the unloading point to the refueling point O;
卡车运输次数要求,卡车运输的次数必须为正整数;The number of truck transportation requirements, the number of truck transportation must be a positive integer;
Xijkl,Yijkl∈{0,1,2,3...}X ijkl ,Y ijkl ∈{0,1,2,3...}
冲突避让状况下的约束条件;Constraints in conflict avoidance situations;
当两辆卡车同时在某个装载点或卸载点时,卡车的路线会出现冲突,对卡车的运行成本和排队时间造成较大的影响。为了尽可能的降低由卡车任务冲突造成的影响,需要在初始状态的约束前提之下,对卡车设定优先级。考虑到满载的卡车会比空载的卡车运行成本更高,因此优先满载卡车先行。此外,优先级的判定同样适用于两辆卡车在同一路口相会时,空载的无人驾驶卡车应当避让。When two trucks are at a certain loading or unloading point at the same time, the truck routes will conflict, which will have a greater impact on the truck's operating costs and queuing time. In order to reduce the impact caused by truck task conflicts as much as possible, it is necessary to set priorities for trucks under the constraints of the initial state. Given that fully loaded trucks are more expensive to operate than empty trucks, priority is given to fully loaded trucks. In addition, the priority determination also applies to when two trucks meet at the same intersection, and the unloaded driverless truck should give way.
卡车故障状况下的约束条件;Constraints in truck failure conditions;
一旦卡车故障无法行驶,则该卡车应及时退出车流规划调度系统。因此,当某辆无人驾驶卡车向总调度中心预警后,应当立即将该卡车排除在系统外,同时降低该卡车所在的路径、装载点或卸载点的优先等级,以便及时调整规划调度方案,完成企业既定的目标;Once a truck fails and cannot move, the truck should exit the traffic flow planning and dispatching system in a timely manner. Therefore, when a driverless truck alerts the general dispatch center, the truck should be immediately excluded from the system, and the priority of the path, loading point or unloading point where the truck is located should be reduced so that the planned dispatch plan can be adjusted in a timely manner. Achieve the company’s established goals;
上式中所用到的参数符号作如下定义:The parameter symbols used in the above formula are defined as follows:
i:装载点的索引号,表示第i个装载点(即挖掘机位置),i=1,2,…,I,个;j:卸载点的索引号,表示第j个卸载点(即破碎站位置),j=1,2,…,J,个;k:卡车车型索引号,表示第k种型号的卡车,k=1,2,…,K,种;l:卡车编号索引号,表示第l辆k型卡车,l=1,2,…,L,辆;Xijkl:编号为l的k型卡车重载从装载点i到卸载点j的次数,次;Yijkl:编号为l的k型卡车空载从卸载点j到装载点i的次数,次;dij:装载点i到卸载点j的距离,km;djo:从卸载点j到加油点O的距离,km;doi,从加油点O到装载点i的最佳距离,km;Ck:k型号卡车的装载容量,t;CEkl:编号为l的k型卡车重载时的单位距离成本,元/km;CLkl:编号为l的k型卡车空载时的单位距离成本,元/km;Ekl:编号为l的k型号卡车的油箱容量,L;EEkl:编号为l的k型卡车重载时的单位距离油耗,L/km;ELkl,编号为l的k型卡车空载时的单位距离油耗,L/km;K:最小剩余油量,L;gi:装载点i的最大生产能力,t;fj:卸载点j的最小生产需求(一个班次内),t;qj:卸载点j的最大生产能力,t;e:矿石品位的限制;αi:装载点i的矿石品位;β:矿石品位允许误差;Gj:卸载点j的目标品位;Tlim:班工作时间;SEkl:编号为l的k型卡车重载时的平均速度,km/h;SLkl:编号为l的k型卡车空载时的平均速度,km/h;TO,卡车加油的平均用时,min;Tz:卡车装载的平均用时,min;Tq:卡车卸载的平均用时,min;KjOkl:编号为l的k型卡车空载时从卸载点j到加油点O的运行次数,次;KOikl:编号为l的k型卡车空载时从加油点O到装载点i的运行次数,次。i: The index number of the loading point, indicating the i-th loading point (i.e., the excavator position), i = 1, 2,...,I,; j: The index number of the unloading point, indicating the j-th unloading point (i.e., crushing station location), j=1,2,...,J,; k: truck model index number, indicating the k-th model of truck, k=1,2,...,K, type; l: truck number index number, Represents the l-th k-type truck, l= 1,2 , ...,L,; The number of times a k-type truck of l is empty from unloading point j to loading point i, times; d ij : the distance from loading point i to unloading point j, km; d jo : the distance from unloading point j to refueling point O, km ; d oi , the optimal distance from refueling point O to loading point i, km; C k : loading capacity of k-type truck, t; CE kl : unit distance cost of k-type truck numbered l when overloaded, yuan /km; CL kl : The unit distance cost of the k-type truck numbered l when empty, yuan/km; E kl : The fuel tank capacity of the k-type truck numbered l, L; EE kl : The k-type truck numbered l Fuel consumption per unit distance when the truck is heavily loaded, L/km; EL kl , fuel consumption per unit distance when the k-type truck numbered l is unloaded, L/km; K: minimum remaining fuel volume, L; g i : loading point i The maximum production capacity, t; f j : the minimum production demand of unloading point j (within one shift), t; q j : the maximum production capacity of unloading point j, t; e: the limit of ore grade; α i : loading point The ore grade of i; β: the allowable error of the ore grade; G j : the target grade of the unloading point j; T lim : the shift working time; SE kl : the average speed of the K-type truck numbered l when heavy load, km/h; SL kl : the average speed of the K-type truck numbered l when empty, km/h; T O , the average time for truck refueling, min; T z : the average time for truck loading, min; T q : the average time for truck unloading Time, min; K jOkl : The number of runs of the k-type truck numbered l from the unloading point j to the refueling point O when empty, times; K Oikl : The k-type truck numbered l from the refueling point O to the loading point when empty The number of runs of point i, times.
步骤4.采用多目标粒子群算法求解调度模型:用于最终调度方案的寻优。Step 4. Use the multi-objective particle swarm algorithm to solve the scheduling model: used to optimize the final scheduling plan.
设置多目标粒子群算法的基本参数如惯性权重ω、飞行速度c1,c2,通过多目标粒子群算法求解露天矿无人驾驶卡车调度优化模型;Set the basic parameters of the multi-objective particle swarm algorithm such as inertia weight ω and flight speed c1, c2, and solve the open-pit mine driverless truck scheduling optimization model through the multi-objective particle swarm algorithm;
判断当前解(方案)是否满足生产能力、品位要求、车流控制三方面的约束,接着更新粒子的位置和速度;上述整个过程完成为一次迭代,当算法迭代完成,则输出结果,结束运算过程,否则继续进行多目标粒子群算法求解;具体操作步骤如下:Determine whether the current solution (scheme) meets the three constraints of production capacity, grade requirements, and traffic flow control, and then update the position and speed of the particles; the above entire process is completed as one iteration. When the algorithm iteration is completed, the result is output and the calculation process ends. Otherwise, continue with the multi-objective particle swarm algorithm solution; the specific steps are as follows:
Step1:初始化粒子群P;对每个粒子,确定其初始位置和速度计算粒子群P中每个粒子的目标向量;Step1: Initialize the particle swarm P; for each particle, determine its initial position and speed and calculate the target vector of each particle in the particle swarm P;
Step2:将粒子群P中部分粒子保存在外部粒子群NP中,这些粒子的位置就是非劣解;Step2: Save some particles in the particle swarm P in the external particle swarm NP, and the positions of these particles are non-inferior solutions;
Step3:确定每个粒子的初始自身最好位置,即每个粒子本身的初始位置;Step3: Determine the best initial position of each particle, that is, the initial position of each particle itself;
Step4:将目标空间分割成很多格子(超立方体),并根据粒子所对应的目标向量确定每个粒子所在的格子;Step 4: Divide the target space into many grids (hypercubes), and determine the grid where each particle is located based on the target vector corresponding to the particle;
Step5:为每个至少包含一个外部粒子群个体的格子定义适应度值(等于或大于1的数与格子内所包含的NP成员个数之比),然后对每个粒子,根据轮盘赌方法选择一个格子,并从中随机选择一个外部粒子群的个体作为粒子的gbest;Step5: Define the fitness value (the ratio of a number equal to or greater than 1 to the number of NP members contained in the grid) for each grid that contains at least one external particle swarm individual, and then for each particle, according to the roulette method Select a grid and randomly select an individual from the external particle group as the gbest of the particle;
Step6:根据PSO的基本公式更新所有粒子的位置和速度;Step6: Update the position and velocity of all particles according to the basic formula of PSO;
Step7:采用如下措施以避免粒子飞出搜索空间:一旦粒子飞出了某个决策变量的边界,该粒子停留在该边界上,同时改变飞行方向;Step7: Use the following measures to prevent particles from flying out of the search space: Once a particle flies out of the boundary of a certain decision variable, the particle stays on the boundary and changes its flight direction at the same time;
Step8:计算粒子群P中每个粒子的目标向量;Step8: Calculate the target vector of each particle in the particle swarm P;
Step9:利用自适应网格法对外部粒子群NP进行更新和维护;Step9: Use the adaptive grid method to update and maintain the external particle swarm NP;
Step10:更新粒子的pbest。根据粒子飞行过程中获得的新解与其自身最好位置pbest比较,若新解支配了pbest,则新解为新的pbest;否则,pbest保持不变;若新解与pbest彼此不受支配,则从两者随机选择一个作为新的自身最好位置;Step10: Update the pbest of the particles. According to the comparison between the new solution obtained during the particle flight and its own best position pbest, if the new solution dominates pbest, the new solution is the new pbest; otherwise, pbest remains unchanged; if the new solution and pbest are not dominated by each other, then Randomly select one of the two as the new best position;
Step11:如果终止条件成立,则停止搜索,判断方案是否符合模型中生产能力、品位限制和车流控制三个约束条件;同时满足上述条件时,输出优化方案;否则,转到Step6。Step11: If the termination condition is established, stop the search and determine whether the plan meets the three constraints of production capacity, grade limit and traffic flow control in the model; when the above conditions are met at the same time, output the optimization plan; otherwise, go to Step6.
综上,本发明针对考虑卡车性能的露天矿无人驾驶卡车调度优化问题,以最小化卡车等待时间、最小化卡车等待时间、最小化剩余产量、最小化矿石品位偏差为目标,考虑了卡车不同的运输效率,构建了露天矿无人驾驶卡车调度优化模型。采用多目标粒子群算法进行优化求解,有效地解决了露天矿无人驾驶卡车调度优化问题,满足了实际矿山运输的需要。In summary, this invention aims at the problem of unmanned truck scheduling optimization in open-pit mines considering truck performance, with the goal of minimizing truck waiting time, minimizing truck waiting time, minimizing remaining production, and minimizing ore grade deviation, taking into account the different characteristics of trucks. To improve the transportation efficiency, a driverless truck scheduling optimization model for open-pit mines was constructed. The multi-objective particle swarm algorithm is used for optimization and solution, which effectively solves the optimization problem of driverless truck dispatching in open-pit mines and meets the needs of actual mine transportation.
Claims (6)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202311456356.1A CN117634772A (en) | 2023-11-03 | 2023-11-03 | Strip mine unmanned truck scheduling method considering truck performance |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202311456356.1A CN117634772A (en) | 2023-11-03 | 2023-11-03 | Strip mine unmanned truck scheduling method considering truck performance |
Publications (1)
Publication Number | Publication Date |
---|---|
CN117634772A true CN117634772A (en) | 2024-03-01 |
Family
ID=90034696
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202311456356.1A Pending CN117634772A (en) | 2023-11-03 | 2023-11-03 | Strip mine unmanned truck scheduling method considering truck performance |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN117634772A (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117829556A (en) * | 2024-03-05 | 2024-04-05 | 北京科技大学 | Multi-stage ore scheduling method and system for short-interval dynamic ore matching |
CN119313250A (en) * | 2024-12-19 | 2025-01-14 | 储动科技有限公司 | Method and equipment for determining transport truck configuration and electric mining truck dispatching plan |
CN119558634A (en) * | 2025-02-05 | 2025-03-04 | 中国矿业大学(北京) | Open pit mine transportation dispatching method and device |
-
2023
- 2023-11-03 CN CN202311456356.1A patent/CN117634772A/en active Pending
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117829556A (en) * | 2024-03-05 | 2024-04-05 | 北京科技大学 | Multi-stage ore scheduling method and system for short-interval dynamic ore matching |
CN119313250A (en) * | 2024-12-19 | 2025-01-14 | 储动科技有限公司 | Method and equipment for determining transport truck configuration and electric mining truck dispatching plan |
CN119558634A (en) * | 2025-02-05 | 2025-03-04 | 中国矿业大学(北京) | Open pit mine transportation dispatching method and device |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN117634772A (en) | Strip mine unmanned truck scheduling method considering truck performance | |
CN113486293B (en) | Intelligent horizontal transportation system and method for full-automatic side loading and unloading container wharf | |
CN110428161A (en) | A Cloud Intelligent Scheduling Method for Unmanned Mining Vehicles Based on Device-Edge Cloud Architecture | |
US10894552B2 (en) | System and method integrating an energy management system and yard planner system | |
CN107918849A (en) | A kind of intelligent scheduling device and method of electronic logistics van | |
CN113222387B (en) | Multi-objective scheduling and collaborative optimization method for hydrogen fuel vehicle | |
CN114595607B (en) | A digital twin textile can conveying method and system | |
CN114662943B (en) | Truck dispatching method for open-pit mines based on multi-objective genetic algorithm | |
CN111860968A (en) | Surface mine vehicle scheduling method and system and computer equipment | |
CN119317936A (en) | System and method for managing task allocation of work machines using machine learning | |
Yang et al. | Battery-powered automated guided vehicles scheduling problem in automated container terminals for minimizing energy consumption | |
CN114444809A (en) | Data-driven multi-target strip mine card path optimization method | |
US12158765B2 (en) | Systems and methods for managing assignments of tasks for mining equipment using machine learning | |
CN114326621B (en) | A group intelligent airport truck dispatching method and system based on hierarchical architecture | |
CN118071108A (en) | Digital twin and causal model fused wharf AGV linkage method and system | |
CN116151723B (en) | Multiple metering method and system for comprehensive grain reserve base | |
CN118674208A (en) | Open pit unmanned truck dynamic scheduling system and method based on reinforcement learning | |
CN117787623A (en) | A data-driven low-carbon dispatching optimization method, system, equipment and medium for vehicles in open-pit mining areas | |
He et al. | Energy-efficient receding horizon trajectory planning of high-speed trains using real-time traffic information | |
CN117350444A (en) | AGV transport vehicle on-board site selection-path optimization method based on genetic algorithm | |
Cao | Mathematical Model and Algorithm of Multi‐Index Transportation Problem in the Background of Artificial Intelligence | |
CN117314297A (en) | Non-pairing delivery path planning method under reusable logistics container recycling and sharing mode | |
CN112734111B (en) | Horizontal transport task AGV dynamic time prediction method | |
Gan et al. | Scheduling problems of automated guided vehicles in automated container terminals using a genetic algorithm | |
CN114971459A (en) | Logistics path multi-objective optimization method based on improved constraint evolution control operator |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |