An open-pit iron mine vehicle flow planning and scheduling optimization method

Abstract

This invention provides an optimization method for truck flow planning and scheduling in open-pit iron ore mines, relating to the field of open-pit iron ore production operation technology. First, based on the daily planned ore blending production ratio provided by the ore dressing department, the required number of trucks to be scheduled is estimated. A multi-objective optimization model is established with the objectives of minimizing total transportation cost, maximizing ore transportation volume, minimizing rock transportation volume, and minimizing total idle time. An improved MOPSO algorithm with adaptive weighting strategy, congestion ranking strategy, local search mechanism, and constraint handling mechanism is proposed to solve the truck flow planning and scheduling model. Based on the optimization results, the actual daily planned output and grade are adjusted, providing a reference for scheduling operation decisions. This invention can effectively handle multiple objectives and complex constraints in the open-pit iron ore mine truck flow planning and scheduling problem, improving solution efficiency and scheduling quality. It helps schedulers evaluate the feasibility and effectiveness of scheduling schemes before actual implementation, improving production efficiency and saving production costs while meeting production output and grade requirements.

Description

Technical Field

[0001] This invention relates to the field of open-pit iron mine production operation technology, and in particular to an optimization method for planning and scheduling traffic flow in open-pit iron mines. Background Technology

[0002] Open-pit iron ore mining is a production activity that integrates mining, transportation, and unloading, with mining as its foundation and transportation as its link. Among these activities, the transportation of ore is the key link connecting all these production activities. Open-pit iron ore mines mainly rely on mining trucks to complete the loading, unloading, and transportation of ore excavated by electric shovels. An efficient and reasonable mining truck scheduling scheme is conducive to saving transportation costs and improving the economic benefits of mining enterprises.

[0003] In existing technologies, open-pit mines utilize scheduling systems to dispatch mining trucks to complete production tasks. However, the existing systems' methods for optimizing mining truck scheduling do not adequately consider the unique operating conditions of open-pit iron mines, rendering them inapplicable. Firstly, open-pit iron mines contain ore crushing stations and rock crushing stations, requiring careful attention and constraints on their grade and output during production processes. Secondly, in actual production, the required number of trucks for each route needs to be determined based on ore blending ratios, followed by the development of a detailed scheduling plan to ensure feasibility. Finally, the actual daily output and grade obtained after the implementation of the open-pit mine truck scheduling plan will deviate from the daily plan, leading to data distortion. Therefore, existing methods fail to adequately consider the specific characteristics of open-pit iron mines, resulting in inaccurate models and impractical solutions that cannot provide strong support for actual open-pit iron mine production. Furthermore, the complexity of the models due to various practical characteristics makes it difficult for existing algorithms to solve efficiently and result in low-quality plans. Therefore, there is an urgent need for a more targeted and practical optimization method for open-pit iron ore truck flow planning and scheduling, which should fully consider practical factors such as constraints of ore crushing stations and rock crushing stations, and deviations between ore matching and truck allocation processes and plans, so as to achieve an effective and high-quality solution to the open-pit iron ore truck planning and scheduling problem. Summary of the Invention

[0004] This invention provides an optimization method for planning and scheduling train traffic in open-pit iron mines. It comprehensively considers the constraints of ore crushing stations and rock crushing stations within the open-pit iron mine, the demand for ore blending and train matching, and the current status of output and grade deviation. An improved MOPSO algorithm is designed that combines an adaptive weight strategy, a congestion ranking strategy, a local search mechanism, and a constraint handling mechanism, thereby better guiding the actual production of open-pit iron mines.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] An optimization method for planning and scheduling train traffic in open-pit iron mines includes the following steps:

[0007] Step 1: Based on the reference truck allocation ratio for each ore loading area in the daily plan provided by the mineral processing department, determine the daily planned truck usage. While meeting the production plan's requirements for output and grade, minimize truck usage to save costs.

[0008] Step 2: Establish the objective function of the multi-objective traffic flow planning and scheduling model for open-pit iron mines; the established model takes the minimum total transportation cost, maximum ore output, and minimum total idle time as its objective functions;

[0009] Step 3: Set constraints for the multi-objective vehicle flow planning and scheduling model for open-pit iron mines, including ore grade constraints for ore crushing stations, production plan constraints for ore crushing stations, production capacity constraints for electric shovel loading points, vehicle flow continuity constraints, remaining fuel constraints, and truck transport frequency constraints.

[0010] Step 4: Based on the truck usage calculated in Step 1 and the given number of electric shovel loading areas for the day, combined with the objective functions and constraints of Steps 2 and 3, apply a multi-objective intelligent optimization algorithm to solve the model and obtain the Pareto optimal solution set.

[0011] Step 5: Based on the Pareto optimal solution set obtained in Step 4, select an optimal solution as the final scheduling scheme. Based on the truck scheduling operation results provided by the selected scheme, revise the actual daily planned output and grade for scheduling operation reference decision-making.

[0012] Furthermore, step 1 specifically includes:

[0013] Step 1.1: Calculate the maximum number of times a single truck can load and unload ore per day in the electric shovel ore loading area i∈(1,p). r1 As shown in the following formula:

[0014]

[0015] Where i∈(1,p) is the electric shovel ore loading area, and i∈(p+1,I) is the electric shovel rock loading area; the daily plan includes two shifts, day shift and night shift, and the working time of each shift is T. l ;T i1 d represents the average time taken by a truck to complete one loading / unloading cycle in the electric shovel ore loading area. i1 For the optimal distance s of the truck from ore loading point i to ore crushing station f Let s be the average speed of the truck when it is heavily loaded. n T is the average speed of the truck when it is unloaded. z T is the average time for truck loading. q γ is the average time for unloading the truck; γ is the control coefficient, adjusted according to the actual daily planned downtime or maintenance time of the truck; T c For truck maintenance time;

[0016] Here, we assume that each truck is the same model, i.e., has the same loading capacity. The mineral processing department roughly estimates the required amount of ore based on the required ore grade and the ore type and grade of each electric shovel loading area, as a reference for the daily planning operation of the production scheduling room. However, the estimated ore output has a large error compared with the actual production output.

[0017] Step 1.2: Based on the daily plan, refer to the truck configuration ratio m1:m2:...:m p Calculate the daily planned number of electric shovels z for each electric shovel ore loading area i∈(1,p). i1 Round up using the following formula:

[0018]

[0019] If the ratio of electric shovel to truck is required to be 1:2 or 1:3, then z must be satisfied. i1 ={2,3}, and m i The reference number of times a truck loads and unloads ore for the amount of ore produced in electric shovel loading zone i;

[0020] Step 1.3: Calculate the maximum number of times a single truck can load and unload ore per day in the electric shovel rock loading area i∈(p+1,I). r2 The formula is as follows:

[0021]

[0022] Among them, T i2 d represents the average time taken by a truck to complete one loading / unloading cycle in the electric shovel rock loading area. i2 The optimal distance for the truck from rock loading point i to the rock crushing station;

[0023] Step 1.4: Calculate the daily planned number of electric shovels z for each rock loading zone i∈(p+1,I). i2 Since the mineral processing department only provides the ore demand plan ratio, and the rock demand is evenly distributed according to the annual plan with minor daily adjustments given by the production scheduling office, it is only necessary to meet the target given by the production scheduling office. The formula is as follows:

[0024]

[0025] If the ratio of electric shovel to truck is required to be 1:2 or 1:3, then z must be satisfied. i2 ={2,3}, and f2 represents the minimum production requirement at the rock unloading point within one shift, C r For the truck's load capacity;

[0026] Step 1.5: Calculate the number of trucks to be dispatched in the daily plan:

[0027]

[0028] Furthermore, the objective function of step 2 specifically includes:

[0029] The objectives of the daily truck dispatch plan include minimizing truck transportation costs, maximizing ore production, and minimizing total truck idle time; the model objective function is:

[0030] F(S)={F1(S),-F2(S),F3(S)};

[0031] Where F1(S), F2(S), and F3(S) are the sub-objectives in the objective function, representing minimizing the total truck transportation cost, maximizing ore production, and minimizing the total truck idle time, respectively.

[0032] Truck transportation costs consist of three parts: the operating cost of a heavily loaded truck from the loading point to the unloading point, the operating cost of an empty truck from the unloading point to the loading point or to / from refueling points, and the truck maintenance cost; the objective function expression for minimizing the total truck transportation cost is:

[0033]

[0034] The objective function for maximizing ore production is specifically expressed as maximizing ore transportation while minimizing rock transportation, and its expression is:

[0035]

[0036] And convert it to:

[0037]

[0038] The idle time of a single truck is the difference between the planned working hours for each shift and the necessary time for driving, loading, unloading, refueling, and inspection. The objective function expression for minimizing the total idle time of trucks is:

[0039]

[0040] Where i is the loading point index number, representing the i-th loading point (i.e., the location of the electric shovel), i = 1, 2, ..., I; j is the unloading point index number, representing the j-th unloading point (i.e., the location of the crushing station), j = 1, 2, ..., J; r is the truck index number, representing the r-th truck, r = 1, 2, ..., R; x rij Let x be the number of times the r-th truck travels from loading point i to unloading point j. rji Let d be the number of times the r-th truck travels from unloading point j to loading point i. i,j The optimal distance d from loading point i to unloading point j jOThe optimal distance from unloading point j to refueling point O, d Oi The optimal distance from refueling point O to loading point i, C r1 Let C be the unit distance cost of the r-th truck when it is heavily loaded. r2 Let C be the unit distance cost of the r-th truck when it is unloaded. r3 Let f1 be the wear and tear cost (i.e., maintenance cost) of the r-th truck per unit time, and T be the minimum production demand at the ore unloading point within one shift. O The average time to refuel a truck, K rjO K represents the number of times truck r travels from unloading point j to refueling point O. rOi Let r be the number of times the truck travels from refueling point O to loading point i.

[0041] Furthermore, the constraints in step 3 specifically include:

[0042] Ore grade constraints for ore crushing plants: The deviation between the actual daily planned grade and the given grade must not exceed the maximum grade deviation β, as shown in the following formula:

[0043]

[0044] Where, α i Let β be the ore grade at loading point i, β be the allowable deviation of ore grade, and G1 be the target grade of the ore crushing station.

[0045] Production planning constraints for ore crushing stations: The total amount of ore unloaded from the ore crushing station must meet the minimum daily production demand f1 per shift, and the total amount of rock unloaded from the rock crushing station must meet the minimum daily production demand f2 per shift, as shown in the following two equations:

[0046]

[0047]

[0048] Electric shovel loading point production capacity constraint: The total amount of ore loaded at each electric shovel loading point must not exceed the maximum production capacity g of that loading point. i As shown in the following formula:

[0049]

[0050] Traffic flow continuity constraint: The inbound and outbound traffic flow at loading / unloading points must be equal, as shown in the following formula:

[0051]

[0052] Remaining fuel constraint: It is necessary to monitor the remaining fuel level of each truck. When it drops to the minimum value A, it needs to be refueled at a refueling point. The remaining fuel level A = maximum fuel consumption from unloading point to loading point + maximum fuel consumption from loading point to unloading point + maximum fuel consumption from unloading point to refueling point. The constraint formula is as follows:

[0053]

[0054] Where E is the truck's fuel tank capacity, and A is the truck's minimum remaining fuel level. r1 Let E be the fuel consumption per unit distance for the r-th truck under heavy load. r2 Let r be the fuel consumption per unit distance when the r-th truck is unloaded;

[0055] Truck transport frequency constraints: The number of truck transports must be a positive integer, and ore and rock trucks must be unloaded at their respective unloading points when fully loaded. Empty trucks at unloading points can go to any loading point. For the traffic flow planning and scheduling problem of mixed ore and rock transport, let I be the number of loading points for ore (the first p points) and rock (the last I p points), and let J be the number of unloading points, with the first unloading point being the ore crushing station and the second unloading point being the rock crushing station. The constraints are as follows:

[0056] x rij ∈{0,1,2...},x rji ∈{0,1,2...};

[0057] in,

[0058] Furthermore, the multi-objective intelligent optimization algorithm in step 4 employs an improved multi-objective particle swarm optimization algorithm to solve the multi-objective optimization problem of open-pit iron ore traffic planning and scheduling. This algorithm introduces several improvements based on the standard multi-objective particle swarm optimization algorithm to enhance its performance in handling complex constraints and multi-objective optimization problems. The specific steps are as follows:

[0059] Step 4.1: Particle encoding and initialization;

[0060] Each particle represents a feasible scheduling scheme, encoded as a multidimensional vector X = (x1, x2, ..., x...). n ), where n is the number of decision variables; during initialization, particle positions are randomly generated under the condition that the constraints are met;

[0061] Step 4.2: Based on the objective function established in Step 2, define a multi-objective fitness function: F(S) = {F1(S), -F2(S), F3(S)}, where F1(S) represents the objective of minimizing the total transportation cost of trucks, F2(S) represents the objective of maximizing ore production, and F3(S) represents the objective of minimizing the total idle time of trucks.

[0062] Step 4.3: Introduce an adaptive weighting strategy. The particle velocity and position update formulas are as follows:

[0063]

[0064] Among them, v id (t+1) v is the velocity of the i-th particle in the d-th dimension at time t+1; id t Let be the velocity of the i-th particle in the d-th dimension at time t; c1 is the individual learning factor, which determines the strength of the particle's tendency to move towards its individual historical best position; c2 is the social learning factor, which determines the strength of the particle's tendency to move towards the group's historical best position; r1 and r2 are random numbers between [0,1], regenerated in each iteration to increase the randomness of the search; p id t p represents the individual optimal position in the d-th dimension of the i-th particle's historical position, i.e., the individual optimal solution. gd t Let x be the historical optimal position of the group in the d-th dimension, i.e., the global optimal solution. id t Let x be the current position of the i-th particle in the d-th dimension at time t. id (t+1) Let be the current position of the i-th particle in the d-th dimension at time t+1; w(t) is the adaptive inertia weight, which is dynamically adjusted using the following formula:

[0065] w(t) = w max -(w max -w min )*(t / T max ) 2 ;

[0066] Among them, w max and w min These represent the maximum and minimum values ​​of the inertia weight, respectively, where t is the current iteration number, and T is the maximum and minimum values. max This represents the maximum number of iterations.

[0067] Step 4.4: Maintenance of non-dominated solution sets;

[0068] A fast non-dominated sorting algorithm is used to sort the particles, and crowding distance is used to maintain the diversity of the non-dominated solution set; the crowding distance calculation formula is as follows:

[0069]

[0070] Among them, CD i Let M be the crowding distance of the i-th solution, and M be the number of objective functions. and Let f be the function value of the m-th objective function among its adjacent solutions after sorting. m max and f m min These are the maximum and minimum values ​​of the m-th objective function, respectively;

[0071] Step 4.5: Local search mechanism;

[0072] After each iteration, a simulated annealing strategy is used to perform a local search on a subset of solutions in the non-dominated solution set. During the local search, to facilitate the algorithm escaping local optima, a certain probability is allowed to be used to accept worse solutions, i.e., an acceptance probability. The formula for calculating the acceptance probability is:

[0073] P(ΔE,T)=exp(-ΔE / T);

[0074] Where ΔE is the difference between the objective function of the new solution and the current solution, and T is the current temperature, which gradually decreases with the number of iterations;

[0075] Step 4.6: Constraint handling mechanism;

[0076] Use an improved constraint dominance rule to handle constraints and define the constraint violation degree:

[0077]

[0078] Among them, g q (X)≤0 is an inequality constraint, q=1,2,...,Q; h k (X) = 0 represents the equality constraint, k = 1, 2, ..., K. When comparing two solutions, we first compare their constraint violation degree. The solution with the smaller constraint violation degree is better than the solution with the larger constraint violation degree.

[0079] Step 4.7: Algorithm termination condition;

[0080] The algorithm iterates until one of the following conditions is met: the preset maximum number of iterations is reached, or the non-dominated solution set does not change after N consecutive iterations.

[0081] Furthermore, the details of step 5 are as follows:

[0082] Based on the Pareto optimal solution set obtained in step 4, the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is used to select an optimal solution from the final non-dominated solution set as the final scheduling scheme. The TOPSIS method is based on the following steps:

[0083] Construct a normalized decision matrix: Calculate the weighted normalized decision matrix; determine the ideal solution (the hypothetical solution where each indicator achieves the best value) and the negative ideal solution (the hypothetical solution where each indicator achieves the worst value); calculate the distance of each solution to the ideal solution and the negative ideal solution; calculate the relative proximity of each solution; rank the solutions according to their relative proximity.

[0084] Calculate the distance to the ideal solution and the negative ideal solution for each solution:

[0085] Let the weighted normalized value of the i-th scheme on the j-th index be v. ij The ideal solution has a value of v for the j-th index. j+ The value of the negative ideal solution on the j-th index is v. j- Then the distance S from the i-th solution to the ideal solution is... i+ S to the negative ideal solution i- The distance can be calculated using Euclidean distance:

[0086]

[0087] Where n is the number of evaluation indicators.

[0088] Calculate the relative proximity of each option:

[0089] The relative closeness C of the i-th scheme i The following formula can be used to calculate:

[0090]

[0091] Relative proximity C i The value range is [0,1], and the closer it is to 1, the better the solution is.

[0092] The solutions are ranked according to their relative similarity:

[0093] All schemes are ranked according to their relative proximity C i Sort from largest to smallest, C i The solution with the largest value is the optimal solution.

[0094] Based on the truck dispatching results of the selected plan, the daily planned actual output and grade are revised for dispatching operation reference. The revised formulas for daily planned ore output, rock output, and grade are as follows:

[0095]

[0096] in, The revised daily planned ore production For the corrected rock yield, For the corrected grade, α i Let i be the ore grade at loading point i.

[0097] The beneficial effects of adopting the above technical solution are as follows: The open-pit iron mine vehicle flow planning and scheduling optimization method provided by this invention prioritizes the issue of ore and vehicle matching, determines the truck usage based on the ore matching requirements of the beneficiation process, and then optimizes vehicle flow scheduling. This conforms to the logical order of actual production decisions in open-pit iron mines and is operable. A multi-objective optimization model is constructed, comprehensively considering multiple optimization objectives such as transportation costs, ore output, rock output, and idle time, and setting realistic constraints, making the optimization results more comprehensive and reliable, balancing factors such as cost and output. The intelligent optimization algorithm efficiently solves the multi-objective optimization model, overcoming the shortcomings of traditional scheduling methods in handling complex multi-objective and multi-constraint problems, significantly improving the efficiency and effectiveness of vehicle flow scheduling optimization. The optimized scheduling scheme is used to correct actual output and grade, providing an important basis for formulating reliable daily planning and scheduling strategies, and improving the reference value and guiding significance of the vehicle flow planning and scheduling scheme. Attached Figure Description

[0098] Figure 1 This is a schematic diagram of the open-pit iron ore mine traffic flow planning and scheduling optimization method provided in an embodiment of the present invention;

[0099] Figure 2 A flowchart of the improved multi-objective particle swarm optimization algorithm provided in an embodiment of the present invention. Detailed Implementation

[0100] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0101] like Figure 1 As shown, the method of this embodiment is described below.

[0102] An optimization method for planning and scheduling train traffic in open-pit iron mines includes the following steps:

[0103] Step 1: Based on the reference truck allocation ratio for each ore loading area in the daily plan provided by the mineral processing department, determine the daily planned truck usage. While meeting the production plan's requirements for output and grade, minimize truck usage to save costs. Specifically, this includes:

[0104] Step 1.1: Calculate the maximum number of times a single truck can load and unload ore per day in the electric shovel ore loading area i∈(1,p). r1 As shown in the following formula:

[0105]

[0106] Where i∈(1,p) is the electric shovel ore loading area, and i∈(p+1,I) is the electric shovel rock loading area; the daily plan includes two shifts, day shift and night shift, and the working time of each shift is T. l ;T i1 d represents the average time taken by a truck to complete one loading / unloading cycle in the electric shovel ore loading area. i1 For the optimal distance s of the truck from ore loading point i to ore crushing station f Let s be the average speed of the truck when it is heavily loaded. n T is the average speed of the truck when it is unloaded. z T is the average time for truck loading. q γ is the average time for unloading the truck; γ is the control coefficient, adjusted according to the actual daily planned downtime or maintenance time of the truck; T c For truck maintenance time.

[0107] Here, we assume that each truck is the same model, i.e., has the same loading capacity. The mineral processing department roughly estimates the required amount of ore based on the required ore grade and the ore type and grade of each electric shovel loading area, as a reference for the daily planning operation of the production scheduling room. However, the estimated ore output has a large error compared with the actual production output.

[0108] Step 1.2: Based on the daily plan, refer to the truck configuration ratio m1:m2:...:m p Calculate the daily planned number of electric shovels z for each electric shovel ore loading area i∈(1,p). i1 Round up using the following formula:

[0109]

[0110] If the ratio of electric shovel to truck is required to be 1:2 or 1:3, then z must be satisfied. i1 ={2,3}, and m i The reference number of times the truck loads and unloads ore for the amount of ore produced in the electric shovel loading area i.

[0111] Step 1.3: Calculate the maximum number of times a single truck can load and unload ore per day in the electric shovel rock loading area i∈(p+1,I). r2 The formula is as follows:

[0112]

[0113] Among them, T i2 d represents the average time taken by a truck to complete one loading / unloading cycle in the electric shovel rock loading area. i2 The optimal distance for the truck from rock loading point i to the rock crushing station.

[0114] Step 1.4: Calculate the daily planned number of electric shovels z for each rock loading zone i∈(p+1,I).i2 Since the mineral processing department only provides the ore demand plan ratio, and the rock demand is evenly distributed according to the annual plan with minor daily adjustments given by the production scheduling office, it is only necessary to meet the target given by the production scheduling office. The formula is as follows:

[0115]

[0116] If the ratio of electric shovel to truck is required to be 1:2 or 1:3, then z must be satisfied. i2 ={2,3}, and f2 represents the minimum production requirement at the rock unloading point within one shift, C r This refers to the load capacity of the truck.

[0117] Step 1.5: Calculate the number of trucks to be dispatched in the daily plan:

[0118]

[0119] Step 2: Establish the objective function for a multi-objective traffic flow planning and scheduling model for open-pit iron mines. The model established in this invention uses the minimum total transportation cost, maximum ore output, and minimum total idle time as its objective functions, as detailed below:

[0120] The objectives of the daily truck dispatch plan include minimizing truck transportation costs, maximizing ore production, and minimizing total truck idle time. The model objective function is:

[0121] F(S)={F1(S),-F2(S),F3(S)};

[0122] Here, F1(S), F2(S), and F3(S) are the sub-objectives in the objective function, representing minimizing the total truck transportation cost, maximizing ore production, and minimizing the total truck idle time, respectively.

[0123] Truck transportation costs consist of three parts: the operating cost of a fully loaded truck traveling from the loading point to the unloading point, the operating cost of an empty truck traveling from the unloading point to the loading point or to / from refueling points, and the truck maintenance cost. The objective function expression for minimizing the total truck transportation cost is:

[0124]

[0125] The objective function for maximizing ore production is specifically expressed as maximizing ore transportation while minimizing rock transportation, and its expression is:

[0126]

[0127] And convert it to:

[0128]

[0129] The idle time of a single truck is the difference between the planned working hours for each shift and the necessary time for driving, loading, unloading, refueling, and inspection. The objective function expression for minimizing the total idle time of trucks is:

[0130]

[0131] Where i is the loading point index number, representing the i-th loading point (i.e., the location of the electric shovel), i = 1, 2, ..., I; j is the unloading point index number, representing the j-th unloading point (i.e., the location of the crushing station), j = 1, 2, ..., J; r is the truck index number, representing the r-th truck, r = 1, 2, ..., R; x rij Let x be the number of times the r-th truck travels from loading point i to unloading point j. rji Let d be the number of times the r-th truck travels from unloading point j to loading point i. i,j The optimal distance d from loading point i to unloading point j jO The optimal distance from unloading point j to refueling point O, d Oi The optimal distance from refueling point O to loading point i, C r1 Let C be the unit distance cost of the r-th truck when it is heavily loaded. r2 Let C be the unit distance cost of the r-th truck when it is unloaded. r3 Let f1 be the wear and tear cost (i.e., maintenance cost) of the r-th truck per unit time, and T be the minimum production demand at the ore unloading point within one shift. O The average time to refuel a truck, K rjO K represents the number of times truck r travels from unloading point j to refueling point O. rOi Let r be the number of times the truck travels from refueling point O to loading point i.

[0132] Step 3: Set constraints for the multi-objective vehicle flow planning and scheduling model for open-pit iron mines, including ore grade constraints at the ore crushing station, production plan constraints at the ore crushing station, production capacity constraints at the electric shovel loading point, vehicle flow continuity constraints, remaining fuel constraints, and truck transport frequency constraints. Specifically, this includes:

[0133] Ore grade constraints for ore crushing plants: The deviation between the actual daily planned grade and the given grade must not exceed the maximum grade deviation β, as shown in the following formula:

[0134]

[0135] Where, α i Let β be the ore grade at loading point i, β be the allowable deviation of ore grade, and G1 be the target grade of the ore crushing station.

[0136] Production planning constraints for ore crushing stations: The total amount of ore unloaded from the ore crushing station must meet the minimum daily production demand f1 per shift, and the total amount of rock unloaded from the rock crushing station must meet the minimum daily production demand f2 per shift, as shown in the following two equations:

[0137]

[0138] Electric shovel loading point production capacity constraint: The total amount of ore loaded at each electric shovel loading point must not exceed the maximum production capacity g of that loading point. i As shown in the following formula:

[0139]

[0140] Traffic flow continuity constraint: The inbound and outbound traffic flow at loading / unloading points must be equal, as shown in the following formula:

[0141]

[0142] Remaining fuel constraint: It is necessary to monitor the remaining fuel level of each truck. When it drops to the minimum value A, it needs to be refueled at a refueling point. The remaining fuel level A = maximum fuel consumption from unloading point to loading point + maximum fuel consumption from loading point to unloading point + maximum fuel consumption from unloading point to refueling point. The constraint formula is as follows:

[0143]

[0144] Where E is the truck's fuel tank capacity, and A is the truck's minimum remaining fuel level. r1 Let E be the fuel consumption per unit distance for the r-th truck under heavy load. r2 Let r be the fuel consumption per unit distance when the r-th truck is unloaded.

[0145] Truck transport frequency constraints: The number of truck transports must be a positive integer, and ore and rock trucks must be unloaded at their respective unloading points when fully loaded. Empty trucks at unloading points can go to any loading point. For the traffic flow planning and scheduling problem of mixed ore and rock transport, let I be the number of loading points for ore (the first p points) and rock (the last I p points), and let J be the number of unloading points, with the first unloading point being the ore crushing station and the second unloading point being the rock crushing station. The constraints are as follows:

[0146] x rij ∈{0,1,2...},x rji ∈{0,1,2...};

[0147] in,

[0148] Step 4: Based on the truck usage calculated in Step 1 and the given number of electric shovel loading areas for the day, combined with the objective functions and constraints of Steps 2 and 3, apply a multi-objective intelligent optimization algorithm to solve the model and obtain the Pareto optimal solution set. The multi-objective intelligent optimization algorithm uses an improved multi-objective particle swarm optimization algorithm (MOPSO) to solve the multi-objective optimization problem of open-pit iron ore truck flow planning and scheduling. This algorithm introduces several improvements on the standard multi-objective particle swarm optimization algorithm MOPSO to improve its performance in handling complex constraints and multi-objective optimization problems. For example... Figure 2 As shown, the specific steps are as follows:

[0149] Step 4.1: Particle encoding and initialization;

[0150] Each particle represents a feasible scheduling scheme, encoded as a multidimensional vector X = (x1, x2, ..., x...). n ), where n is the number of decision variables; during initialization, particle positions are randomly generated under the condition of satisfying constraints.

[0151] Step 4.2: Based on the objective function established in Step 2, define a multi-objective fitness function: F(S) = {F1(S), -F2(S), F3(S)}, where F1(S) represents the objective of minimizing the total transportation cost of trucks, F2(S) represents the objective of maximizing ore production, and F3(S) represents the objective of minimizing the total idle time of trucks.

[0152] Step 4.3: Introduce an adaptive weighting strategy. The particle velocity and position update formulas are as follows:

[0153]

[0154] Among them, v id (t+1) v is the velocity of the i-th particle in the d-th dimension at time t+1; id t Let be the velocity of the i-th particle in the d-th dimension at time t; c1 is the individual learning factor, which determines the strength of the particle's tendency to move towards its individual historical best position; c2 is the social learning factor, which determines the strength of the particle's tendency to move towards the group's historical best position; r1 and r2 are random numbers between [0,1], regenerated in each iteration to increase the randomness of the search; p id t p represents the individual optimal position in the d-th dimension of the i-th particle's historical position, i.e., the individual optimal solution. gd t Let x be the historical optimal position of the group in the d-th dimension, i.e., the global optimal solution. id t Let x be the current position of the i-th particle in the d-th dimension at time t. id(t+1) Let be the current position of the i-th particle in the d-th dimension at time t+1; w(t) is the adaptive inertia weight, which is dynamically adjusted using the following formula:

[0155] w(t) = w max -(w max -w min )*(t / T max ) 2 ;

[0156] Among them, w max and w min These represent the maximum and minimum values ​​of the inertia weight, respectively, where t is the current iteration number, and T is the maximum and minimum values. max This represents the maximum number of iterations.

[0157] Step 4.4: Maintenance of non-dominated solution sets.

[0158] A fast non-dominated sorting algorithm is used to sort the particles, and crowding distance is used to maintain the diversity of the non-dominated solution set; the crowding distance calculation formula is as follows:

[0159]

[0160] Among them, CD i Let M be the crowding distance of the i-th solution, and M be the number of objective functions. and Let f be the function value of the m-th objective function among its adjacent solutions after sorting. m max and f m min These are the maximum and minimum values ​​of the m-th objective function, respectively.

[0161] Step 4.5: Local search mechanism.

[0162] After each iteration, a simulated annealing strategy is used to perform a local search on a subset of solutions in the non-dominated solution set. During the local search, to facilitate the algorithm escaping local optima, a certain probability is allowed to be used to accept worse solutions, i.e., an acceptance probability. The formula for calculating the acceptance probability is:

[0163] P(ΔE,T)=exp(-ΔE / T);

[0164] Where ΔE is the difference between the objective function of the new solution and the current solution, and T is the current temperature, which gradually decreases with the number of iterations.

[0165] Step 4.6: Constraint handling mechanism.

[0166] Use an improved constraint dominance rule to handle constraints and define the constraint violation degree:

[0167]

[0168] Among them, g q (X)≤0 is an inequality constraint, q=1,2,...,Q; h k (X) = 0 represents an equality constraint, and k = 1, 2, ..., K. When comparing two solutions, their constraint violation degrees are compared first; the solution with the smaller constraint violation degree is preferred over the solution with the larger constraint violation degree. When the constraint violation degrees are equal, the objective function values ​​are then compared.

[0169] Step 4.7: Algorithm termination condition.

[0170] The algorithm iterates until one of the following conditions is met: the preset maximum number of iterations is reached, or the non-dominated solution set does not change after N consecutive iterations.

[0171] Step 5: Based on the Pareto optimal solution set obtained in Step 4, select an optimal solution as the final scheduling scheme. Based on the truck scheduling operation results provided by the selected scheme, revise the actual daily planned output and grade for scheduling operation reference decision-making.

[0172] Since the optimization model is a multi-objective model, a set of complementary Pareto optimal solutions will be obtained during the solution process. These solutions all meet the constraints in the model but correspond to different objective function values. The three objectives in the model are in conflict with each other to some extent.

[0173] Based on the Pareto optimal solution set obtained in step 4, the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is used to select an optimal solution from the final non-dominated solution set as the final scheduling scheme. The TOPSIS method is based on the following steps:

[0174] Construct a normalized decision matrix: Calculate the weighted normalized decision matrix; determine the ideal solution (the hypothetical solution where each indicator achieves the best value) and the negative ideal solution (the hypothetical solution where each indicator achieves the worst value); calculate the distance of each solution to the ideal solution and the negative ideal solution; calculate the relative proximity of each solution; rank the solutions according to their relative proximity.

[0175] The method for calculating the distances from each solution to the ideal solution and the negative ideal solution is as follows:

[0176] Let the weighted normalized value of the i-th scheme on the j-th index be v. ij The ideal solution has a value of v for the j-th index. j+ The value of the negative ideal solution on the j-th index is v. j- Then the distance S from the i-th solution to the ideal solution is... i+S to the negative ideal solution i- The distance can be calculated using Euclidean distance:

[0177]

[0178] Where n is the number of evaluation indicators.

[0179] The method for calculating the relative proximity of each option is as follows:

[0180] The relative closeness C of the i-th scheme i The following formula can be used to calculate:

[0181]

[0182] Relative proximity C i The value range is [0,1], and the closer it is to 1, the better the solution is.

[0183] The method for ranking the schemes based on their relative proximity is as follows:

[0184] All schemes are ranked according to their relative proximity C i Sort from largest to smallest, C i The solution with the largest value is the optimal solution.

[0185] Based on the truck dispatching results of the selected plan, the daily planned actual output and grade are revised for dispatching operation reference. The revised formulas for daily planned ore output, rock output, and grade are as follows:

[0186]

[0187] in, The revised daily planned ore production For the corrected rock yield, For the corrected grade, α i Let i be the ore grade at loading point i.

[0188] This embodiment proposes an open-pit iron ore mine truck flow planning and scheduling method that first considers ore blending and truck allocation, and then performs multi-objective optimization. This method first estimates truck usage based on the daily planned ore blending ratio provided by the ore beneficiation department, and then establishes a multi-objective optimization model with the objectives of minimizing total transportation costs, maximizing ore output, and minimizing total idle time. This method better aligns with actual production needs and can achieve cost savings while ensuring production plans are met. This embodiment employs an improved multi-objective particle swarm optimization algorithm (MOPSO) to solve the truck flow planning and scheduling model. This algorithm introduces an adaptive weight strategy, a congestion ranking strategy, and a local search mechanism, and designs a dedicated constraint handling mechanism. This improved algorithm can effectively handle multiple objectives and complex constraints in the open-pit iron ore mine truck flow planning and scheduling problem, improving the efficiency and quality of the solution. The method in this embodiment can correct the actual daily planned output and grade based on the optimization results, providing a reference for scheduling operation decisions. This correction function allows schedulers to evaluate the feasibility and effectiveness of the scheduling plan before actual execution, helping to improve the accuracy of decision-making and production efficiency.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A method for optimizing the planning and scheduling of train traffic in an open-pit iron mine, characterized in that: Includes the following steps: Step 1: Determine the daily truck usage plan based on the reference truck configuration ratio for each ore loading area in the daily plan provided by the mineral processing department; While meeting the production plan's requirements for output and quality, the use of trucks should be minimized to save costs; specifically including: Step 1.1: Calculate the load of a single truck in the electric shovel ore loading area. Maximum number of times ore can be loaded and unloaded per day As shown in the following formula: ； ； in, This is the electric shovel ore loading area. This is the electric shovel rock loading area; the daily schedule includes two shifts, a day shift and a night shift, with each shift's working hours being... ; This refers to the average time taken for a truck to complete one loading / unloading cycle in the electric shovel ore loading area. For trucks from ore loading point The optimal distance to the ore crushing station. The average speed of the truck when heavily loaded. The average speed of the truck when it is unloaded. The average time for loading the truck, The average time taken to unload the truck; The control coefficient is adjusted based on the actual daily scheduled suspension or maintenance time of the trucks; For truck maintenance time; Step 1.2: Refer to the truck configuration ratio based on the daily plan. Calculate the ore loading area for each electric shovel. Daily planned vehicle usage Round up using the following formula: ； If the ratio of electric shovel to truck is required to be 1:2 or 1:3, then the following must be met: ,and , Loading area for electric shovels The number of times ore is loaded and unloaded by trucks is used as a reference for the amount of ore produced; Step 1.3: Calculate the load of a single truck in the electric shovel rock loading area. Maximum number of times ore can be loaded and unloaded per day The formula is as follows: ； ； in, This represents the average time taken for a truck to complete one loading / unloading cycle in the electric shovel rock loading area. For trucks from rock loading point Optimal distance to the rock crushing station; Step 1.4: Calculate the rock loading zone for each electric shovel Daily planned vehicle usage Since the mineral processing department only provides the ore demand plan ratio, and the rock demand is evenly distributed according to the annual plan with minor daily adjustments given by the production scheduling office, it is only necessary to meet the target given by the production scheduling office. The formula is as follows: ； If the ratio of electric shovel to truck is required to be 1:2 or 1:3, then the following must be met: ,and ; This represents the minimum production requirement for the rock unloading point within one shift. For the truck's load capacity; Step 1.5: Calculate the number of trucks to be dispatched in the daily plan: ； Step 2: Establish the objective function of the multi-objective traffic flow planning and scheduling model for open-pit iron mines; the established model takes the minimum total transportation cost, maximum ore output, and minimum total idle time as its objective functions; Step 3: Set constraints for the multi-objective vehicle flow planning and scheduling model for open-pit iron mines, including ore grade constraints for ore crushing stations, production plan constraints for ore crushing stations, production capacity constraints for electric shovel loading points, vehicle flow continuity constraints, remaining fuel constraints, and truck transport frequency constraints. Step 4: Based on the truck usage calculated in Step 1 and the given number of electric shovel loading areas for the day, combined with the objective functions and constraints of Steps 2 and 3, apply a multi-objective intelligent optimization algorithm to solve the model and obtain the Pareto optimal solution set. The multi-objective intelligent optimization algorithm employs an improved multi-objective particle swarm optimization algorithm to solve the multi-objective optimization problem of open-pit iron mine traffic planning and scheduling. This algorithm introduces several improvements to the standard multi-objective particle swarm optimization algorithm to enhance its performance in handling complex constraints and multi-objective optimization problems. The specific steps are as follows: Step 4.1: Particle encoding and initialization; Each particle represents a feasible scheduling scheme, encoded as a multidimensional vector. , where n is the number of decision variables; during initialization, particle positions are randomly generated while satisfying the constraints; Step 4.2: Based on the objective function established in Step 2, define the multi-objective fitness function: ,in This indicates the objective of minimizing the total transportation cost of trucks. This indicates the objective of maximizing ore production. This represents the objective of minimizing the total idle time of trucks; Step 4.3: Introduce an adaptive weighting strategy. The particle velocity and position update formulas are as follows: ； in, Let be the velocity of the i-th particle in the d-th dimension at time t+1; Let be the velocity of the i-th particle in the d-th dimension at time t; As an individual learning factor, it determines the strength of the tendency for particles to move towards their historical best position. As a social learning factor, it determines the strength of the tendency for particles to move toward the group's historical best position; , for Random numbers between these values ​​are regenerated in each iteration to increase the randomness of the search. Let be the individual historical optimal position of the i-th particle in the d-th dimension, which is also the individual optimal solution; Let d be the historical optimal position of the group in the d-th dimension, which is also the global optimal solution. Let be the current position of the i-th particle in the d-th dimension at time t. Let be the current position of the i-th particle in the d-th dimension at time t+1; The adaptive inertia weights are dynamically adjusted using the following formula: ； in, and These are the maximum and minimum values ​​of the inertia weight, respectively, and t is the current iteration number. This represents the maximum number of iterations. Step 4.4: Maintenance of non-dominated solution sets; A fast non-dominated sorting algorithm is used to sort the particles, and crowding distance is used to maintain the diversity of the non-dominated solution set; the crowding distance calculation formula is as follows: ； in, Let M be the crowding distance of the i-th solution, and M be the number of objective functions. and These are the function values ​​of the m-th objective function and its adjacent solutions after sorting. and These are the maximum and minimum values ​​of the m-th objective function, respectively; Step 4.5: Local search mechanism; After each iteration, a simulated annealing strategy is used to perform a local search on a subset of solutions in the non-dominated solution set. During the local search, to facilitate the algorithm escaping local optima, a certain probability is allowed: accepting a less optimal solution. The formula for calculating the acceptance probability is as follows: ； in, The objective function difference between the new solution and the current solution is represented by T, which is the current temperature and gradually decreases with the number of iterations. Step 4.6: Constraint handling mechanism; Use an improved constraint dominance rule to handle constraints and define the constraint violation degree: ； in, For inequality constraints, ; For equality constraints, When comparing two solutions, first compare their constraint violation rates; the solution with a lower constraint violation rate is preferred over the solution with a higher constraint violation rate. Step 4.7: Algorithm termination condition; The algorithm iterates until either of the following conditions is met: the preset maximum number of iterations is reached, or the non-dominated solution set does not change after N consecutive iterations; Step 5: Based on the Pareto optimal solution set obtained in Step 4, select an optimal solution as the final scheduling scheme. Based on the truck scheduling operation results provided by the selected scheme, revise the actual daily planned output and grade for scheduling operation reference decision-making.

2. The open-pit iron ore mine traffic flow planning and scheduling optimization method according to claim 1, characterized in that: The objective function in step 2 specifically includes: The objectives of the daily truck dispatch plan include minimizing truck transportation costs, maximizing ore production, and minimizing total truck idle time; the model objective function is: ； in, , , Let be the sub-objectives in the objective function, representing minimizing the total truck transportation cost, maximizing ore production, and minimizing the total truck idle time, respectively. Truck transportation costs consist of three parts: the operating cost of a heavily loaded truck from the loading point to the unloading point, the operating cost of an empty truck from the unloading point to the loading point or to / from refueling points, and the truck maintenance cost; the objective function expression for minimizing the total truck transportation cost is: ； The objective function for maximizing ore production is specifically expressed as maximizing ore transportation while minimizing rock transportation, and its expression is: ； And convert it to: ； The idle time of a single truck is the difference between the planned working hours for each shift and the necessary time for driving, loading, unloading, refueling, and inspection. The objective function expression for minimizing the total idle time of trucks is: ； Where i is the loading point index number, indicating the location of the i-th loading point, i.e., the electric shovel. ;j is the unloading point index number, indicating the location of the j-th unloading point, i.e., the crushing station. ; r is the index number of the truck, indicating the r-th truck. ; Let be the number of times the r-th truck travels from loading point i to unloading point j. Let be the number of times the r-th truck travels from unloading point j to loading point i. The optimal distance from loading point i to unloading point j The optimal distance from unloading point j to refueling point O The optimal distance from refueling point O to loading point i Let r be the unit distance cost when the r-th truck is heavily loaded. Let r be the unit distance cost of the r-th truck when it is unloaded. Let r be the wear and tear cost, or maintenance cost, of the r-th truck per unit time. This represents the minimum production requirement for one shift at the ore unloading point. The average time to refuel a truck Let r be the number of times truck r travels from unloading point j to refueling point O. Let r be the number of times the truck travels from refueling point O to loading point i.

3. The open-pit iron ore mine traffic flow planning and scheduling optimization method according to claim 2, characterized in that: The constraints in step 3 specifically include: Ore grade constraints for ore crushing plants: The deviation between the actual grade and the given grade on a daily basis must not exceed the maximum grade deviation. As shown in the following formula: ； in, Let i be the ore grade at loading point i. This is the allowable deviation for ore grade. The target grade for the ore crushing station; Production planning constraints for ore crushing stations: The total amount of ore unloaded from the ore crushing station must meet the minimum demand of each shift in the daily ore production plan. The total amount of rock unloaded at the rock crushing station must meet the minimum demand of each shift in the daily rock production plan. The following two formulas: ； ； Electric shovel loading point production capacity constraint: The total amount of ore loaded at each electric shovel loading point must not exceed the maximum production capacity of that loading point. As shown in the following formula: ； Traffic flow continuity constraint: The inbound and outbound traffic flow at loading / unloading points must be equal, as shown in the following formula: ； Remaining fuel constraint: It is necessary to monitor the remaining fuel level of each truck. When it drops to the minimum value A, it needs to be refueled at a refueling point. The remaining fuel level A = maximum fuel consumption from unloading point to loading point + maximum fuel consumption from loading point to unloading point + maximum fuel consumption from unloading point to refueling point. The constraint formula is as follows: ； Where E is the truck's fuel tank capacity, and A is the truck's minimum remaining fuel level. Let r be the fuel consumption per unit distance when the r-th truck is heavily loaded. Let r be the fuel consumption per unit distance when the r-th truck is unloaded; Truck transport constraints: The number of truck transport trips must be a positive integer, and fully loaded trucks must proceed to the ore and rock unloading points for unloading. Empty trucks at the unloading points can proceed to any loading point. For the traffic flow planning and scheduling problem of mixed ore and rock transport, let I be the number of electric shovel loading points, where the first p are ore loading points and the last p are... Let J be the rock loading points, and let the first unloading point be the ore crushing station and the second unloading point be the rock crushing station. The constraints are as follows: ； in, .

4. The open-pit iron ore mine traffic flow planning and scheduling optimization method according to claim 3, characterized in that: The details of step 5 are as follows: Based on the Pareto optimal solution set obtained in step 4, the TOPSIS method is used to select an optimal solution from the final non-dominated solution set as the final scheduling scheme. Based on the truck dispatching results of the selected plan, the daily planned actual output and grade are revised for dispatching operation reference. The revised formulas for daily planned ore output, rock output, and grade are as follows: ； in, The revised daily planned ore production For the corrected rock yield, For the corrected taste, Let i be the ore grade at loading point i.

5. The open-pit iron ore mine traffic flow planning and scheduling optimization method according to claim 4, characterized in that: The TOPSIS method is based on the following steps: Construct a normalized decision matrix: Calculate the weighted normalized decision matrix; determine the ideal solution and the negative ideal solution, where the ideal solution is the hypothetical solution in which each indicator achieves the best value, and the negative ideal solution is the hypothetical solution in which each indicator achieves the worst value; calculate the distance of each solution to the ideal solution and the negative ideal solution; calculate the relative proximity of each solution; rank the solutions according to their relative proximity. The method for calculating the distances from each solution to the ideal solution and the negative ideal solution is as follows: Let the weighted normalized value of the i-th scheme on the j-th index be... The ideal solution has the value of the j-th index. The value of the negative ideal solution on the j-th index is Then the distance from the i-th solution to the ideal solution is... Sum to the negative ideal solution The distance is calculated using Euclidean distance, as shown in the following formula: ； Where n is the number of evaluation indicators; The method for calculating the relative proximity of each scheme is as follows: Relative closeness of the i-th solution Calculate using the following formula: ； relative proximity The range of values ​​is The closer the value is to 1, the better the solution. The method for ranking the schemes based on relative proximity is as follows: All schemes are ranked according to their relative proximity Sort from largest to smallest. The solution with the largest value is the optimal solution.

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