Coal machine equipment production plan optimization method based on variable neighborhood search algorithm

Abstract

This invention provides a method for optimizing coal mining equipment production planning based on a variable neighborhood search algorithm, belonging to the field of engineering scheduling technology. The method includes: 1. Initializing algorithm parameters; 2. Constructing a priority ranking set and a total cost function; 3. Setting and optimizing an initial solution; 4. Generating and updating a neighborhood solution set to obtain a candidate solution set; 5. Selecting a neighborhood structure; 6. Performing a neighborhood search to obtain a local optimum; 7. Updating the optimal solution; 8. Optimizing the optimal solution; and 9. Outputting the global optimum after a certain number of iterations. This invention improves the initial solution and neighborhood structure of the variable neighborhood search algorithm, optimizing the quality of the initial solution and avoiding premature entrapment in local optima. It can obtain approximate optimal solutions for coal mining equipment production planning optimization problems, providing more effective decision support for decision-makers, thereby improving the efficiency and effectiveness of batch production of equipment.

Description

Technical Field

[0001] This invention relates to the field of engineering scheduling technology, specifically to a method for optimizing the production plan of coal mining equipment based on a variable neighborhood search algorithm. Background Technology

[0002] With the rapid development of intelligent manufacturing and industrial internet technologies, the coal mining equipment manufacturing industry is facing unprecedented opportunities and challenges. Among these, the mass production and operation and maintenance of coal mining equipment play a crucial role. Efficient mass production can significantly reduce production costs, shorten production cycles, and improve product quality and market competitiveness.

[0003] Traditional coal mining equipment production planning optimization is often based on fixed parameters and relies heavily on human experience. The accuracy of such manually formulated plans cannot be guaranteed. Furthermore, due to the real-time changes in multi-source data such as order demand and after-sales demand, traditional production planning cannot be updated in a timely manner.

[0004] Therefore, it is necessary to design a production planning optimization method that can accurately formulate and optimize production plans based on actual market demand and production conditions. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a method for optimizing coal mining equipment production planning based on a variable neighborhood search algorithm, which solves the problem that traditional production planning cannot accurately respond to actual market demand and production conditions.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] In a first aspect, the present invention provides a method for optimizing coal mining equipment production planning based on an improved variable neighborhood search algorithm, comprising:

[0010] S1 initializes the various parameters in the algorithm, including production parameters and execution parameters; the production parameters include complete equipment and spare parts production information, customer information, delay penalty costs, production capacity constraints, order requirements, production costs, production efficiency, and delivery time; the execution parameters include the maximum number of iterations. Current iteration number Crossover probability Local search algorithm population size ;

[0011] S2 prioritizes complete sets of equipment and spare parts according to the difference between processing time and expected delivery time; constructs a total cost function with the goal of minimizing production cost and delivery time penalty cost;

[0012] S3, Generate an initial solution based on the priority sorting result, optimize the initial solution with the goal of minimizing the total cost function, and set the optimized initial solution as the global optimal solution;

[0013] S4. Generate and update the neighborhood solution set based on the global optimal solution to obtain the candidate solution set;

[0014] S5, based on the probability selection of roulette wheel selection to obtain the local search neighborhood structure;

[0015] S6. Obtain the neighborhood and search it according to the candidate solution set and the local search neighborhood structure to obtain the local optimal solution;

[0016] S7. Based on the total cost function, determine the merits of the local optimal solution and the candidate solution set. If the local optimal solution is superior, update the local optimal solution to the global optimal solution and increase the weight of the neighborhood structure that obtained the local optimal solution. If the candidate solution set is superior, decrease the weight of the neighborhood structure that obtained the local optimal solution and return to step S5.

[0017] S8. Based on the greedy algorithm, the global optimal solution is greedily destroyed and greedily reconstructed to obtain a new solution. The new solution is compared with the global optimal solution. If the global optimal solution is better, the improved Boltzmann function is used to determine whether to accept the new solution and return to step S4; otherwise, the new solution is assigned as the global optimal solution.

[0018] S9, Assign to ,judge If the condition is met, return to step S4; otherwise, the algorithm execution ends, and the global optimal solution is output as the optimal coal mining equipment production plan.

[0019] Preferably, the roulette wheel probability selection includes:

[0020] The initial weights of each local search neighborhood structure are assumed to be equal.

[0021] The cumulative probability of each local search neighborhood structure is calculated based on the weight of each local search neighborhood structure and the roulette wheel probability.

[0022] The neighborhood structure is selected for local search based on the cumulative probability.

[0023] Preferably, the step of using a greedy algorithm to greedily destroy and greedily reconstruct the global optimal solution to obtain a new solution includes:

[0024] Based on the production plan for all months corresponding to the global optimal solution epsilon-greedy The rule selects several months;

[0025] Randomly select a complete set of equipment or spare parts from the month mentioned above, and record the selected set of products as follows: The remaining product set is denoted as ;

[0026] The The products in the collection are inserted one by one into the... The set is used to calculate the total production cost based on the total cost function at all possible positions other than the initial position.

[0027] Set the insertion position with the lowest total production cost as the new solution.

[0028] Preferably, the local search neighborhood structure includes ~ , Indicates the number of indexes for the device. Indicates the number of spare parts indexed;

[0029] The To define a variable x, a random integer within the interval [1, P] is generated and assigned to variable x. All codes to the left of x in the solution set of the neighborhood are processed in reverse order. Then, resource selection for all positions is performed probabilistically. Make a random selection;

[0030] The To define a variable x, a random integer within the interval [1, Q] is generated and assigned to variable x. The codes to the right of x in the neighborhood solution set are then reversed. Finally, resource selection for all positions is performed based on probability. Make a random selection;

[0031] The To define variables x and y, generate two integers in the range [1, P] and [P+1, P+Q] respectively, and assign them to variables x and y. The x-th position in the neighborhood solution set corresponds to the allocation month code of the complete set of equipment, and the y-th position corresponds to the allocation month code of the spare parts. Swap the month numbers corresponding to the x-th and y-th positions in the neighborhood solution set.

[0032] The To define variables x, y, and P + Q = n, two integers within the ranges [1, P] and [P + 1, P + Q] are respectively generated and assigned to variables x and y. Among them, the x-th position in the neighborhood solution set corresponds to the allocation month code of the complete set of equipment, and the y-th position corresponds to the allocation month code of spare parts. The codes to the left of position x and the codes to the right of position y are reversed, and then resources at all positions are randomly selected with a probability of (x + y) / 2n;

[0033] The To define variables x, y, and P + Q = n, without replacement, two integers within the range [1, n] are randomly generated and assigned to variables x and y, where x ≤ y. The codes on both sides of positions x and y in the neighborhood solution set are exchanged, and then resources at all positions are randomly selected with a probability of (x + y) / 2n;

[0034] The To define variables x, y, z, and P + Q = n, without replacement, three integers within the range [1, n] are randomly obtained and assigned to variables x, y, z, where x < y < z. The codes on both sides of positions x and z in the neighborhood solution set are reversed, and the codes inside positions x, y and y, z are exchanged, and then resources at all positions are randomly selected with a probability of (x + y + z) / 3n;

[0035] The To define variables x, y, z, and P + Q = n, without replacement, three integers within the range [1, n] are randomly obtained and assigned to variables x, y, z, where x < y < z. The codes on both sides of positions x and z in the neighborhood solution set are exchanged, and the codes inside positions x, y and y, z are reversed, and then resources at all positions are randomly selected with a probability of (x + y + z) / 3n;

[0036] The To define variables x, y, z, and P + Q = n, without replacement, three integers within the range [1, n] are randomly obtained and assigned to variables x, y, z, where x < y < z. The codes on both sides of positions x and z in the neighborhood solution set and the codes inside positions x, y and y, z are reversed, and then resources at all positions are randomly selected with a probability of (x + y + z) / 3n;

[0037] The To define variables , , without replacement, four integers within the range [1, n] are randomly obtained and assigned to variables, , where, , the ones located in the neighborhood solution set and Reverse the encoding within the position, and The encoding is exchanged within the locations between them, and then resources are selected probabilistically for all locations. Make a random selection.

[0038] Preferably, obtaining the candidate solution set specifically includes:

[0039] S41, a neighborhood solution set is generated based on the initial solution, and the solution set is denoted as... ;in, The CCP is considering the neighborhood solution. Individual, The first solution in the neighborhood is represented by the second solution. There are individuals, and each individual is determined by the initial solution. Random exchange This was the first time it was obtained;

[0040] S42, Define variables The variable and Having the same meaning, and putting Individual values ​​assigned to ;

[0041] S43, Define variables and Let the variable Let the variable , will variables Assigned to the interval Random numbers generated within a range; where, and With individuals Same dimensions;

[0042] S44, using the formula renew ,in Indicates the th in the global optimal solution One element, , In the interval respectively Random numbers generated within a range;

[0043] S45, in the interval Generate random numbers within the range and judge Is it true? If it is true, then... Assign to Otherwise Assign to ;

[0044] S46, Order ,judge Check if the condition is met. If it is met, return to step S44; otherwise, proceed to step S47.

[0045] S47, calculate the individual costs according to the total cost function. and intermediates fitness value and and compare and ,like Then put Assigning values ​​to individuals ;

[0046] S48, order ,judge Check if the condition is met. If it is met, return to step S43; otherwise, proceed to step S49.

[0047] S49. Based on the total cost function, select the better-quality individuals in the neighborhood solution set and assign them to the candidate solution set.

[0048] Preferably, the total cost function is:

[0049]

[0050] In the formula, Indicates the total cost; This indicates minimizing production costs; This indicates minimizing the cost of delivery time penalties; Indicates a complete set of equipment index; Indicates spare parts index; Indicates the customer index; Indicates the month index; Indicates complete set of equipment Production time; Indicates complete set of equipment Production completion time; Indicates spare parts Production time; Indicates spare parts Production completion time; Indicates manufacturer Monthly production complete set of equipment The cost; Indicates manufacturer Monthly production spare parts The cost; Indicates customer The complete set of equipment ordered; Indicates customer The collection of ordered spare parts; Indicates customer Ordered complete sets of equipment Quantity; Indicates customer Ordered spare parts Quantity; Indicates customer Ordered complete sets of equipment The unit delay penalty cost; Indicates customer Ordered complete sets of equipment Expected delivery time; Indicates customer Ordered spare parts The unit delay penalty cost; Indicates customer Ordered spare parts Expected delivery time; Indicates if complete set of equipment Allocated to months If production is initiated, the value is 1; otherwise, it is 0. Indicates if spare parts Allocated to months If production is initiated, the value is 1; otherwise, it is 0. Indicates if the customer Ordered complete set of equipment If the result is positive, then it equals 1; otherwise, it equals 0. If the customer Spare parts ordered If the result is positive, then it equals 1; otherwise, it equals 0.

[0051] Preferably, optimizing the initial solution with the aim of minimizing the total cost function includes:

[0052] In the sorted production sequence, new production tasks are inserted into the production sequence with the objective of minimizing the total cost function.

[0053] Secondly, the present invention also provides a coal mining equipment production planning optimization system based on an improved variable neighborhood search algorithm, comprising:

[0054] The calculation module is used to perform the following steps:

[0055] S1 initializes the various parameters in the algorithm, including production parameters and execution parameters; the production parameters include complete equipment and spare parts production information, customer information, delay penalty costs, production capacity constraints, order requirements, production costs, production efficiency, and delivery time; the execution parameters include the maximum number of iterations. Current iteration number Crossover probability Local search algorithm population size ;

[0056] S2 prioritizes complete sets of equipment and spare parts according to the difference between processing time and expected delivery time; constructs a total cost function with the goal of minimizing production cost and delivery time penalty cost;

[0057] S3, generate an initial solution according to the priority sorting, optimize the initial solution with the aim of minimizing the total cost function, and set the optimized initial solution as the optimal solution;

[0058] S4. Generate and update the neighborhood solution set based on the initial solution to obtain the candidate solution set;

[0059] S5, based on the probability selection of roulette wheel selection to obtain the local search neighborhood structure;

[0060] S6. Obtain the neighborhood and search it according to the candidate solution set and the local search neighborhood structure to obtain the local optimal solution;

[0061] S7. Based on the total cost function, determine the merits of the local optimal solution and the candidate solution set. If the local optimal solution is superior, update the local optimal solution to the global optimal solution and increase the weight of the neighborhood structure that obtained the local optimal solution. If the candidate solution set is superior, decrease the weight of the neighborhood structure of the local optimal solution and return to step S5.

[0062] S8. Based on the greedy algorithm, the global optimal solution is greedily destroyed and greedily reconstructed to obtain a new solution. The new solution is compared with the global optimal solution. If the global optimal solution is better, the improved Boltzmann function is used to determine whether to accept the new solution and return to step S4; otherwise, the new solution is assigned as the global optimal solution.

[0063] S9, Assign to ,judge Check if the condition is true; if true, return to step S4; otherwise, the algorithm execution ends.

[0064] The output module is used to output the global optimal solution obtained by the calculation module as the optimal coal mining equipment production plan.

[0065] Thirdly, the present invention also provides a computer-readable storage medium storing a computer program for a coal mining equipment production planning optimization method based on an improved variable neighborhood search algorithm, wherein the computer program causes a computer to execute a coal mining equipment production planning optimization method based on an improved variable neighborhood search algorithm as described above.

[0066] Fourthly, the present invention also provides an electronic device, comprising:

[0067] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing coal mining equipment production planning optimization methods based on improved variable neighborhood search algorithms as described above.

[0068] (III) Beneficial Effects

[0069] This invention provides a method for optimizing coal mining equipment production planning based on an improved variable neighborhood search algorithm. Compared with existing technologies, it has the following advantages:

[0070] (1) This invention optimizes the initial solution by designing a total cost function, thereby increasing the quality of the initial solution. At the same time, it designs a greedy algorithm to further optimize the optimal solution, which can avoid the variable neighborhood search algorithm from getting trapped in local optima early and obtain a better scheduling scheme.

[0071] (2) The present invention is an optimization method for formulating equipment production plans with the goal of minimizing production costs and penalty costs, which improves the production efficiency and service quality of enterprises.

[0072] (3) By combining multiple objectives such as historical production, order demand, after-sales demand, production cost, production cycle, production efficiency, product quality and delivery time, this invention can more comprehensively and accurately reflect the complexity of the actual production environment, provide decision-makers with more effective decision support, and thus improve the efficiency and effectiveness of equipment mass production. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a flowchart of a coal mining equipment production planning optimization method based on an improved variable neighborhood search algorithm, provided by an embodiment of the present invention. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] This application provides a coal mining equipment production planning optimization method based on a variable neighborhood algorithm, which solves the problem that traditional production planning cannot accurately respond to actual market demand and production conditions, and realizes the accurate formulation and optimization of production plans.

[0077] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0078] The mass production process of equipment often involves multiple interrelated factors with complex nonlinear relationships, making the optimization problem exceptionally complex. Furthermore, traditional production planning optimization is often based on fixed parameters and heavily reliant on human experience, which not only fails to guarantee accuracy but also makes it impossible to update in real time according to actual market demand and labor conditions.

[0079] This study addresses the technical problems existing in the current mass production process of equipment. On the one hand, we need to construct a real-time optimization plan by combining multi-source data such as historical production, order demand, and after-sales demand. On the other hand, we need to consider the production plan of complete sets of equipment and spare parts, especially with the optimization objective of minimizing production costs and penalty costs. In terms of research methods, the main factors affecting the performance of the variable neighborhood search algorithm are: the quality of the initial solution, the neighborhood structure, and the search strategy within the neighborhood structure. In some cases, the limitations of the initial solution and the neighborhood structure can cause the variable neighborhood search algorithm to get stuck in local optima early on, failing to obtain a good scheduling scheme. Therefore, this invention makes corresponding improvements to the variable neighborhood search algorithm to solve this problem.

[0080] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0081] like Figure 1 As shown, a method for optimizing coal mining equipment production planning based on an improved variable neighborhood search algorithm includes:

[0082] S1 initializes the various parameters in the algorithm, including production parameters and execution parameters. Production parameters include production information for complete sets of equipment and spare parts, customer information, delay penalty costs, production capacity constraints, order requirements, production costs, production efficiency, and delivery time. Execution parameters include the maximum number of iterations. Current iteration number Crossover probability Local search algorithm population size .

[0083] S2 prioritizes complete sets of equipment and spare parts according to the difference between processing time and expected delivery time; constructs a total cost function with the goal of minimizing production costs and delivery time penalty costs.

[0084] S3 generates an initial solution based on the priority sorting result, optimizes the initial solution with the goal of minimizing the total cost function, and sets the optimized initial solution as the global optimal solution.

[0085] S4. Generate and update the neighborhood solution set based on the global optimal solution to obtain the candidate solution set.

[0086] S5 uses roulette wheel probability selection to obtain the local search neighborhood structure.

[0087] S6. Obtain the neighborhood based on the candidate solution set and the local search neighborhood structure, and search for the local optimal solution.

[0088] S7. Based on the total cost function, determine the merits of the local optimal solution and the candidate solution set. If the local optimal solution is better, update the local optimal solution to the global optimal solution and increase the weight of the neighborhood structure that obtained the local optimal solution. If the candidate solution set is better, decrease the weight of the neighborhood structure that obtained the local optimal solution and return to step S5.

[0089] S8. Based on the greedy algorithm, the global optimal solution is greedily destroyed and greedily reconstructed to obtain a new solution. The new solution is compared with the global optimal solution. If the global optimal solution is better, the improved Boltzmann function is used to determine whether to accept the new solution and return to step S4; otherwise, the new solution is assigned as the global optimal solution.

[0090] S9, Assign to ,judge If the condition is met, return to step S4; otherwise, the algorithm ends and outputs the global optimal solution as the optimal coal mining equipment production plan.

[0091] In this embodiment, the initial solution is optimized by designing a total cost function, which increases the quality of the initial solution and avoids the variable neighborhood search algorithm from getting trapped in local optima early on. At the same time, the variable neighborhood search algorithm is used to optimize the coal mining equipment production planning method, which can obtain a better scheduling scheme.

[0092] It should be noted that each bit of the code in the solution of this application embodiment corresponds to a resource used in the production of a complete set of equipment or spare parts, and its position corresponds to its relative order in the production plan.

[0093] Step S1: Initialize the various parameters in the algorithm, including production parameters and execution parameters. Production parameters include complete equipment and spare parts production information, customer information, delay penalty costs, production capacity constraints, order requirements, production costs, production efficiency, and delivery time. Execution parameters include the maximum number of iterations. Current iteration number Crossover probability Local search algorithm population size .

[0094] Specifically, the production parameters include:

[0095] Complete Equipment Index ;

[0096] Spare parts index ;

[0097] Customer Index ;

[0098] Month Index ;

[0099] Complete set of equipment Production time;

[0100] Complete set of equipment Production completion time;

[0101] :spare parts Production time;

[0102] :spare parts Production completion time;

[0103] Manufacturer Monthly production complete set of equipment The cost;

[0104] Manufacturer Monthly production spare parts The cost;

[0105] :client The ordered complete set of equipment

[0106] :client The ordered spare parts collection

[0107] :client Ordered complete sets of equipment Quantity;

[0108] :client Ordered spare parts Quantity;

[0109] :client Ordered complete sets of equipment The unit delay penalty cost;

[0110] :client Ordered complete sets of equipment Expected delivery time;

[0111] :client Ordered spare parts The unit delay penalty cost;

[0112] :client Ordered spare parts Expected delivery time;

[0113] If complete sets of equipment Allocated to months If production is initiated, the value is 1; otherwise, it is 0.

[0114] If spare parts Allocated to months If production is initiated, the value is 1; otherwise, it is 0.

[0115] If the customer Ordered complete set of equipment If the result is positive, then it equals 1; otherwise, it equals 0.

[0116] If the customer Spare parts ordered If the result is positive, then it equals 1; otherwise, it equals 0.

[0117] : No. Monthly production equipment and spare parts quantity;

[0118] : No. Maximum monthly production capacity;

[0119] Constraints: ;

[0120] This embodiment, by considering various aspects of production parameters, can more comprehensively and accurately reflect the complexity of the actual production environment, providing decision-makers with more effective decision support, thereby improving the efficiency and effectiveness of equipment mass production.

[0121] Step S2: Prioritize the complete sets of equipment and spare parts according to the difference between processing time and expected delivery time. Construct a total cost function with the objectives of minimizing production costs and minimizing delivery time penalty costs.

[0122] First, calculate the difference in expected delivery time between the complete set of equipment and the spare parts set:

[0123] All complete sets of equipment produced by the manufacturer and spare parts collection The difference between processing time and its corresponding expected delivery time for:

[0124]

[0125] Assembly of complete sets of equipment and spare parts according to Arrange in non-increasing order to form a priority order. ,in Indicates spare parts, Indicates a complete set of equipment; if there are identical... The priority is then determined randomly.

[0126] The total cost function is constructed with the objectives of minimizing production costs and minimizing delivery time penalty costs.

[0127] The total cost function is:

[0128]

[0129] In the formula, Indicates the total cost; This indicates minimizing production costs; This indicates minimizing the cost of delivery time penalties; Indicates a complete set of equipment index; Indicates spare parts index; Indicates the customer index; Indicates the month index; Indicates complete set of equipment Production time; Indicates complete set of equipment Production completion time; Indicates spare parts Production time; Indicates spare parts Production completion time; Indicates manufacturer Monthly production complete set of equipment The cost; Indicates manufacturer Monthly production spare parts The cost; Indicates customer The complete set of equipment ordered; Indicates customer The collection of ordered spare parts; Indicates customer Ordered complete sets of equipment Quantity; Indicates customer Ordered spare parts Quantity; Indicates customer Ordered complete sets of equipment The unit delay penalty cost; Indicates customer Ordered complete sets of equipment Expected delivery time; Indicates customer Ordered spare parts The unit delay penalty cost; Indicates customer Ordered spare parts Expected delivery time; Indicates if complete set of equipment Allocated to months If production is initiated, the value is 1; otherwise, it is 0. Indicates if spare parts Allocated to months If production is initiated, the value is 1; otherwise, it is 0. Indicates if the customer Ordered complete set of equipment If the result is positive, then it equals 1; otherwise, it equals 0. If the customer Spare parts ordered If the result is positive, then it equals 1; otherwise, it equals 0.

[0130] Step S3: Generate an initial solution based on the priority sorting result, optimize the initial solution with the aim of minimizing the total cost function, and set the optimized initial solution as the global optimum. In specific implementation, this step further includes the following steps:

[0131] Step S31: In the sorted production sequence, insert the new production task into the production sequence with the goal of minimizing the total cost function.

[0132] In order of priority Distribution of complete sets of equipment and spare parts Establish a complete equipment and spare parts allocation list for each production machine of the manufacturer. .

[0133]

[0134] in, The representative will provide complete sets of equipment Allocated to months To carry out production, The representative will provide spare parts Allocated to months Production is carried out, and complete sets of equipment are put into use. and spare parts When assigning to a month, the current part of the sorted list will be inserted. Of all candidate positions, choose the one that maximizes the total cost. The smallest position.

[0135] This is the initial solution. The initial solution is then optimized with the goal of minimizing the total cost function. Specifically, when allocating complete sets of equipment to months, the position that minimizes the difference between the expected delivery time and the total cost is selected from all candidate positions in the current partial permutation list. This initial solution is optimized through multiple insertions.

[0136] Set the optimized initial solution as the global optimum, i.e. .

[0137] Step S4: Generate and update the neighborhood solution set based on the global optimal solution to obtain the candidate solution set. The specific implementation of this step includes the following steps:

[0138] S41, generate a neighborhood solution set based on the global optimal solution, denoted as . ;in, The CCP is considering the neighborhood solution. Individual, The first solution in the neighborhood is represented by the second solution. There are individuals, and each individual is determined by the initial solution. Random exchange This was the first time it was obtained;

[0139] S42, Define variables The variable and Having the same meaning, and putting Individual values ​​assigned to ;

[0140] S43, Define variables and Let the variable Let the variable , will variables Assigned to the interval Random numbers generated within a range; where, and With individuals Same dimensions;

[0141] S44, using the formula renew ,in Indicates the th in the global optimal solution One element, , In the interval respectively Random numbers generated within a range;

[0142] S45, in the interval Generate random numbers within the range and judge Is it true? If it is true, then... Assign to Otherwise Assign to ;

[0143] S46, Order ,judge Check if the condition is met. If it is met, return to step S44; otherwise, proceed to step S47.

[0144] S47, calculate the individual costs according to the total cost function. and intermediates fitness value and and compare and ,like Then put Assigning values ​​to individuals ;

[0145] S48, order ,judge Check if the condition is met. If it is met, return to step S43; otherwise, proceed to step S49.

[0146] S49: Select different individuals with better quality from the neighborhood solution set and assign them to the candidate solution set.

[0147] Step S5: Obtain the local search neighborhood structure based on roulette wheel probability selection. The specific implementation of this step includes the following steps:

[0148] S51, pre-set that the initial weights of each local search neighborhood structure are equal.

[0149] In the initial parameter setting stage of the algorithm, the local search neighborhood structure set of the variable neighborhood search algorithm is... The initial weights corresponding to each neighborhood structure are: Initially, given the unknown effectiveness of the local search neighborhood structure, the initial neighborhood structure weights are assumed to be equal.

[0150] S52, calculate the cumulative probability of each local search neighborhood structure based on the weight of each local search neighborhood structure and the roulette wheel probability.

[0151] The weights of each local search neighborhood structure are combined with the probability formula of roulette. The cumulative probability of each neighborhood structure can be calculated. This is the sum of the selection probabilities of all individuals preceding each individual. In the formula, Representing neighborhood structure The cumulative probability; Representing neighborhood structure The corresponding initial weights; This represents the sum of the weights of all neighborhood structures.

[0152] S53, select the neighborhood structure based on the cumulative probability to perform a local search.

[0153] Randomly generated ,if Then choose neighborhood structure Perform a local search.

[0154] The local search neighborhood structure in this embodiment includes ~ Specifically:

[0155] Domain Structure Define a variable x, randomly generate an integer in the range [1, P] and assign it to variable x. Reverse the order of all codes to the left of x in the solution set of the neighborhood. Then select resources for all positions based on probability. Make a random selection.

[0156] Neighborhood structure Define a variable x, randomly generate an integer in the range [1, Q] and assign it to variable x. Reverse the encoding of all positions to the right of x in the neighborhood solution set. Then select resources for all positions based on probability. Make a random selection.

[0157] Neighborhood structure Define variables x and y, generate two integers in the range [1, P] and [P+1, P+Q] respectively, and assign them to variables x and y. The x-th position in the neighborhood solution set corresponds to the allocation month code of the complete set of equipment, and the y-th position corresponds to the allocation month code of the spare parts. Swap the month numbers corresponding to the x-th and y-th positions in the neighborhood solution set.

[0158] Neighborhood structure : Define variables x, y, P + Q = n. Generate two integers within the ranges [1, P] and [P + 1, P + Q] respectively, and assign them to variables x and y. Among them, the x-th position in the neighborhood solution set corresponds to the allocation month code of the complete set of equipment, and the y-th position corresponds to the allocation month code of the spare parts. Reverse the codes of all positions to the left of position x and all positions to the right of position y. Then, randomly select resources for all positions with a probability of (x + y) / 2n;

[0159] Neighborhood structure : Define variables x, y, P + Q = n. Without replacement, randomly generate two integers within the range [1, n], and assign them to variables x and y, where x ≤ y. Exchange the codes on both sides of positions x and y in the neighborhood solution set. Then, randomly select resources for all positions with a probability of (x + y) / 2n.

[0160] Neighborhood structure : Define variables x, y, z, P + Q = n. Without replacement, randomly obtain three integers within the range [1, n], and assign them to variables x, y, z, where x < y < z. Reverse the codes on both sides of positions x and z in the neighborhood solution set, and exchange the codes inside positions x, y and y, z. Then, randomly select resources for all positions with a probability of (x + y + z) / 3n.

[0161] Neighborhood structure : Define variables x, y, z, P + Q = n. Without replacement, randomly obtain three integers within the range [1, n], and assign them to variables x, y, z, where x < y < z. Exchange the codes on both sides of positions x and z in the neighborhood solution set, and reverse the codes inside positions x, y and y, z. Then, randomly select resources for all positions with a probability of (x + y + z) / 3n.

[0162] Neighborhood structure : Define variables x, y, z, P + Q = n. Without replacement, randomly obtain three integers within the range [1, n], and assign them to variables x, y, z, where x < y < z. Reverse the codes on both sides of positions x and z in the neighborhood solution set, as well as the codes inside positions x, y and y, z. Then, randomly select resources for all positions with a probability of (x + y + z) / 3n.

[0163] Neighborhood structure : Define variables , , without replacement, randomly obtain four integers within the range [1, n], and assign them to variables, , where, , for the positions in the neighborhood solution set and Reverse the encoding within the position, and The encoding is exchanged within the locations between them, and then resources are selected probabilistically for all locations. Make a random selection.

[0164] Step S6: Obtain the neighborhood based on the candidate solution set and the local search neighborhood structure, and search for the local optimal solution.

[0165] Step S7: Determine the relative merits of the local optimal solution and the candidate solution set based on the total cost function. If the local optimal solution is superior, update it to the global optimal solution and increase the weight of the neighborhood structure that obtained the local optimal solution. If the candidate solution set is superior, decrease the weight of the neighborhood structure that obtained the local optimal solution and return to step S5.

[0166] Step S8: Based on a greedy algorithm, the global optimal solution is greedily destroyed and greedily reconstructed to obtain a new solution. The new solution is compared with the global optimal solution. If the global optimal solution is better, the improved Boltzmann function is used to determine whether to accept the new solution, and the process returns to step S4; otherwise, the new solution is assigned as the global optimal solution. The specific implementation steps of this embodiment also include the following steps:

[0167] S81, based on the production plan for all months corresponding to the global optimal solution. epsilon-greedy The rules select several months.

[0168] Specifically, in terms of probability Select the month with the highest total production cost (TC) in the current production stage; then, based on probability... Use a roulette wheel selection method to choose other months; if the maximum total production cost is the same for multiple months, then randomly select one of them.

[0169] S82, randomly select a complete set of equipment or spare parts from the month, and record the selected product set as S82. The remaining product set is denoted as .

[0170] Specifically, for each selected production month, one complete set of equipment or spare part is randomly selected as the product to be removed. The set of products extracted from the production month is denoted as […]. The remaining portion after removing these products is denoted as... .

[0171] S83, The products in the collection are inserted one by one. The set is used to calculate the total production cost based on the total cost function, taking into account all possible positions other than the initial position.

[0172] Specifically, products previously removed from the production month ( Insert one by one into At all possible locations (excluding their initial locations). For each insertion location, calculate the maximum total production cost (TC) of the system after insertion; when calculating the maximum total production cost (TC), products that are not inserted are ignored. The remaining part (of the text).

[0173] S84 sets the insertion position with the minimum total production cost as the new solution.

[0174] Specifically, compare all possible insertion positions and select the insertion position that minimizes the total production cost (TC) as the new solution. ).

[0175] In this embodiment, firstly, according to steps S81 to S84, the current global optimal solution ( Greedy insertion and greedy reconstruction are used to obtain a new solution. Then, the total generation cost of the global optimal solution is calculated based on the total cost function. ( ) and the new solution to total production cost ( Comparing the advantages and disadvantages of the two, if That is, when the global optimal solution is relatively good, generate ,if If so, accept the worse solution and set... = The new solution is then assigned as the global optimum. The improved Boltzmann function is: This function aims to facilitate escape from local optima during the search process; where, , indicating the solution and The relative percentage difference between the total production cost (TC). Then return to step S4.

[0176] like If the new solution is better at this point, then the new solution is assigned to the globally optimal solution. = Then proceed to the next step.

[0177] The coal mining equipment production plan optimization method in this embodiment breaks down and reconstructs the current global optimal solution to obtain a new solution. It compares the merits of the current solution and the new solution, accepts the better new solution or selectively accepts the worse new solution, and then returns to step S4 to iterate again. This avoids possible local optima and further optimizes the algorithm's ability to obtain the optimal solution.

[0178] Step S9, will Assign to ,judge If the condition is met, return to step S4; otherwise, the algorithm ends and outputs the global optimal solution as the optimal coal mining equipment production plan.

[0179] Thus, the entire process of this embodiment of the invention is completed.

[0180] This invention also provides a coal mining equipment production planning optimization system based on an improved variable neighborhood search algorithm, specifically including:

[0181] The calculation module is used to perform the following steps:

[0182] S1 initializes the various parameters in the algorithm, including production parameters and execution parameters. Production parameters include production information for complete sets of equipment and spare parts, customer information, delay penalty costs, production capacity constraints, order requirements, production costs, production efficiency, and delivery time. Execution parameters include the maximum number of iterations. Current iteration number Crossover probability Local search algorithm population size .

[0183] S2 prioritizes complete sets of equipment and spare parts according to the difference between processing time and expected delivery time; constructs a total cost function with the goal of minimizing production costs and delivery time penalty costs.

[0184] S3 generates an initial solution based on priority order, optimizes the initial solution with the goal of minimizing the total cost function, and sets the optimized initial solution as the optimal solution.

[0185] S4. Generate and update the neighborhood solution set based on the global optimal solution to obtain the candidate solution set.

[0186] S5 uses roulette wheel probability selection to obtain the local search neighborhood structure.

[0187] S6. Obtain the neighborhood based on the candidate solution set and the local search neighborhood structure, and search for the local optimal solution.

[0188] S7. Based on the total cost function, determine the merits of the local optimal solution and the candidate solution set. If the local optimal solution is better, update the local optimal solution to the global optimal solution and increase the weight of the neighborhood structure that obtained the local optimal solution. If the candidate solution set is better, decrease the weight of the neighborhood structure that obtained the local optimal solution and return to step S5.

[0189] S8: Based on the greedy algorithm, the global optimal solution is greedily destroyed and greedily reconstructed to obtain a new solution. The new solution is compared with the global optimal solution. If the global optimal solution is better, the improved Boltzmann function is used to determine whether to accept the new solution and return to step S4; otherwise, the new solution is assigned as the global optimal solution.

[0190] S9, Assign to ,judge If the condition is met, return to step S4; otherwise, the algorithm ends and outputs the global optimal solution as the optimal coal mining equipment production plan.

[0191] The output module is used to output the global optimal solution obtained by the calculation module as the optimal coal mining equipment production plan.

[0192] It is understood that the coal mining equipment production planning optimization system based on the improved variable neighborhood search algorithm provided in this embodiment of the invention corresponds to the coal mining equipment production planning optimization method based on the improved variable neighborhood search algorithm described above. The explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding content in the coal mining equipment production planning optimization method based on the improved variable neighborhood search algorithm, and will not be repeated here.

[0193] This invention also provides a computer-readable storage medium storing a computer program for optimizing a coal mining equipment production plan based on an improved variable neighborhood search algorithm, wherein the computer program causes a computer to execute the coal mining equipment production plan optimization method based on the improved variable neighborhood search algorithm as described above.

[0194] This application also provides an electronic device, including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing the coal mining equipment production planning optimization method based on the improved variable neighborhood search algorithm as described above.

[0195] In summary, compared with existing technologies, it has the following beneficial effects:

[0196] 1. This invention optimizes the initial solution by designing a total cost function, thereby increasing the quality of the initial solution; it optimizes the search strategy of the neighborhood search by using a self-designed neighborhood structure; and it further optimizes the optimal solution by designing a greedy algorithm, which can prevent the variable neighborhood search algorithm from getting trapped in local optima early on. This allows for finding an approximate optimal solution for the coal mining equipment production planning optimization problem, enabling enterprises to make full use of their production resources to the maximum extent, reduce production costs, and improve enterprise service levels and customer satisfaction.

[0197] 2. The present invention provides an optimization method for formulating equipment production plans with the goal of minimizing production costs and penalty costs, thereby improving the production efficiency and service quality of enterprises.

[0198] 3. By combining multiple objectives such as historical production, order demand, after-sales demand, production cost, production cycle, production efficiency, product quality and delivery time, the embodiments of the present invention can more comprehensively and accurately reflect the complexity of the actual production environment, provide decision-makers with more effective decision support, and thus improve the efficiency and effectiveness of equipment mass production.

[0199] 4. In this embodiment of the invention, the months are first encoded, and the workpieces are assigned to each month by priority sorting to obtain the corresponding production plan. Then, the improved variable neighborhood search algorithm is used to continuously optimize the production plan. This method performs well in terms of speed and quality and can greatly improve the production efficiency of enterprises.

[0200] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0201] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing coal mining equipment production planning based on an improved variable neighborhood search algorithm, characterized in that, include: S1 initializes the various parameters in the algorithm, including production parameters and execution parameters; the production parameters include complete equipment and spare parts production information, customer information, delay penalty costs, production capacity constraints, order requirements, production costs, production efficiency, and delivery time; the execution parameters include the maximum number of iterations. Current iteration number Crossover probability Local search algorithm population size ; S2 prioritizes complete sets of equipment and spare parts according to the difference between processing time and expected delivery time; Construct a total cost function with the objectives of minimizing production costs and minimizing delivery time penalty costs; S3, Encode the result of the priority sorting and generate an initial solution, and set the initial solution as the global optimal solution; S4. Generate and update the neighborhood solution set based on the global optimal solution to obtain the candidate solution set; S5, based on the probability selection of roulette wheel selection to obtain the local search neighborhood structure; S6. Obtain the neighborhood and search it according to the candidate solution set and the local search neighborhood structure to obtain the local optimal solution; S7. Based on the total cost function, determine the merits of the local optimal solution and the candidate solution set. If the local optimal solution is superior, update the local optimal solution to the global optimal solution and increase the weight of the neighborhood structure that obtained the local optimal solution. If the candidate solution set is superior, decrease the weight of the neighborhood structure that obtained the local optimal solution and return to step S5. S8. Based on the greedy algorithm, the global optimal solution is greedily destroyed and greedily reconstructed to obtain a new solution. The new solution is compared with the global optimal solution. If the global optimal solution is better, the improved Boltzmann function is used to determine whether to accept the new solution and return to step S4; otherwise, the new solution is assigned as the global optimal solution. S9, Assign to ,judge Check if the condition is met; if it is met, return to step S4. Otherwise, the globally optimal solution will be output as the optimal coal mining equipment production plan. The roulette probability selection includes: The initial weights of each local search neighborhood structure are assumed to be equal. The cumulative probability of each local search neighborhood structure is calculated based on the weight of each local search neighborhood structure and the roulette wheel probability. Local search is performed by selecting a neighborhood structure based on the cumulative probability. The method of obtaining a new solution by greedily destroying and reconstructing the global optimal solution based on a greedy algorithm includes: Based on all months of the production plan corresponding to the global optimal solution epsilon-greedy The rule selects several months; Randomly select a complete set of equipment or spare parts from the month mentioned above, and record the selected set of products as follows: Let the remaining product set be denoted as ; The The products in the collection are inserted one by one into the... The set is used to calculate the total production cost based on the total cost function at all possible positions other than the initial position. Set the insertion position with the lowest total production cost as the new solution.

2. The method for optimizing coal mining equipment production planning based on an improved variable neighborhood search algorithm according to claim 1, characterized in that, The local search neighborhood structure includes ~ ,in, Indicates the number of indexes for the device. Indicates the number of spare parts indexed; The To define a variable x, a random integer within the interval [1, P] is generated and assigned to variable x. The codes of all positions to the left of x in the neighborhood solution set are then reversed. Finally, resource selection for all positions is performed based on probability. Make a random selection; The To define a variable x, a random integer within the interval [1, Q] is generated and assigned to variable x. The codes to the right of x in the neighborhood solution set are then reversed. Finally, resource selection for all positions is performed based on probability. Make a random selection; The To define variables x and y, generate two integers in the range [1, P] and [P+1, P+Q] respectively, and assign them to variables x and y. The x-th position in the neighborhood solution set corresponds to the allocation month code of the complete set of equipment, and the y-th position corresponds to the allocation month code of the spare parts. Swap the month numbers corresponding to the x-th and y-th positions in the neighborhood solution set. The To define variables x, y, and P+Q=n, generate two integers in the range [1, P] and [P+1, P+Q] respectively, and assign them to variables x and y. The x-th position in the neighborhood solution set corresponds to the allocation month code of the complete set of equipment, and the y-th position corresponds to the allocation month code of the spare parts. Reverse the order of all codes to the left of position x and all codes to the right of position y. Then, randomly select resources for all positions with probability (x+y) / 2n. The To define variables x and y, P+Q=n, without replacement, two integers in the range [1, n] are randomly generated and assigned to variables x and y, where x≤y. The codes located on both sides of position x and y in the neighborhood solution set are swapped. Then, the resource selection for all positions is randomly selected with probability (x+y) / 2n. The is to define variables x, y, z, P + Q = n, without replacement, randomly obtain three integers within the range of [1, n], assign them to variables x, y, z, where x < y < z, reverse the code on both sides of the positions of x and z in the neighborhood solution set, exchange the code inside the positions of x, y and y, z, and then randomly select resources at all positions with a probability of (x + y + z) / 3n; The is to define variables x, y, z, P + Q = n, without replacement, randomly obtain three integers within the range of [1, n], assign them to variables x, y, z, where x < y < z, swap the codes on both sides of the x and z positions in the neighborhood solution set, reverse the codes inside the x, y and y, z positions, and then randomly select resources at all positions with a probability of (x + y + z) / 3n; The is to define variables x, y, z, P + Q = n, without replacement, randomly obtain three integers within the range of [1, n], assign them to variables x, y, z, where x < y < z, reverse the code on both sides of the positions of x and z in the neighborhood solution set and the code inside the positions of x, y and y, z, and then randomly select the resources at all positions with a probability of (x + y + z) / 3n; The To define variables , Without replacement, randomly select four integers within the interval [1, n] and assign them to variables. ,in, The solution set located in the neighborhood and Reverse the encoding within the position, and The encoding is exchanged within the locations between them, and then resources are selected probabilistically for all locations. Make a random selection.

3. The method for optimizing coal mining equipment production planning based on an improved variable neighborhood search algorithm according to claim 1, characterized in that, The process of obtaining the candidate solution set specifically includes: S41, a neighborhood solution set is generated based on the initial solution, and the solution set is denoted as... ;in, The CCP is considering the neighborhood solution. Individual, The first solution in the neighborhood is represented by the second solution. There are individuals, and each individual is determined by the initial solution. Random swap This was the first time it was obtained; S42, Define variables The variable and Having the same meaning, and putting Individual values ​​assigned to ; S43, Define variables and Let the variable Let the variable , will variables Assigned to the interval Random numbers generated within a range; where, and With individuals Same dimensions; S44, using the formula renew ,in Indicates the th in the global optimal solution One element, , In the interval respectively Random numbers generated within a range; S45, in the interval Generate random numbers within the range and judge Is it true? If it is true, then... Assign to Otherwise Assign to ; S46, let ,judge Check if the condition is met. If it is met, return to step S44; otherwise, proceed to step S47. S47, calculate the individual costs according to the total cost function. and intermediates fitness value and and compare and ,like Then put Assigning values ​​to individuals ; S48, order ,judge Check if the condition is met. If it is met, return to step S43; otherwise, proceed to step S49. S49. Based on the total cost function, select the better-quality individuals in the neighborhood solution set and assign them to the candidate solution set.

4. The method for optimizing coal mining equipment production planning based on an improved variable neighborhood search algorithm according to claim 1, characterized in that, The total cost function is: In the formula, Indicates the total cost; This indicates minimizing production costs; This indicates minimizing the cost of delivery time penalties; Indicates a complete set of equipment index; Indicates spare parts index; Indicates the customer index; Indicates the month index; Indicates complete set of equipment Production time; Indicates complete set of equipment Production completion time; Indicates spare parts Production time; Indicates spare parts Production completion time; Indicates manufacturer Monthly production complete set of equipment The cost; Indicates manufacturer Monthly production spare parts The cost; Indicates customer The complete set of equipment ordered; Indicates customer The collection of ordered spare parts; Indicates customer Ordered complete sets of equipment Quantity; Indicates customer Ordered spare parts Quantity; Indicates customer Ordered complete sets of equipment The unit delay penalty cost; Indicates customer Ordered complete sets of equipment Expected delivery time; Indicates customer Ordered spare parts The unit delay penalty cost; Indicates customer Ordered spare parts Expected delivery time; Indicates if complete set of equipment Allocated to months If production is initiated, the value is 1; otherwise, it is 0. Indicates if spare parts Allocated to months If production is initiated, the value is 1; otherwise, it is 0. Indicates if the customer Ordered complete set of equipment If the result is positive, then it equals 1; otherwise, it equals 0. If the customer Spare parts ordered If the result is positive, then it equals 1; otherwise, it equals 0.

5. The method for optimizing coal mining equipment production planning based on an improved variable neighborhood search algorithm according to claim 1, characterized in that, Also includes: In the sorted production sequence, new production tasks are inserted into the production sequence of the initial solution with the objective of minimizing the total cost function.

6. A coal mining equipment production planning optimization system based on an improved variable neighborhood search algorithm, applied to the coal mining equipment production planning optimization method as described in claim 1, characterized in that, include: The calculation module is used to perform the following steps: S1 initializes the various parameters in the algorithm, including production parameters and execution parameters; the production parameters include complete equipment and spare parts production information, customer information, delay penalty costs, production capacity constraints, order requirements, production costs, production efficiency, and delivery time; the execution parameters include the maximum number of iterations. Current iteration number Crossover probability Local search algorithm population size ; S2 prioritizes complete sets of equipment and spare parts according to the difference between processing time and expected delivery time; constructs a total cost function with the goal of minimizing production cost and delivery time penalty cost; S3, generate an initial solution according to the priority sorting, optimize the initial solution with the aim of minimizing the total cost function, and set the optimized initial solution as the optimal solution; S4. Generate and update the neighborhood solution set based on the initial solution to obtain the candidate solution set; S5, based on the probability selection of roulette wheel selection to obtain the local search neighborhood structure; S6. Obtain the neighborhood and search it according to the candidate solution set and the local search neighborhood structure to obtain the local optimal solution; S7. Based on the total cost function, determine the merits of the local optimal solution and the candidate solution set. If the local optimal solution is superior, update the local optimal solution to the global optimal solution and increase the weight of the neighborhood structure that obtained the local optimal solution. If the candidate solution set is superior, decrease the weight of the neighborhood structure of the local optimal solution and return to step S5. S8. Based on the greedy algorithm, the global optimal solution is greedily destroyed and greedily reconstructed to obtain a new solution. The new solution is compared with the global optimal solution. If the global optimal solution is better, the improved Boltzmann function is used to determine whether to accept the new solution and return to step S4; otherwise, the new solution is assigned as the global optimal solution. S9, Assign to ,judge Check if the condition is true; if true, return to step S4; otherwise, the algorithm execution ends. The output module is used to output the global optimal solution obtained by the calculation module as the optimal coal mining equipment production plan.

7. A computer-readable storage medium, characterized in that, It stores a computer program for optimizing the production plan of coal mining equipment based on an improved variable neighborhood search algorithm, wherein the computer program causes a computer to execute the optimization method for coal mining equipment based on an improved variable neighborhood search algorithm as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing the coal mining equipment production planning optimization method based on an improved variable neighborhood search algorithm as described in any one of claims 1 to 5.

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