Double-layer planning optimization method and system for part production and product assembly

Abstract

The invention provides a bilevel planning optimization method and system for part production and product assembly, and relates to the technical field of supply chains, the bilevel planning optimization method comprises the following steps: setting input parameters based on a neighborhood search algorithm, and setting execution parameters of the algorithm; obtaining an initial population formed by a plurality of initial solutions through coding; processing the initial population based on oscillation operation of a variable neighborhood search algorithm, obtaining an individual population and an optimal individual in the individual population through a neighborhood structure, and updating the initial solution through the optimal individual to obtain a new solution; and searching an optimal solution in the neighborhood, comparing the optimal solution with the new solution, determining a globally optimal solution, and outputting the current globally optimal solution when the current number of times reaches the maximum number of iterations. According to the method, collaborative scheduling optimization can be powerfully carried out on supplier production and manufacturer assembly, so that the production and assembly efficiency of suppliers and manufacturers is maximized, and the total time of supplier production and manufacturer assembly is reduced.

Description

Technical Field

[0001] The present application relates to the field of supply chain technology, and in particular to a two-level planning optimization method and system for parts production and product assembly. Background Art

[0002] The production and assembly of high-end equipment usually involves complex and diverse parts. How to allocate parts to suppliers for production determines the cost of the manufacturer's assembly process. The specific process involves the manufacturer allocating parts orders to suppliers for production, and the suppliers delivering the finished products to the manufacturer for assembly.

[0003] The production and assembly of high-end equipment can be defined as a two-level planning problem. The upper level problem is that the manufacturer allocates the parts corresponding to all products to suppliers for production. After the suppliers produce the products, they deliver them to the manufacturer. The supplier's parts decision determines the manufacturer's assembly cost. The lower level problem is that the supplier takes into account its own production capacity and the delivery time of the parts, and allocates the production lines and sorts the production of the allocated parts to minimize its production cost.

[0004] Through the integrated scheduling of parts production and product assembly, efficient coordination of production and assembly can be achieved to maximize resource utilization, reduce costs, and shorten delivery cycles. However, there are relatively few studies on the two-stage bi-level planning optimization problem of high-end equipment parts production and product assembly, and the collaborative efficiency of supply chain integrated scheduling still needs to be optimized and improved. Summary of the invention

[0005] In response to the deficiencies in the prior art, the present application provides a two-level planning optimization method and system for parts production and product assembly, which solves the problem of insufficient collaborative efficiency faced by current equipment production during integrated scheduling.

[0006] To achieve the above objectives, this application is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a two-level planning optimization method for parts production and product assembly, the two-level planning optimization method comprising the following steps:

[0008] S1. Set input parameters based on the neighborhood search algorithm according to the obtained manufacturer's parts order allocation, production line selection in the assembly process, and parts production line allocation and production sequencing in the supplier's production process;

[0009] S2. Setting execution parameters of the algorithm, including: initial weight of the local search neighborhood structure of the variable neighborhood search algorithm, maximum number of iterations, and number of individuals in the population;

[0010] S3, encoding according to the input parameters and the variable neighborhood search algorithm to obtain an initial population X0 consisting of multiple initial solutions;

[0011] S4, based on the oscillation operation of the variable neighborhood search algorithm to process the initial population X0, obtain the individual population and the best individual in the individual population through the neighborhood structure, update the initial solution through the best individual, and obtain the new solution X′;

[0012] S5. Based on the new solution X′ and the obtained local search neighborhood structure, search for the optimal solution X″ in the neighborhood and compare it with the new solution X′ to determine the global optimal solution. If the current number of iterations reaches the maximum number, output the current global optimal solution.

[0013] In a second aspect, an embodiment of the present application provides a two-level planning optimization system for parts production and product assembly, the two-level planning optimization system comprising: an input parameter setting module, an execution parameter setting module, an encoding module, an oscillation processing module, and an output module;

[0014] The input parameter setting module is used to set the input parameters based on the neighborhood search algorithm according to the obtained manufacturer's parts order allocation, production line selection in the assembly process, and the parts production line allocation and production sequencing in the supplier's production process;

[0015] The execution parameter setting module is used to set the execution parameters of the algorithm, including: the initial weight of the local search neighborhood structure of the variable neighborhood search algorithm, the maximum number of iterations, and the number of individuals in the population;

[0016] The encoding module is used to encode according to the input parameters and the variable neighborhood search algorithm to obtain an initial population X0 composed of multiple initial solutions;

[0017] The oscillation processing module is used to process the initial population X0 based on the oscillation operation of the variable neighborhood search algorithm, obtain the individual population and the optimal individual in the individual population through the neighborhood structure, update the initial solution through the optimal individual, and obtain the new solution X′;

[0018] The output module is used to search for the optimal solution X″ in the neighborhood based on the new solution X′ and the obtained local search neighborhood structure, compare it with the new solution X′, determine the global optimal solution, and output the current global optimal solution when the current number of iterations reaches the maximum number.

[0019] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the two-level planning optimization method for component production and product assembly in the first aspect mentioned above is implemented.

[0020] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a program or instruction, which, when executed by a processor, implements the two-level planning optimization method for component production and product assembly in the aforementioned first aspect.

[0021] This application provides a two-level planning optimization method and system for parts production and product assembly. Compared with the prior art, it has the following beneficial effects:

[0022] This application recognizes the constraints of the supplier's production capacity and the manufacturer's assembly capacity, considers the two-stage double-level planning optimization problem of equipment component production and product assembly, optimizes the double-level planning problem of equipment component production and product assembly, comprehensively considers the supplier's component allocation, production sequence and production machine selection, and manufacturer's assembly machine selection, solves the problem through a variable neighborhood search algorithm and obtains a global optimal solution, and can effectively optimize the coordinated scheduling of supplier production and manufacturer assembly, so as to maximize the production and assembly efficiency of suppliers and manufacturers, reduce the total time of supplier production and manufacturer assembly, thereby ensuring that the parts produced by suppliers can be supplied to manufacturers on time, effectively solving the problem of supplier production and manufacturer assembly scheduling, and optimizing the overall efficiency of high-end equipment production and assembly. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 It is a flowchart of a two-level planning optimization method for parts production and product assembly provided in an embodiment of the present application;

[0025] Figure 2 is an exemplary distribution diagram of PN sequences and MU sequences provided in an embodiment of the present application;

[0026] Figure 3 It is a structural diagram of a two-layer planning optimization system for parts production and product assembly provided in an embodiment of the present application;

[0027] Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0030] The embodiments of the present application solve the problem of insufficient collaborative efficiency faced by current equipment production during integrated scheduling by providing a two-level planning optimization method and system for parts production and product assembly.

[0031] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0032] The production and assembly of high-end equipment usually involves complex and diverse parts. How to allocate parts to suppliers for production determines the cost of the manufacturer's assembly process. The specific process involves the manufacturer allocating parts orders to suppliers for production, and the suppliers delivering the finished products to the manufacturer for assembly. This type of problem can be defined as a two-level planning problem. The upper level problem is that the manufacturer allocates parts corresponding to all products to suppliers for production. After the suppliers produce, they deliver the products to the manufacturer. If the delivery time is too long, the supplier will incur penalty costs; the supplier's parts decision determines the manufacturer's assembly cost. The lower level problem is that the supplier takes into account its own production capacity and the delivery time of parts, and allocates production lines and sorts the production of the allocated parts to minimize its production costs.

[0033] Through the integrated scheduling of parts production and product assembly, efficient coordination of production and assembly can be achieved to maximize resource utilization, reduce costs, and shorten delivery cycles. However, there are relatively few studies on the two-stage bi-level planning optimization problem of high-end equipment parts production and product assembly, and the collaborative efficiency of supply chain integrated scheduling still needs to be optimized and improved.

[0034] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0035] The following first introduces a two-level planning optimization method for parts production and product assembly provided in an embodiment of the present application.

[0036] The present application embodiment provides a flowchart of a two-level planning optimization method for parts production and product assembly, such as Figure 1 As shown, the two-level planning optimization method may include the following steps S1-S5.

[0037] S1. Set input parameters based on the neighborhood search algorithm according to the obtained manufacturer's parts order allocation, production line selection in the assembly process, and parts production line allocation and production sequencing in the supplier's production process;

[0038] S2. Setting execution parameters of the algorithm, including: initial weight of the local search neighborhood structure of the variable neighborhood search algorithm, maximum number of iterations, and number of individuals in the population;

[0039] S3, encoding according to the input parameters and the variable neighborhood search algorithm to obtain an initial population X0 consisting of multiple initial solutions;

[0040] S4, based on the oscillation operation of the variable neighborhood search algorithm to process the initial population X0, obtain the individual population and the best individual in the individual population through the neighborhood structure, update the initial solution through the best individual, and obtain the new solution X′;

[0041] S5. Based on the new solution X′ and the obtained local search neighborhood structure, search for the optimal solution X″ in the neighborhood and compare it with the new solution X′ to determine the global optimal solution. If the current number of iterations reaches the maximum number, output the current global optimal solution.

[0042] The above is a specific implementation method of a two-level planning optimization method and system for parts production and product assembly provided in an embodiment of the present application. The present application recognizes the constraints of the supplier's production capacity and the manufacturer's assembly capacity, considers the two-stage two-level planning optimization problem of equipment parts production and product assembly, optimizes the two-level planning problem of equipment parts production and product assembly, and comprehensively considers the supplier's parts allocation, production sequence and production machine selection, and the manufacturer's assembly machine selection.

[0043] Furthermore, the present application solves the problem through a variable neighborhood search algorithm and obtains a global optimal solution, which can effectively optimize the coordinated scheduling of supplier production and manufacturer assembly, maximize the production and assembly efficiency of suppliers and manufacturers, and reduce the total time of supplier production and manufacturer assembly, thereby ensuring that the parts produced by suppliers can be supplied to manufacturers on time, effectively solving the problem of supplier production and manufacturer assembly scheduling, and optimizing the overall efficiency of high-end equipment production and assembly.

[0044] In one example, the input parameters of the neighborhood search algorithm in S1 include: defining supplier index p = 1, 2, ..., f; component index i, g = 1, 2, ..., n, and i ≠ g; machine equipment index k = 1, 2, ..., m; manufacturer component order set {a1, ..., a i ,…,a n}, where a i represents the i-th component order of the manufacturer; the quantity of the component order produced by supplier p is c p ,∑c p =n; represents the production line of supplier p; j k Indicates the manufacturer's production line; e i represents the assembly time of different products; the machine start time is t, t = 1, 2, ..., T, t = 0 means it is not started; the delivery time d of the supplier to produce component i i ;e pi represents the planned processing time for supplier p to produce component i; the actual completion time for supplier p to produce component i The start time of production of component i by supplier p The unit penalty cost for delayed production by the supplier is the unit cost μ; the fixed cost E of the manufacturer when assembling equipment is activated fa ; The unit cost E of the manufacturer when assembling the equipment a ; Fixed cost E of supplier's production equipment when it is put into use fP ; Unit cost E of supplier's production equipment when it is put into use P ; The production cost EC of supplier p p ; Total assembly cost EC a .

[0045] If component a i Assigned to supplier p for production, then x pi is equal to 1, otherwise x pi Equal to 0; if component a in supplier p g In component a i If production is carried out before, then x pig is equal to 1, otherwise x pig Equal to 0; if in the manufacturer part a g In component a i Before assembly, then x ig is equal to 1, otherwise x ig is equal to 0; if component a i Assembled on manufacturer's machine k, then x ki is equal to 1, otherwise x ki is equal to 0; if component a i If x is produced on machine k of supplier p, then pki is equal to 1, otherwise x pki is equal to 0.

[0046] In some embodiments, in the upper-level problem, the total assembly cost EC of the manufacturer is a Calculate using the following formula:

[0047]

[0048] Where: E fa 、E a They represent the fixed cost and unit cost of the assembly equipment when it is activated; e i Represents the assembly time of different products;

[0049] In the lower problem, the production cost EC of supplier p is p Calculate using the following formula:

[0050]

[0051] Where: E fp 、E p They represent the fixed cost and unit cost of the supplier’s production equipment when it is put into use; d i represents the delivery time of the supplier to produce component i; represents the actual completion time of supplier p in producing component i; μ represents the unit penalty cost of supplier's delayed production.

[0052] In some embodiments, the perturbation neighborhood set for the oscillation operation is N k ,k=1,2,3,…k max ; The aforementioned S4 step may specifically include the following process:

[0053] S41, define variables x, y, g = 1, obtain the input initial population X0, and the current iteration number is k;

[0054] S42, randomly generate two integers in the interval [1, f] and assign them to variables x and y;

[0055] S43, select the xth and yth elements in the initial population X0, exchange the two elements, and obtain a new solution X1;

[0056] S44, let g = g + 1, determine whether g ≤ k is established, if yes, return to step S42; otherwise, execute step S45;

[0057] S45, perturb the type of resources used in the work tasks in the coding individuals, and perform the work tasks of all positions in the initial population X0 according to The probability of perturbing the resources used by the task is , and a specific resource in the random() resource set is used as the resource used by the task, and the optimal individual X is obtained. min ; Among them, 0≤a≤0.5, 0≤b≤0.5;

[0058] S46. Calculate the fitness value and determine the optimal individual X min Is it better than the initial solution X0? If so, output the new solution X′=X min ; Otherwise, output the new solution X′=X0.

[0059] In the embodiment of the present application, it can be understood that the present application corresponds to generating k max The oscillation operation is equivalent to jumping from the initial solution to another point in the feasible domain according to certain rules. This avoids continuous iteration under the same initial solution, which can easily lead to falling into the local optimal situation, and improves the global search ability of the algorithm.

[0060] In some embodiments, please refer to Figure 2 , the new solution X′ includes the PN sequence that characterizes the allocation of parts and the MU sequence that characterizes the manufacturer’s assembly machine selection;

[0061] Among them, each bit code in the PN sequence corresponds to the supplier number assigned to the parts order, and the position of each bit code corresponds to the part number assigned by the manufacturer to the supplier. The length of the PN sequence is n;

[0062] Each bit of code in the MU sequence represents the start-up time of the machine in the manufacturer's production line. The position of each bit of code corresponds to the number of the machine in the manufacturer's production line. The length of the MU sequence is m.

[0063] In the embodiments of the present application, it can be understood that the solutions in the present application are based on the characteristics of the two key stages of component production and product assembly, and the solutions of each stage are represented by a two-dimensional integer array.

[0064] In some embodiments, the aforementioned step S5 may specifically include the following process:

[0065] S51, based on the roulette probability, select a local search neighborhood structure from a preset neighborhood structure set, determine a neighborhood based on the new solution X′ and the local search neighborhood structure, and search for the optimal solution X″ in the neighborhood;

[0066] S52, comparing the optimal solution X″ with the new solution X′, if the optimal solution X″ is better than the new solution X′, determining the optimal solution X″ as the global optimal solution, and increasing the weight of the local search neighborhood structure for obtaining the global optimal solution; otherwise, reducing the weight of the current local search neighborhood structure, and returning to step S51;

[0067] S53, determine whether the current number of iterations reaches the maximum number of iterations, if so, output the current global optimal solution; otherwise, return to step S4.

[0068] In the embodiments of the present application, it should be noted that since the variable neighborhood search algorithm VNS local search algorithm will only recycle the local search neighborhood structure when a solution better than the initial solution is found in the neighborhood structure, in order to search for more effective neighborhood structures in advance, thereby reducing the number of unnecessary iterations and reducing time, a weight selection strategy is added to the variable neighborhood search algorithm: roulette probability selection.

[0069] In one example, the aforementioned step S52 may include:

[0070] S521, a new solution obtained by oscillating the initial solution X0, i.e., the initial solution X′ of the local search, is compared with the optimal solution X″ of the local search;

[0071] S522, by comparing the fitness values ​​of X″ and X′, in each iteration cycle, the formula Dynamically change the weight of the current local search neighborhood structure, Ω i is the weight of the neighborhood structure, where λ is a fixed parameter, The parameter value is obtained by the following judgment formula:

[0072]

[0073] S523. If the fitness value of X″ is better than X′, then obtain the neighborhood structure weight Ω corresponding to X″ i =λΩ i+(1-λ)ω1, the corresponding weight increases, and a larger proportion will be selected in the next round of roulette probability selection neighborhood structure; on the contrary, the neighborhood structure weight Ω corresponding to X″ i =λΩ i +(1-λ)ω2, the corresponding weight decreases, and the proportion in the next round of roulette probability selection neighborhood structure will be reduced; the real-time update of the algorithm's local search neighborhood structure weight, combined with the roulette probability selection mechanism and the iterative characteristics of VNS, effectively improves the optimization efficiency and performance of the algorithm's global search.

[0074] In some embodiments, the aforementioned step S51 may specifically include the following process:

[0075] S511, under the premise of the validity of the initial unknown local search neighborhood structure, determine that the initial neighborhood structure weights are equal; for the local search neighborhood structure set N of the variable neighborhood search algorithm l ,l=1,2,3,…l max , the initial weights corresponding to each neighborhood structure are

[0076] S512, based on the weight of each local search neighborhood structure, combined with the probability formula of roulette Calculate the cumulative probability of each neighborhood structure

[0077] S513, randomly generate r=random(0,1), if Q l >r>Q l-1 , then select the neighborhood structure N l Perform a local search.

[0078] In the embodiment of the present application, it can be understood that the neighborhood structure N obtained by the roulette probability weight strategy is l , this application will generate a population of Q individuals in the neighborhood structure by X′; obtain the local search neighborhood structure N by probability l , which corresponds to the neighborhood structure l.

[0079] In some embodiments, taking into account the large scale, large number and complexity of constraints of parts and components of high-end equipment manufacturing enterprises, which leads to a wide range and complex structure of the feasible domain, a total of 7 different neighborhood structures are set in the setting of the neighborhood structure set of the local search algorithm.

[0080] Specifically, the neighborhood structure set includes a first neighborhood structure, a second neighborhood structure, a third neighborhood structure, a fourth neighborhood structure, a fifth neighborhood structure, a sixth neighborhood structure, and a seventh neighborhood structure;

[0081] In the first neighborhood structure, define a variable x, randomly generate an integer within the range of [1, f], assign it to the variable x, reverse all the codes to the left of the position x in the PN sequence, and then for all positions, select resources with a probability of perform random random();

[0082] In the second neighborhood structure, define a variable x, randomly generate an integer within the range of [1, f], assign it to the variable x, reverse all the codes to the right of the position x in the PN sequence, and for all positions, select resources with a probability of perform random random();

[0083] In the third neighborhood structure, define variables x and y, randomly generate two integers within the range of [1, f], assign them to the variables x and y, where x ≤ y, reverse the codes between the positions x and y in the PN sequence, and for all positions, select resources with a probability of perform random random();

[0084] In the fourth neighborhood structure, define variables x and y, randomly generate two integers within the range of [1, f], assign them to the variables x and y, where x ≤ y, reverse the codes on both sides of the positions x and y in the PN sequence, and for all positions, select resources with a probability of perform random random();

[0085] In the fifth neighborhood structure, define variables x, y, and z, randomly obtain three integers within the range of [1, f] without replacement, assign them to the variables x, y, and z, where x < y < z, replace the code at the x position in the PN sequence with the code at the y position, the code at the y position with the code at the z position, and the code at the z position with the code at the x position, and for all positions, select resources with a probability of perform random random();

[0086] In the sixth neighborhood structure, define a variable x, randomly generate an integer within the range of [0, T], assign it to the variable x, reverse the codes on both sides of the x position in the MU sequence, and for all positions, select resources with a probability of perform random random();

[0087] In the seventh neighborhood structure, define variables x and y, randomly generate two integers within the range of [0, T], assign them to the variables x and y, where x ≤ y, reverse the codes between the positions x and y in the MU sequence, and for all positions, select resources with a probability of perform random random().

[0088] In some embodiments, after the aforementioned step S5, the two-level planning optimization method for parts production and product assembly further includes:

[0089] S601, based on the PN code in the global optimal solution, generate a set of parts to be distributed to different suppliers for processing; let p = 1, define a variable f ph =0, representing the processing completion time of the hth part of supplier p;

[0090] S602. Based on the supplier cost formula, use heuristic rules to solve the optimal supplier parts allocation strategy and the number of production lines to be opened. Let h = 1, k = 1, and define the variables

[0091] S603, f ph =f ph +e pi , i is the number of the corresponding component, e pi is the assembly time of product p of supplier, let h = h + 1;

[0092] S604: Determine whether the hth component If the production line is in production, execute step S603, otherwise, execute step S605;

[0093] S605, judgment Is it true? If so, define Execute step S603, otherwise, execute step S606;

[0094] S606: Determine h≥c p Is it true? If so, execute step S607; otherwise, return to step S602;

[0095] S607: Select the maximum value among the completion times of the various parts of the supplier obtained from steps S602 to S606, and assign it to but Define variable p = p + 1 as the minimum maximum completion time of supplier p;

[0096] S608, determine whether p≥f holds, if so, execute step S609, otherwise return to S602;

[0097] S609: Select the maximum value among the completion times of the various components obtained from steps S602 to S607, and assign it to T max , then T max is the supplier’s maximum completion time;

[0098] S610, define T≤T in MU coding in the global optimal solutionmax , the MU code in the global optimal solution generates the manufacturer's production machine opening set; let j k =1, representing the k-th production line of the manufacturer;

[0099] S611, according to the completion time of each component of the supplier obtained in steps S602 to S606, all f ph Sort from small to large, let p = 1, h = 1, f ph Represents the processing completion time of the hth part of supplier p.

[0100] In the embodiment of the present application, it can be understood that the decoding process is to obtain the global optimal solution, calculate the manufacturer's parts order allocation, production line selection in the assembly process, and parts production line allocation and production sequencing in the supplier's production process according to the actual scenario.

[0101] In an example, the aforementioned S602 may specifically include the following steps:

[0102] S602a, Initialize supplier parts allocation strategy and start production line Let k = 1, Initial allocation of parts to production lines

[0103] S602b, sort the production of parts on all production lines in order of delivery time from small to large, and calculate the current supplier's total production cost Ec p , if less than EC p * , then Ec p * Update to EC p ;

[0104] S602c, set k=k+1;

[0105] S602d, assign the parts with the longest delivery time among the remaining production lines to the production line Calculation of EC p , if EC p Less than EC p * Repeat the above steps until EC p If no change is made, execute S602e; otherwise, execute S602f;

[0106] S602e, EC p * Update to EC p , Execute S602b;

[0107] S602f, output the optimal production cost EC p * , supplier parts allocation strategy and start-up production line

[0108] In some embodiments, the present application provides a two-level planning optimization system 700 for parts production and product assembly, such as Figure 3 As shown, the two-layer planning optimization system 700 may include the following modules:

[0109] An input parameter setting module 710 is used to set input parameters based on a neighborhood search algorithm according to the obtained manufacturer's parts order allocation, production line selection in the assembly process, and the parts production line allocation and production sequencing in the supplier's production process;

[0110] An execution parameter setting module 720 is used to set the execution parameters of the algorithm, the execution parameters including: the initial weight of the local search neighborhood structure of the variable neighborhood search algorithm, the maximum number of iterations, and the number of individuals in the population;

[0111] An encoding module 730 is used to perform encoding according to the input parameters and the variable neighborhood search algorithm to obtain an initial population X0 consisting of multiple initial solutions;

[0112] The oscillation processing module 740 is used to process the initial population X0 based on the oscillation operation of the variable neighborhood search algorithm, obtain the individual population and the best individual in the individual population through the neighborhood structure, update the initial solution through the best individual, and obtain a new solution X′;

[0113] The output module 750 is used to search for the optimal solution X″ in the neighborhood based on the new solution X′ and the obtained local search neighborhood structure and compare it with the new solution X′ to determine the global optimal solution, and output the current global optimal solution when the current number of iterations reaches the maximum number of iterations.

[0114] According to an embodiment of the present application, any multiple modules among the input parameter setting module 710, the execution parameter setting module 720, the encoding module 730, the oscillation processing module 740 and the output module 750 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.

[0115] In some embodiments, the two-layer planning optimization system 700 may further include a decoding module 760, which may be specifically used to:

[0116] S601, based on the PN code in the global optimal solution, generate a set of parts to be distributed to different suppliers for processing; let p = 1, define a variable f ph =0, representing the processing completion time of the hth part of supplier p;

[0117] S602. Based on the supplier cost formula, use heuristic rules to solve the optimal supplier parts allocation strategy and the number of production lines to be opened. Let h = 1, k = 1, and define the variables

[0118] S603, f ph =f ph +e pi , i is the number of the corresponding component, e pi is the assembly time of product p of supplier, let h = h + 1;

[0119] S604: Determine whether the hth component If the production line is in production, execute step S603, otherwise, execute step S605;

[0120] S605, judgment Is it true? If so, define Execute step S603, otherwise, execute step S606;

[0121] S606: Determine h≥c p Is it true? If so, execute step S607; otherwise, return to step S602;

[0122] S607: Select the maximum value among the completion times of the various parts of the supplier obtained from steps S602 to S606, and assign it to but Define variable p = p + 1 as the minimum maximum completion time of supplier p;

[0123] S608, determine whether p≥f holds, if so, execute step S609, otherwise return to S602;

[0124] S609: Select the maximum value among the completion times of the various components obtained from steps S602 to S607, and assign it to T max , then T max is the supplier’s maximum completion time;

[0125] S610, define T≤T in MU coding in the global optimal solution max , the MU code in the global optimal solution generates the manufacturer's production machine opening set; let j k =1, representing the k-th production line of the manufacturer;

[0126] S611, according to the completion time of each component of the supplier obtained in steps S602 to S606, all f ph Sort from small to large, let p = 1, h = 1, f ph Represents the processing completion time of the hth part of supplier p.

[0127] Figure 3 Each module in the system shown has the function of implementing each step in the aforementioned two-level planning optimization method for parts production and product assembly, and can achieve its corresponding technical effect. For the sake of brevity, it will not be repeated here.

[0128] In some embodiments, the present application provides an electronic device, the structure diagram of the electronic device is as follows Figure 4 shown.

[0129] The electronic device may include a processor 810 and a memory 820 storing computer program instructions.

[0130] Specifically, the processor 810 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0131] The memory 820 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 820 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 820 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 820 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 820 is a non-volatile solid-state memory.

[0132] The memory 820 may include a read-only memory (ROM), a random access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory 820 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any of the above-mentioned embodiments of the two-level planning optimization method for component production and product assembly.

[0133] The processor 810 reads and executes computer program instructions stored in the memory 820 to implement any one of the two-level planning optimization methods for component production and product assembly in the above embodiments.

[0134] In one example, the electronic device may further include a communication interface 830 and a bus 800. Figure 4 As shown, the processor 810, the memory 820, and the communication interface 830 are connected via a bus 800 and communicate with each other.

[0135] The communication interface 830 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0136] Bus 800 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 800 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0137] In addition, in combination with the two-level planning optimization method for parts production and product assembly in the above embodiments, the present application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the two-level planning optimization methods for parts production and product assembly in the above embodiments is implemented.

[0138] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0139] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0140] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0141] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0142] In summary, compared with the prior art, this application has the following beneficial effects:

[0143] 1. This application optimizes the two-stage two-level planning problem of equipment parts production and product assembly by considering the two-stage two-level planning optimization of equipment parts production and product assembly, comprehensively considers the supplier's production cost and the manufacturer's assembly cost, solves the problem through a hybrid intelligent algorithm and obtains an approximate optimal solution, which can effectively optimize the coordinated scheduling of supplier production and manufacturer assembly, maximize the production and assembly efficiency of suppliers and manufacturers, reduce the total time of supplier production and manufacturer assembly, thereby ensuring that the parts produced by suppliers can be supplied to manufacturers on time, effectively solving the problem of supplier production and manufacturer assembly scheduling, and optimizing the overall efficiency of high-end equipment production and assembly.

[0144] 2. The embodiment of the present invention designs a hybrid genetic algorithm based on neighborhood search, which combines the global optimization of the genetic algorithm and the local search capability of the variable neighborhood search algorithm. At the same time, an adaptive selection mechanism based on search results is introduced into the variable neighborhood search algorithm, and a corresponding local search neighborhood structure is designed, which effectively improves the optimization efficiency of the algorithm and the quality of the solution.

[0145] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A two-level planning optimization method for parts production and product assembly, characterized in that: include: S1. Set input parameters based on the neighborhood search algorithm according to the obtained manufacturer's parts order allocation, production line selection in the assembly process, and parts production line allocation and production sequencing in the supplier's production process; S2. Setting execution parameters of the algorithm, wherein the execution parameters include: the initial weight of the local search neighborhood structure of the variable neighborhood search algorithm, the maximum number of iterations, and the number of individuals in the population; S3, encoding according to the input parameters and the variable neighborhood search algorithm to obtain an initial population X0 consisting of multiple initial solutions; S4, processing the initial population X0 based on the oscillation operation of the variable neighborhood search algorithm, obtaining the individual population and the best individual in the individual population through the neighborhood structure, updating the initial solution through the best individual, and obtaining a new solution X′; S5. Based on the new solution X′ and the obtained local search neighborhood structure, search for the optimal solution X″ in the neighborhood and compare it with the new solution X′ to determine the global optimal solution, and output the current global optimal solution if the current number of iterations reaches the maximum number of iterations.

2. The two-level planning optimization method for parts production and product assembly as claimed in claim 1, characterized in that: The perturbation neighborhood set of the oscillation operation is N k ,k=1,2,3,…k max ; The S4 step comprises: S41, define variables x, y, g = 1, obtain the input initial population X0, and the current iteration number is k; S42, randomly generate two integers in the interval [1, f] and assign them to variables x and y; S43, select the xth and yth elements in the initial population X0, exchange the two elements, and obtain a new solution X1; S44, let g=g+1, determine whether g≤k holds, if so, return to step S42; otherwise, execute step S45; S45, disturb the type resources used by the work tasks in the coding individuals, and perform the work tasks of all positions in the initial population X0 according to The probability of perturbing the resources used by the task is , and a specific resource in the random() resource set is used as the resource used by the task, and the optimal individual X is obtained. min ; Among them, 0≤a≤0.5, 0≤b≤0.5; S46. Calculate the fitness value and determine the optimal individual X min Is it better than the initial solution X0? If so, output the new solution X′=X min ; Otherwise, output the new solution X′=X0.

3. The two-level planning optimization method for parts production and product assembly as claimed in claim 1, characterized in that: The new solution X′ includes a PN sequence representing the allocation of parts and a MU sequence representing the manufacturer's assembly machine selection; Wherein, each bit of the PN sequence corresponds to the supplier number of the assigned parts order, the position of each bit of the PN sequence corresponds to the part number assigned by the manufacturer to the supplier, and the length of the PN sequence is n; Each bit of code in the MU sequence represents the start-up time of the machine in the manufacturer's production line, and the position of each bit of code corresponds to the serial number of the machine in the manufacturer's production line. The length of the MU sequence is m.

4. The two-level planning optimization method for parts production and product assembly as claimed in claim 3 is characterized in that: The S5 step includes: S51, based on the roulette probability, select a local search neighborhood structure from a preset neighborhood structure set, determine a neighborhood based on the new solution X′ and the local search neighborhood structure, and search for an optimal solution X″ in the neighborhood; S52, comparing the optimal solution X″ with the new solution X′, if the optimal solution X″ is better than the new solution X′, determining the optimal solution X″ as the global optimal solution, and increasing the weight of the local search neighborhood structure for obtaining the global optimal solution; otherwise, reducing the weight of the current local search neighborhood structure, and returning to step S51; S53, determine whether the current number of iterations reaches the maximum number of iterations, if so, output the current global optimal solution; otherwise, return to step S4.

5. The two-level planning optimization method for parts production and product assembly as claimed in claim 4 is characterized in that: The step S51 includes: S511, under the premise of the validity of the initial unknown local search neighborhood structure, determine that the initial neighborhood structure weights are equal; for the local search neighborhood structure set N of the variable neighborhood search algorithm l ,l=1,2,3,…l max , the initial weights corresponding to each neighborhood structure are S512, based on the weight of each local search neighborhood structure, combined with the probability formula of roulette Calculate the cumulative probability of each neighborhood structure S513, randomly generate r=random(0,1), if Q l >r>Q l-1 , then select the neighborhood structure N l Perform a local search.

6. The two-level planning optimization method for parts production and product assembly as claimed in claim 5, characterized in that: The neighborhood structure set includes a first neighborhood structure, a second neighborhood structure, a third neighborhood structure, a fourth neighborhood structure, a fifth neighborhood structure, a sixth neighborhood structure and a seventh neighborhood structure; In the first neighborhood structure, define a variable x, randomly generate an integer in the range [1, f], assign it to the variable x, reverse all the codes to the left of x in the PN sequence, and then select the resources at all positions with probability Perform random(); In the second neighborhood structure, define a variable x, randomly generate an integer in the range [1, f], assign it to the variable x, reverse the order of all the codes to the right of x in the PN sequence, and select the resources at all positions with probability Perform random(); In the third neighborhood structure, define variables x and y, randomly generate two integers in the range of [1, f], assign them to variables x and y, where x≤y, reverse the code between x and y in the PN sequence, and select resources at all positions with probability Perform random(); In the fourth neighborhood structure, define variables x and y, randomly generate two integers in the range of [1, f], assign them to variables x and y, where x≤y, reverse the codes on both sides of the PN sequence at positions x and y, and select resources at all positions with probability Perform random(); In the fifth neighborhood structure, define variables x, y, and z, and randomly obtain three integers within the range of [1, f] without replacement, and assign them to variables x, y, and z. Among them, x < y < z. Replace the code at the x position in the PN sequence with the code at the y position, the code at the y position with the code at the z position, and the code at the z position with the code at the x position, and perform random selection for all positions with a probability of randomize using random(); In the sixth neighborhood structure, a variable x is defined, an integer in the interval [0, T] is randomly generated and assigned to the variable x, the codes on both sides of the x position in the MU sequence are reversed, and the resource selection for all positions is performed with probability Perform random(); In the seventh neighborhood structure, define variables x and y, randomly generate two integers in the range [0, T], assign them to variables x and y, where x≤y, reverse the code between x and y in the MU sequence, and select resources at all positions with probability Perform a random operation using random().

7. The two-level planning optimization method for parts production and product assembly as claimed in claim 1 is characterized in that: After the step S5, the two-level planning optimization method for parts production and product assembly further includes: S601, generating a set of parts to be distributed to different suppliers for processing based on the PN code in the global optimal solution; Let p = 1, define the variable f ph =0, representing the processing completion time of the hth part of supplier p; S602. Based on the supplier cost formula, use heuristic rules to solve the optimal supplier parts allocation strategy and the number of production lines to be opened. Let h = 1, k = 1, and define the variables S603, f ph =f ph +e pi , i is the number of the corresponding component, e pi is the assembly time of product p of supplier, let h = h + 1; S604: Determine whether the hth component If the production line is in production, execute step S603, otherwise, execute step S605; S605, judgment Is it true? If so, define Execute step S603, otherwise, execute step S606; S606: Determine h≥c p Is it true? If so, execute step S607; otherwise, return to step S602; S607: Select the maximum value among the completion times of the various parts of the supplier obtained from steps S602 to S606, and assign it to but Define variable p = p + 1 as the minimum maximum completion time of supplier p; S608, determine whether p≥f holds, if so, execute step S609, otherwise return to S602; S609: Select the maximum value among the completion times of the various components obtained from steps S602 to S607, and assign it to T max , then T max is the supplier’s maximum completion time; S610: Define T≤T in MU coding in the global optimal solution max , the MU code in the global optimal solution generates the manufacturer's production machine opening set; let j k =1, representing the kth production line of the manufacturer; S611, according to the completion time of each component of the supplier obtained in steps S602 to S606, all f ph Sort from small to large, let p = 1, h = 1, f ph Represents the processing completion time of the hth part of supplier p.

8. A two-level planning optimization system for parts production and product assembly, characterized in that: include: An input parameter setting module is used to set input parameters based on a neighborhood search algorithm according to the obtained manufacturer's parts order allocation, production line selection in the assembly process, and parts production line allocation and production sequencing in the supplier's production process; An execution parameter setting module is used to set the execution parameters of the algorithm, wherein the execution parameters include: the initial weight of the local search neighborhood structure of the variable neighborhood search algorithm, the maximum number of iterations, and the number of individuals in the population; An encoding module, used for encoding according to the input parameters and the variable neighborhood search algorithm to obtain an initial population X0 consisting of multiple initial solutions; An oscillation processing module is used to process the initial population X0 based on the oscillation operation of the variable neighborhood search algorithm, obtain the individual population and the best individual in the individual population through the neighborhood structure, update the initial solution through the best individual, and obtain a new solution X′; The output module is used to search for the optimal solution X″ in the neighborhood based on the new solution X′ and the obtained local search neighborhood structure, compare it with the new solution X′, determine the global optimal solution, and output the current global optimal solution when the current number of iterations reaches the maximum number of iterations.

9. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the two-level planning optimization method for parts production and product assembly as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores programs or instructions, and when the programs or instructions are executed by the processor, the two-level planning optimization method for parts production and product assembly as described in any one of claims 1 to 7 is implemented.

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