Bi-level planning optimization method and system for parts production and product assembly

Abstract

The application provides a double-layer planning optimization method and system for part production and product assembly, relates to the technical field of supply chains, and the double-layer 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 composed of multiple initial solutions through coding; processing the initial population based on a shock operation of a variable neighborhood search algorithm, obtaining an individual population and an optimal individual in the individual population through a neighborhood structure, updating the initial solution through the optimal individual, and obtaining a new solution; searching for an optimal solution in the neighborhood and comparing the optimal solution with the new solution, determining a global optimal solution, and outputting the current global optimal solution whether the current number of times reaches a maximum iteration number. The method can effectively optimize the collaborative scheduling of supplier production and manufacturer assembly, maximizes the production and assembly efficiency of the supplier and the manufacturer, and reduces the total time of supplier production and manufacturer assembly.

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 after production.

[0003] The production and assembly of high-end equipment can be defined as a two-level planning problem. The top-level problem involves the manufacturer allocating all product components to suppliers for production. The suppliers then deliver the products to the manufacturer. The suppliers' component decisions determine the manufacturer's assembly costs. The bottom-level problem involves suppliers allocating allocated components to production lines and sequencing production to minimize production costs, taking into account their own production capacity and component delivery times.

[0004] Integrated scheduling of parts production and product assembly can achieve efficient collaboration between the two stages of production and assembly, maximizing resource utilization, reducing costs, and shortening delivery cycles. However, relatively little research has considered the two-stage, bi-level planning optimization problem of high-end equipment parts production and product assembly, and the collaborative efficiency of integrated supply chain scheduling still needs to be optimized and improved. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, this 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 component production and product assembly, the two-level planning optimization method comprising the following steps:

[0008] S1. Set the input parameters of the neighborhood search algorithm based on 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;

[0009] S2. Setting 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;

[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 optimal individual in the individual population through the neighborhood structure, update the initial solution through the optimal 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 component 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 of the neighborhood search algorithm based on 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;

[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 consisting 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, and update the initial solution through the optimal individual to 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 runnable 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 aforementioned first aspect is implemented.

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

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

[0022] This application recognizes the constraints of suppliers' production capacity and manufacturers' assembly capacity, considers the two-stage double-level planning optimization problem of equipment parts production and product assembly, optimizes the double-level planning problem of equipment parts production and product assembly, comprehensively considers the supplier's parts allocation, production sequence and production machine selection, and manufacturer's assembly machine selection, solves the problem through a variable neighborhood search algorithm and obtains the global optimal solution, which can effectively optimize the collaborative 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. 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

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

[0025] Figure 2 1 is a schematic diagram of an exemplary distribution of PN sequences and MU sequences provided in an embodiment of the present application;

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

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

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

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

[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 embodiments 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 often involves a complex and diverse range of parts. How these parts are allocated to suppliers for production determines the manufacturer's assembly costs. The specific process involves the manufacturer assigning parts orders to suppliers for production, and the suppliers then delivering the finished products to the manufacturer for assembly. This type of problem can be defined as a two-level planning problem. The top-level problem involves the manufacturer assigning parts to suppliers for production, and then the suppliers delivering the finished products to the manufacturer. Long delivery times incur penalty costs for the suppliers, and the suppliers' decisions on parts determine the manufacturer's assembly costs. The bottom-level problem involves suppliers allocating parts to production lines and prioritizing production to minimize production costs, taking into account their own production capacity and parts delivery times.

[0033] Integrated scheduling of parts production and product assembly can achieve efficient collaboration between the two stages of production and assembly, maximizing resource utilization, reducing costs, and shortening delivery cycles. However, relatively little research has considered the two-stage, bi-level planning optimization problem of high-end equipment parts production and product assembly, and the collaborative efficiency of integrated supply chain 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 with reference to 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 embodiment of the present application provides a flow chart of a two-level planning optimization method for parts production and product assembly, as shown in FIG. Figure 1 As shown, the two-level planning optimization method may include the following steps S1-S5.

[0037] S1. Set the input parameters of the neighborhood search algorithm based on 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;

[0038] S2. Setting 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;

[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 optimal individual in the individual population through the neighborhood structure, update the initial solution through the optimal 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 production capacity constraints of suppliers and the assembly capacity constraints of manufacturers, 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 the global optimal solution, which can effectively optimize the collaborative 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.

[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 manufacturer's i-th component order; the supplier p's production component order quantity 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 supplier's delivery time d for 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 supplier's delayed production is unit cost μ; the fixed cost E of manufacturer's assembly equipment startup fa ; The unit cost E of the manufacturer when assembling the equipment a ; Fixed cost E of supplier’s production equipment startup fP ; The unit cost E of the supplier's production equipment when it is put into use P ; Supplier p's production cost EC 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 Production was carried out before, then x pig is equal to 1, otherwise x pig Equal to 0; if the manufacturer has component a g In component a i Before assembly, then x ig is equal to 1, otherwise x ig Equal to 0; if component a i Assemble on manufacturer's machine k, then x ki is equal to 1, otherwise x ki Equal to 0; if component a i Production is performed on machine k of supplier p, then x pki is equal to 1, otherwise x pki Equal to 0.

[0046] In some embodiments, in the upper-level problem, the manufacturer's total assembly cost EC 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 put into use; 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 supplier's delivery time for producing 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 step S4 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, swap these two elements, and obtain a new solution X1;

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

[0057] S45, perturb the type of resources used in the work tasks of 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 used, and a specific resource in the random() resource set is used as the resource used by the task to obtain the optimal individual X 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 makes it easy to fall 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 parts allocation and the MU sequence that characterizes the manufacturer’s assembly machine selection;

[0061] Wherein, each bit of the PN sequence corresponds to the supplier number assigned to the parts order, and the position of each bit of the PN sequence 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 solution in the present application is based on the characteristics of the two key stages of component production and product assembly, and the solutions of each stage are represented by two-dimensional integer arrays.

[0064] In some embodiments, the foregoing S5 step can specifically include the following process:

[0065] S51, selecting a local search neighborhood structure from the preset neighborhood structure set based on a roulette probability, determining a neighborhood based on the new solution X' and the local search neighborhood structure, and searching for an 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 that the optimal solution X" is a global optimal solution, and increasing the weight of the local search neighborhood structure from which the global optimal solution is obtained; otherwise, decreasing the weight of the current local search neighborhood structure, and returning to step S51;

[0067] S53, determining whether the current iteration number reaches a maximum iteration number, if yes, outputting the current global optimal solution; otherwise, returning 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 only searches for a better solution than the initial solution in the neighborhood structure, and then re-circulates the neighborhood structure of the local search, in order to search for more effective neighborhood structures in advance, thereby reducing unnecessary iteration times and time, the variable neighborhood search algorithm adds a weight selection strategy: roulette probability selection.

[0069] In one example, the foregoing S52 step can include:

[0070] S521, comparing the new solution obtained by the initial solution X0 through the shock operation, i.e., the initial solution X' of the local search, 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 fitness value of X" is compared with the fitness value of X' by the formula dynamically changing 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 of is obtained by the following judgment formula:

[0072]

[0073] S523, if the fitness value of X" is better than that of X', the neighborhood structure weight Ω i of X" is obtained 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 wide range and complex structure of the feasible domain due to the large scale, large number and complexity of the constraints of parts and components of high-end equipment manufacturing enterprises, 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 the 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 left of x in the PN sequence, and then select the resources at all positions with probability Perform random();

[0082] In the second neighborhood structure, define the 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();

[0083] In the third neighborhood structure, define variables x and y, randomly generate two integers in the range [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();

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

[0085] In the fifth neighborhood structure, define variables x, y, and z. Randomly obtain three integers in the interval [1, f] without replacement and assign them to the variables x, y, and z. <y<z,将PN序列中x位置的编码替换到y位置,y位置的编码替换到z位置,z位置的编码替换到x位置,并对所有位置的资源选择以概率 Perform random();

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

[0087] 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 random().

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

[0089] S601. Generate a set of parts to be processed by different suppliers 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;

[0090] S602. Based on the supplier cost formula, use heuristic rules to solve the optimal solution for the 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 supplier p’s product, let h = h + 1;

[0092] S604, determine whether the hth component If the production line is in production, go to step S603, otherwise go to 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, go to step S607; otherwise, return to step S602;

[0095] S607: Select the maximum value among the completion times of each supplier component 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 manufacturer's production machine opening set is generated by MU coding in the global optimal solution; let j k =1, representing the k-th production line of the manufacturer;

[0099] S611, according to the completion time of each supplier component 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, and calculate the manufacturer's parts order allocation, production line selection in the assembly process, and parts production line allocation and production sorting 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 it is less than EC p * , then Ec p * Updated 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 Calculating 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 * Updated to EC p , Execute S602b;

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

[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-level planning optimization system 700 may include the following modules:

[0109] An input parameter setting module 710 is used to set input parameters based on the neighborhood search algorithm based on 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 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;

[0111] An encoding module 730 is configured to perform encoding based on input parameters and a 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 optimal individual in the individual population through the neighborhood structure, and update the initial solution through the optimal individual to obtain a new solution X′;

[0113] 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, 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.

[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 a single module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in a single module.

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

[0116] S601. Generate a set of parts to be processed by different suppliers 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;

[0117] S602. Based on the supplier cost formula, use heuristic rules to solve the optimal solution for the 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 supplier p’s product, let h = h + 1;

[0119] S604, determine whether the hth component If the production line is in production, go to step S603, otherwise go to 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, go to step S607; otherwise, return to step S602;

[0122] S607: Select the maximum value among the completion times of each supplier component 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 manufacturer's production machine opening set is generated by MU coding in the global optimal solution; let j k =1, representing the k-th production line of the manufacturer;

[0126] S611, according to the completion time of each supplier component 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 effects. For the sake of brevity, it will not be repeated here.

[0128] In some embodiments, the present application provides an electronic device, the structural 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. Where appropriate, the memory 820 may include removable or non-removable (or fixed) media. Where appropriate, 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 magnetic 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 may perform the operations described in any one of the bi-level planning optimization methods for component production and product assembly in the above-described embodiments.

[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 metering equipment are coupled to each other. For example, but not limitation, bus can include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry 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 can include one or more buses. Although the present application embodiment describes and shows specific bus, the application considers any suitable bus or interconnection.

[0137] In addition, in conjunction with the two-level planning optimization method for component production and product assembly in the above-mentioned embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, they implement any of the two-level planning optimization methods for component production and product assembly in the above-mentioned embodiments.

[0138] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of 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, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet, intranets, 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. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0141] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments 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 is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the 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, 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, and can effectively optimize the collaborative 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 they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 various embodiments of the present invention.

Claims

1. A two-level planning optimization method for parts production and product assembly, characterized by: include: S1. Set the input parameters of the neighborhood search algorithm based on 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; 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 composed of multiple initial solutions ; S4, processing the initial population 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 ; S5. Based on the new solution And the obtained local search neighborhood structure, search for the optimal solution in the neighborhood And with the new solution Perform comparison 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; The new solution Including characterization of component allocation sequence, and characterize the manufacturer's assembly machine selection sequence; Among them, the Each bit of code in the sequence corresponds to the supplier number assigned to the parts order, and the position of each bit of code corresponds to the part number assigned to the supplier by the manufacturer. The length of the sequence is ; described Each bit of code in the sequence represents the start time of the machine in the manufacturer's production line, and the position of each bit of code corresponds to the number of the machine in the manufacturer's production line. The length of the sequence is ; After step S5, the two-level planning optimization method for parts production and product assembly further includes: S601, based on the global optimal solution Coding, generating a collection of parts that are assigned to different suppliers for processing; , define the variable , representing suppliers No. The processing completion time of each part; S602. Based on the supplier cost formula, use heuristic rules to solve the optimal solution for the supplier parts allocation strategy and the number of production lines to be opened. ,make , define the variable ; S603, , is the number of the corresponding parts, For suppliers Product assembly time, ; S604, determine whether the hth component If the production line is in production, go to step S603, otherwise go to step S605; S605, judgment Is it true? If so, define , execute step S603, otherwise, execute step S606; S606, judgment Is it true? If so, go to step S607; otherwise, return to step S602; S607: Select the maximum value among the completion times of each supplier component obtained from steps S602 to S606 and assign it to ,but For suppliers The minimum and maximum completion time of the project, define the variable ; S608, judgment Is it true? If so, go to step S609, otherwise return to S602; S609: Select the maximum value among the completion times of the components obtained from steps S602 to S607 and assign it to ,but is the supplier's maximum completion time; S610, defining the global optimal solution In coding , from the global optimal solution The code generation manufacturer's production machine opens the collection; , representing the manufacturer's production lines; S611, according to the completion time of each supplier component obtained in steps S602 to S606, all Sort from small to large, , Representative Supplier No. The processing completion time of each part.

2. The two-level planning optimization method for parts production and product assembly according to claim 1, characterized in that: The perturbation neighborhood set of the oscillation operation is ; The S4 step comprises: S41. Define variables , get the initial population of input , the current iteration number is ; S42, randomly generate two Assign the integer in to the variable ; S43. Select the initial population The and elements, swap these two elements and get a new solution ; S44, Order ,judge Is it true? If so, return to step S42; otherwise, execute step S45; S45, disturb the type of resources used in the work tasks of the coding individuals, and All positions in the job tasks are as follows The probability of perturbing the resources used by the task is used, and a specific resource in the random() resource set is used as the resource used by the task to obtain the optimal individual ;in, ; S46. Calculate the fitness value and determine the optimal individual Is it better than the initial solution? If so, output the new solution Otherwise, output the new solution .

3. The two-level planning optimization method for parts production and product assembly according to claim 1, characterized in that: The S5 step includes: S51, based on the roulette probability, select a local search neighborhood structure from the preset neighborhood structure set, based on the new solution The local search neighborhood structure determines the neighborhood and searches for the optimal solution in the neighborhood ; S52, the optimal solution With the new interpretation For comparison, if the optimal solution Better than the new solution , determine the optimal solution is the global optimal solution, and the weight of the local search neighborhood structure that obtains the global optimal solution is increased; otherwise, the weight of the current local search neighborhood structure is reduced, and the process returns 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.

4. The two-level planning optimization method for parts production and product assembly according to claim 3, characterized in that: The S51 step 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 of the variable neighborhood search algorithm , 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, random generation ,if , then select the neighborhood structure Perform a local search.

5. The two-level planning optimization method for parts production and product assembly according to claim 4 is 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 the variable , randomly generate a Integer in the range, assigned to the variable ,Will Position in the sequence is All the codes on the left are processed in reverse order, and then the resources at all positions are selected with probability Perform random(); In the second neighborhood structure, define the variable , randomly generate a Integer in the range, assigned to the variable ,Will Position in the sequence is All the codes on the right are processed in reverse order, and the resources at all positions are selected with probability Perform random(); In the third neighborhood structure, define the variable , randomly generate two Integer in the range, assigned to the variable ,in, ,Will Position in the sequence is The codes between are processed in reverse order, and the resource selection of all locations is based on probability. Perform random(); In the fourth neighborhood structure, define the variable , randomly generate two Integer in the range, assigned to the variable ,in, ,Will The position in the sequence is The codes on both sides are processed in reverse order, and the resource selection of all positions is based on probability. Perform random(); In the fifth neighborhood structure, define the variable , randomly obtain three Integer in the range, assigned to the variable ,in, ,Will in sequence The position code is replaced by Location, The position code is replaced by Location, The position code is replaced by Location, and select resources at all locations with probability Perform random(); In the sixth neighborhood structure, define the variable , randomly generate a Integer in the range, assigned to the variable ,Will in sequence The codes on both sides of the position are reversed, and the resource selection of all positions is based on probability. Perform random(); In the seventh neighborhood structure, define the variable , randomly generate two Integer in the range, assigned to the variable ,in, ,Will Position in the sequence is The codes between are processed in reverse order, and the resource selection of all locations is based on probability. Perform random().

6. A two-level planning optimization system for parts production and product assembly, based on the two-level planning optimization method for parts production and product assembly according to any one of claims 1 to 5, characterized in that: include: An input parameter setting module is used to set input parameters based on the neighborhood search algorithm based on 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; The encoding module is used to encode according to the input parameters and the variable neighborhood search algorithm to obtain an initial population composed of multiple initial solutions. ; An oscillation processing module is used to process the initial population 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 ; Output module for And the obtained local search neighborhood structure, search for the optimal solution in the neighborhood And with the new solution Perform comparison 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.

7. 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 component production and product assembly as described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program or instruction, and when the program or instruction 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 5 is implemented.

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