High-end equipment development and batch production collaborative scheduling method based on production outsourcing situation

Abstract

This invention provides a method, system, storage medium, and electronic device for collaborative scheduling of high-end equipment R&D and mass production based on outsourcing scenarios, relating to the field of collaborative scheduling of high-end equipment R&D and mass production. This invention addresses the two-stage collaborative scheduling problem of R&D and mass production for high-end equipment manufacturers, adding the scenario of manufacturers outsourcing orders, thus meeting the actual needs of high-end equipment enterprises. For the scenario where manufacturers conduct R&D and mass production themselves, the optimization objective is to maximize the manufacturer's overall profit. The invention obtains the set of self-developed mass production orders and their corresponding R&D teams and production lines, and determines the set of outsourced orders accordingly. Furthermore, an improved iterative greedy algorithm is used to quickly and accurately obtain an approximate optimal solution to the problem. The solution can meet the production needs of high-end equipment manufacturers, improve the efficiency of high-end equipment R&D and mass production, and increase the total revenue of high-end equipment manufacturers.

Description

Technical Field

[0001] This invention relates to the field of collaborative scheduling of high-end equipment development and mass production, specifically to a method, system, storage medium, and electronic device for collaborative scheduling of high-end equipment development and mass production based on production outsourcing. Background Technology

[0002] High-end equipment, as technologically advanced devices at the top of the manufacturing value chain, faces fierce market competition. To cope with an uncertain market environment and rising production costs, outsourcing has gradually become a means for high-end equipment companies to solve production and operational problems. In the case of production outsourcing, high-end equipment manufacturers entrust a portion of their orders to outsourcing companies to ensure they have sufficient resources for product research and development and mass production. However, implementing outsourcing strategies increases the difficulty of scheduling for high-end equipment manufacturers. On the one hand, manufacturers need to decide which orders to outsource; on the other hand, they need to coordinate the research and mass production of the remaining orders.

[0003] In related technologies, existing research and development-mass production scheduling problems are mainly analyzed based on traditional production models, i.e., scheduling in two stages sequentially according to time. However, these problems fail to consider the situation of production outsourcing, resulting in solutions that do not meet the actual needs of manufacturers. At the same time, existing solutions mainly use some precise algorithms and simulation algorithms, which increases the computation time and fails to quickly obtain the optimal solution, thus wasting resources for high-end equipment manufacturers. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a method, system, storage medium, and electronic device for collaborative scheduling of high-end equipment development and mass production based on production outsourcing scenarios, solving the technical problem that existing technologies have failed to take production outsourcing scenarios into account.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for collaborative scheduling of high-end equipment development and mass production based on production outsourcing, comprising:

[0009] Obtain information on high-end equipment orders, as well as data on manufacturers' R&D teams and production lines;

[0010] An initialization population consisting of multiple initial solutions is generated by adopting a three-segment coding rule based on the order layer, R&D layer, and mass production layer. The order allocation sequence of the initial solution is generated by comparing the outsourcing revenue of the order with the self-developed mass production revenue.

[0011] For each initial solution, a destructive operation is performed. Then, by comparing the outsourcing revenue of the order with the revenue of self-developed mass production, and with the optimization objective of maximizing the manufacturer's total revenue, multiple new solutions are obtained.

[0012] A partial search and update is performed on the manufacturer's production orders in the new solution;

[0013] Determine if the temperature is greater than the iteration termination temperature. If so, re-execute the local search update operation until the condition is met; otherwise, output the global optimal solution, decode to obtain the set of self-developed mass production orders and their corresponding R&D teams and production lines, and combine the high-end equipment order information to obtain the set of outsourced orders.

[0014] Preferably, the step of generating an initial population including multiple initial solutions by adopting a three-segment coding rule based on the order layer, research and development layer, and mass production layer includes:

[0015] Based on the high-end equipment order information, an order allocation sequence for the order layer is generated sequentially, and the order index j = 1 of the order allocation sequence is initialized.

[0016] Calculate the outsourcing revenue NP for order j oj Compared with the revenue from self-developed mass production NP aj ;

[0017] Comparing outsourcing benefits NP oj and the revenue from self-developed and mass-produced products (NP) aj The size of NP; if NP oj ≥NP aj Order j will be outsourced; otherwise, the manufacturer will develop and mass-produce the product and allocate it to the corresponding R&D team r and production line i.

[0018] Update order j at the earliest start time (min) of development team r. jr ), and the earliest start time min(BT) of batch production at production line i. ij );

[0019] Let j = j + 1. If j is less than or equal to the total number of orders n, then recalculate the outsourcing revenue NP for order j. oj Compared with the revenue from self-developed mass production NP aj Continue until the conditions are met; otherwise, terminate population initialization.

[0020] Preferably, the outsourcing revenue NP of order j oj Compared with the revenue from self-developed mass production NP aj The calculation method is as follows:

[0021] NP oj =u j -oc j(1)

[0022] NP aj =u j -max {FT j -d j ,0}·yc j PC r ·YT jr PC i ·PT ij (2)

[0023] Among them, u j Represents the revenue of order j; oc j This represents the outsourcing cost for order j;

[0024] Max is the maximization function; FT j This indicates the completion time for order j, which is developed and mass-produced by the manufacturer; d j This indicates the delivery date for order j; yc j PC represents the overdue unit penalty cost for order j. r YT represents the research and development cost of the research and development team r; jr Indicates the development time of order j within development team r; PC i Batch production cost of production line j; PT ij This indicates the batch production time of order j on production line i.

[0025] Preferably, the earliest start time (min) of order j at the research and development team r is... jr ) and the earliest start time min (BT) of batch production at production line i. ij The calculation method for ) is as follows:

[0026]

[0027]

[0028] Where min is the minimization function; CS jr Indicates the start time of order j within the research and development team r; This indicates the time required to develop the order that precedes order j and is handled by development team r.

[0029] BT ij Indicates the start time of order j in production line i; WT jr This indicates the completion time of order j within the research and development team r; This indicates the time required for the batch production of the order that precedes order j and is handled by production line i.

[0030] Preferably, the step of performing a destructive operation on each initial solution, and then comparing the outsourcing revenue with the self-developed mass production revenue, with the optimization objective of maximizing the manufacturer's total revenue, yields multiple new solutions; including:

[0031] Based on the initial solution x 0 The number of orders for domestically developed and mass-produced products Given the destruction rate θ, generate a corresponding number of position coordinates and remove the initial solution x. 0 The order at the corresponding location and its corresponding R&D team and production line yield an incomplete solution x. ′ And the removed orders are placed into the set δ of the orders to be inserted;

[0032] Randomly select an order j from set δ, remove it from the set, and insert it into x. ′ All possible locations;

[0033] After updating order j, the completion time of the manufacturer's research and development batch production for each order is updated, and the revenue NP of the self-developed batch production for each order is recalculated. aj If we develop and mass-produce our own products, the NP (NP) revenue will be... aj Lower than the outsourcing revenue NP oj If so, the order processing will be outsourced.

[0034] Calculate the manufacturer's total revenue TP at different insertion positions, and select the scheme with the highest manufacturer's total revenue TP as the order j insertion scheme; the calculation method for the manufacturer's total revenue TP is as follows:

[0035]

[0036] Where s is the total number of research and development teams; m is the total number of production lines; X j For decision variables;

[0037] Determine if set δ is empty. If not, proceed to S32 to select a new order j from set δ. ′ Perform the insertion operation until the condition is met; if so, end the reconstruction operation and output the new solution x. 1 .

[0038] Preferably, the following neighborhood structures are designed to perform local search update operations:

[0039] Neighborhood Structure 1: Define variable h1, randomly generate an integer in the interval [1,n] and assign it to h1. For the order allocation sequence PS of the order layer, determine whether the orders at the h1th position and the 1st position are both developed and produced by the manufacturer. If so, swap the order codes at the h1th position and the 1st position; otherwise, do not change.

[0040] Neighborhood Structure 2: Define variables h1 and h2, randomly generate two integers in the interval [1, n], assign them to h1 and h2 such that h1 < h2. For the research team assignment sequence RS in the research layer, determine whether the orders at the h1-th and h2-th positions are both for manufacturer research. If so, swap the order codes at the h1-th and h2-th positions; otherwise, do not change.

[0041] Neighborhood Structure 3: Define variables h1 and h2, randomly generate two integers in the interval [1, n], assign them to h1 and h2 such that h1 < h2. For the production line assignment sequence BS in the mass production layer, swap the order codes of the orders responsible for mass production by the manufacturer on the left side of the h1-th position and on the right side of the h2-th position.

[0042] Neighborhood Structure 4: Define variables h1, h2, and h3, randomly generate three integers in the interval [1, n], assign them to h1, h2, and h3 such that h1 < h2 < h3. For the research team assignment sequence RS in the research layer, reverse the order codes of the orders responsible for mass production by the manufacturer on the left side of the h1-th position and on the right side of the h3-th position. For the production line assignment sequence BS in the mass production stage, reverse the order codes of the orders responsible for mass production by the manufacturer between the h1-th and h2-th positions.

[0043] Preferably, the decoding rules include:

[0044] For the research team assignment sequence in the research layer, obtain the orders assigned to each research team. When multiple orders are assigned to research team r, compare the research times YT of different orders j in this research team r jr , select the order j with a longer research time for research first. If the research times of the orders are the same, then preferentially select the order j with a higher income u j for research;

[0045] For the production line assignment sequence in the mass production layer, obtain the orders assigned to each production line. When multiple orders are assigned to production line i, compare the earliest completion times min(WT jr ) of different orders j being developed, select the order j with the earliest research completion time for mass production. If the research completion times of the orders are the same, then preferentially select the order j with a higher income u j for mass production;

[0046] Combine the decoding results of the two parts to obtain the set of self-developed mass production orders and the corresponding research teams and production lines.

[0047] A high-end equipment research and mass production collaborative scheduling system based on the production outsourcing scenario, including:

[0048] The acquisition module is used to acquire information on high-end equipment orders, as well as data on manufacturers' R&D teams and production lines.

[0049] An initialization module is used to generate an initialization population including multiple initial solutions by adopting a three-segment coding rule based on the order layer, R&D layer, and mass production layer; wherein, by comparing the outsourcing revenue of an order with the self-developed mass production revenue, the order allocation sequence of the order layer of the initial solution is generated.

[0050] The comparison module is used to destroy each initial solution, and then compare the outsourcing revenue of the order with the self-developed mass production revenue, and obtain multiple new solutions with the optimization objective of maximizing the manufacturer's total revenue;

[0051] The update module is used to perform a partial search and update of the manufacturer's production orders in the new solution;

[0052] The output module is used to determine whether the temperature is greater than the iteration termination temperature. If so, the local search update operation is re-executed until the condition is met; otherwise, the global optimal solution is output, the self-developed batch production order set and its corresponding R&D team and production line are decoded and obtained, and the outsourced order set is obtained by combining the high-end equipment order information.

[0053] A storage medium storing a computer program for collaborative scheduling of high-end equipment development and mass production based on production outsourcing, wherein the computer program causes a computer to execute the high-end equipment development and mass production collaborative scheduling method as described above.

[0054] An electronic device, comprising:

[0055] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing the high-end equipment development and mass production collaborative scheduling method as described above.

[0056] (III) Beneficial Effects

[0057] This invention provides a method, system, storage medium, and electronic device for collaborative scheduling of high-end equipment development and mass production based on outsourcing. Compared with existing technologies, it has the following advantages:

[0058] Beneficial effects:

[0059] This invention addresses the two-stage collaborative scheduling problem of R&D and mass production for high-end equipment manufacturers, incorporating the scenario of manufacturers outsourcing orders, thus aligning with the actual needs of high-end equipment enterprises. For the scenario where manufacturers conduct in-house R&D and mass production, the optimization objective is to maximize the manufacturer's overall profit. This involves obtaining the set of in-house R&D and mass production orders, along with their corresponding R&D teams and production lines, and determining the set of outsourced orders accordingly. Furthermore, an improved iterative greedy algorithm is used to quickly and accurately find the approximate optimal solution to the problem. The solution meets the production needs of high-end equipment manufacturers, improves the efficiency of high-end equipment R&D and mass production, and increases the overall revenue of high-end equipment manufacturers. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A block diagram illustrating a collaborative scheduling method for high-end equipment development and mass production based on production outsourcing, provided as an embodiment of the present invention.

[0062] Figure 2 A flowchart illustrating an improved iterative greedy algorithm provided in an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of a three-segment coding sequence based on the order layer, research and development layer, and mass production layer, provided for an embodiment of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] This application provides a method, system, storage medium, and electronic device for collaborative scheduling of high-end equipment development and mass production based on outsourcing scenarios. It addresses the technical problem that existing technologies fail to consider outsourcing scenarios, providing a reference for quickly and accurately finding the optimal solution. In other words, this method can quickly find an approximate optimal solution to the problem, meeting the development and mass production scheduling needs of manufacturers.

[0066] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0067] Existing optimization problems for the research, development, and mass production of high-end equipment mainly consider the two-stage process of manufacturers developing and mass-producing their own equipment, failing to incorporate outsourcing scenarios. Therefore, the scheduling results do not meet the requirements of real-world manufacturing enterprises. Furthermore, solving such problems often relies on precise algorithms and simulation algorithms, resulting in low efficiency. To address these issues, this invention proposes a collaborative scheduling method for the research, development, and mass production of high-end equipment based on outsourcing scenarios. By incorporating outsourcing providers, an improved iterative greedy method accurately and quickly finds the approximate optimal solution, thereby improving the efficiency of high-end equipment manufacturers' research, development, and mass production, and maximizing manufacturers' profits.

[0068] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0069] Example 1:

[0070] like Figure 1 As shown, a collaborative scheduling method for the development and mass production of high-end equipment based on production outsourcing includes:

[0071] S1. Obtain information on high-end equipment orders, and data on manufacturers' R&D teams and production lines;

[0072] S2. An initialization population including multiple initial solutions is generated by adopting a three-segment coding rule based on the order layer, R&D layer and mass production layer; wherein, by comparing the outsourcing revenue of the order with the self-developed mass production revenue, the order allocation sequence of the order layer of the initial solution is generated.

[0073] S3. Destroy each initial solution, compare the outsourcing revenue of the order with the self-developed mass production revenue, and obtain multiple new solutions with the goal of maximizing the manufacturer's total revenue.

[0074] S4. Perform a partial search and update for the manufacturer's production orders in the new solution;

[0075] S5. Determine if the temperature is greater than the iteration termination temperature. If yes, proceed to S4 to re-execute the local search update operation until the condition is met; otherwise, output the global optimal solution, decode to obtain the set of self-developed batch production orders and their corresponding R&D teams and production lines, and combine the high-end equipment order information to obtain the set of outsourced orders.

[0076] This invention addresses the two-stage collaborative scheduling problem of R&D and mass production for high-end equipment manufacturers, adding the scenario of manufacturers outsourcing orders, thus meeting the actual needs of high-end equipment enterprises. For the scenario where manufacturers conduct R&D and mass production in-house, the optimization objective is to maximize the manufacturer's overall profit. This involves obtaining a set of in-house R&D and mass production orders along with their corresponding R&D teams and production lines, and then determining the set of outsourced orders accordingly.

[0077] Furthermore, the improved iterative greedy algorithm can quickly and accurately obtain the approximate optimal solution to the problem. The solution can meet the production needs of high-end equipment manufacturers, improve the efficiency of high-end equipment research and development and mass production, and increase the total revenue of high-end equipment manufacturers.

[0078] First, it should be noted that the solution provided by this invention takes into account the following constraints:

[0079] First, outsourced orders are developed and mass-produced by the outsourcing company, which has unlimited production capacity and no overdue issues.

[0080] Second, orders for which the manufacturer is responsible for research and development and mass production are subject to predecessor-successor constraints, meaning that mass production can only begin after the research and development is completed.

[0081] Third, the research and development team cannot work on the development of two orders at the same time, and the production line cannot carry out the mass production of two orders at the same time.

[0082] like Figure 2 As shown, Figure 2 A flowchart of the improved iterative greedy algorithm is given, and the following will be a collection of... Figure 2 The steps of the above scheme are described in detail:

[0083] Corresponding to step S1, the first step is to obtain high-end equipment order information, manufacturer's R&D team and production line data, in order to set the input parameters for the iterative greedy algorithm, including at least:

[0084] Order index j = 1, 2, ..., n;

[0085] Research team index r = 1, 2, ..., s;

[0086] Production line index i = 1, 2, ..., m;

[0087] Order revenue u j ;

[0088] Order outsourcing fees j ;

[0089] Order delivery period d j ;

[0090] The development cost PC of the research and development team r r ;

[0091] Manufacturer's production line batch cost PC i .

[0092] It is not difficult to understand that, in addition to obtaining the above-mentioned execution parameters, the embodiments of the present invention also implicitly require the pre-setting of the running parameters of the iterative greedy algorithm, including the initial temperature T0 and the annealing coefficient. Termination temperature T f The destruction rate is θ, the number of neighborhood structure sets is NS, and the neighborhood set count is initialized to ω = 0.

[0093] Corresponding to step S2, a three-segment coding rule based on the order layer, R&D layer, and mass production layer is then used to generate an initialization population containing multiple initial solutions. Specifically, by comparing the outsourcing revenue with the self-developed mass production revenue of an order, an order allocation sequence for the order layer of the initial solutions is generated.

[0094] This invention innovatively proposes a three-segment coding rule based on the order layer, R&D layer, and mass production layer to encode the scheduling scheme. The first segment is the order layer code PS, representing the order allocation sequence; the second segment is the R&D layer code RS, representing the R&D team allocation sequence corresponding to the order; and the third segment is the mass production layer code BS, representing the production line allocation sequence corresponding to the order. These three segments together form the initial solution x. 0 Specifically, it includes:

[0095] The generation of initial solutions includes the generation of initial solutions for order allocation, the generation of initial solutions for the development phase, and the generation of initial solutions for the mass production phase. The assignments in the development phase are based on the development team, while the assignments in the mass production phase are based on the production line. The initial solutions are divided into three parts:

[0096] like Figure 3 As shown, the encoding length of the order layer is the number of orders n, x j ={0,1}, where 0 is used when the order is outsourced, and 1 is used otherwise. Each column of the R&D layer corresponds to the R&D team of the order, where y r ={0,1,2…s}, when y r When z = 0, it indicates that the order is outsourced. Each column of the batch production layer represents the production line assigned to the order, where z i ={0,1,2…m}, when z i When x = 0, it indicates that the order is outsourced. Therefore, the resulting initial solution can be represented as x. 0 ={x1,…,x j ,…,x n ;y1,…,y r ,…,y s ;z1,…,z i ,…,z m}

[0097] Based on this, this step specifically includes:

[0098] S21. Based on the high-end equipment order information, generate the order allocation sequence of the order layer in sequence, and initialize the order index j=1 of the order allocation sequence.

[0099] S22. Calculate the outsourcing revenue NP for order j. oj Compared with the revenue from self-developed mass production NP aj The calculation method is as follows:

[0100] NP oj =u j -oc j (1)

[0101] NP aj =u j -max {FT j -d j ,0}·yc j PC r ·YT jr PC i ·PT ij (2)

[0102] Where Max is the maximization function; FT j This indicates the completion time for order j, which is developed and mass-produced by the manufacturer; yc j YT represents the overdue unit penalty cost for order j; jr This indicates the development time of order j within the development team r; PT ij This indicates the batch production time of order j on production line i.

[0103] Note that max{FT j -d j If ,0 can be considered as the overdue time of order j, then max{FT j -d j ,0}·yc j This indicates the potential late payment penalty cost for order j, which is developed and mass-produced by the manufacturer.

[0104] S23, Comparing Outsourcing Benefits (NP) oj and the revenue from self-developed and mass-produced products (NP) aj The size of NP; if NP oj ≥NP aj Order j will be outsourced; otherwise, the manufacturer will develop and mass-produce the product and allocate it to the corresponding R&D team r and production line i.

[0105] S24, Update order j at the earliest start time of development at development team r (min(CS) jr), and the earliest start time min(BT) of batch production at production line i. ij The calculation method is as follows:

[0106]

[0107] Where min is the minimization function; CS jr Indicates the start time of order j within the research and development team r; This indicates the time required to develop the order that precedes order j and is handled by development team r.

[0108] BT ij Indicates the start time of order j in production line i; WT jr This indicates the completion time of order j within the research and development team r; This indicates the time required for the batch production of the order that precedes order j and is handled by production line i.

[0109] S25. Let j = j + 1. If j is less than or equal to the total number of orders n, then go back to S22 to recalculate the outsourcing revenue NP of order j. oj Compared with the revenue from self-developed mass production NP aj Continue until the conditions are met; otherwise, terminate population initialization.

[0110] Corresponding to step S3, a destructive operation is then performed on each initial solution. The outsourcing revenue and the revenue from self-developed mass production are compared again, and multiple new solutions are obtained with the optimization objective of maximizing the manufacturer's total revenue. Specifically, this includes:

[0111] S31, Based on the initial solution x 0 The number of orders for domestically developed and mass-produced products Given the destruction rate θ, generate a corresponding number of position coordinates and remove the initial solution x. 0 The order at the corresponding location and its corresponding R&D team and production line yield an incomplete solution x. ′ And the removed orders are placed into the set δ of the orders to be inserted;

[0112] S32. Randomly select an order j from set δ, remove it from the set, and insert it into x. ′ All possible locations;

[0113] S33. After inserting order j, update the completion time of the manufacturer's research and development batch production for each order, and recalculate the revenue NP of the self-developed batch production for each order. aj If we develop and mass-produce our own products, the NP (NP) revenue will be... aj Lower than the outsourcing revenue NP oj If so, the order processing will be outsourced.

[0114] S34. Calculate the total revenue TP of the manufacturer at different insertion positions, and select the scheme with the largest total revenue TP of the manufacturer as the insertion scheme for order j; the calculation method for the total revenue TP of the manufacturer is as follows:

[0115]

[0116] Among them, X j This is a decision variable used to distinguish whether order j is developed and mass-produced by the manufacturer itself.

[0117] S35. Determine if set δ is empty. If not, proceed to S32 to select a new order j from set δ. ′ Perform the insertion operation until the condition is met; if so, end the reconstruction operation and output the new solution x. 1 .

[0118] Corresponding to step S4, a partial search update is performed on the manufacturer's production orders in the new solution.

[0119] like Figure 2 As shown, the specific steps in this process are as follows:

[0120] For the new solution x 1 The local optimum x is obtained by performing a local search update on the orders for mass production developed by the Chinese manufacturer. 2 Compare the new solution x 1 Fitness and local optimal solution x 2 The fitness value. If x 2 The fitness value is less than x 1 Let x be the fitness value. 1 =x 2 Continue the local search within the current neighborhood structure; if x 2 The fitness value is not less than x 1 Find the fitness value, let ω = ω + 1, and determine whether ω ≤ NS is true. If it is true, repeat the local search update operation in this step; otherwise, go to S5.

[0121] For example, the fitness function described above can be directly calculated using the total revenue TP formula given in formula (5).

[0122] Furthermore, embodiments of the present invention also design the following various neighborhood structures to perform local search update operations:

[0123] Neighborhood Structure 1: Define variable h1, randomly generate an integer in the interval [1,n] and assign it to h1. For the order allocation sequence PS of the order layer, determine whether the orders at the h1th position and the 1st position are both developed and produced by the manufacturer. If so, swap the order codes at the h1th position and the 1st position; otherwise, do not change.

[0124] Neighborhood Structure 2: Define variables h1 and h2. Randomly generate two integers in the interval [1, n], assign them to h1 and h2 such that h1 < h2. For the research team allocation sequence RS in the research layer, check whether the orders at the h1-th and h2-th positions are both for manufacturer research. If so, swap the order codes at the h1-th and h2-th positions; otherwise, keep them unchanged.

[0125] Neighborhood Structure 3: Define variables h1 and h2. Randomly generate two integers in the interval [1, n], assign them to h1 and h2 such that h1 < h2. For the production line allocation sequence BS in the mass production layer, swap the order codes of the orders responsible for mass production by the manufacturer on the left side of the h1-th position and on the right side of the h2-th position.

[0126] Neighborhood Structure 4: Define variables h1, h2, and h3. Randomly generate three integers in the interval [1, b], assign them to h1, h2, and h3 such that h1 < h2 < h3. For the research team allocation sequence RS in the research layer, reverse the order codes of the orders responsible for mass production by the manufacturer on the left side of the h1-th position and on the right side of the h3-th position. For the production line allocation sequence BS in the mass production stage, reverse the order codes of the orders responsible for mass production by the manufacturer between the h1-th and h2-th positions.

[0127] Corresponding to step S5, finally, check whether it is greater than the iteration termination temperature. If so, transfer to S4 to re-execute the local search update operation until the condition is satisfied; otherwise, output the global optimal solution, decode to obtain the set of self-developed mass production orders and their corresponding research teams and production lines, and combine the high-end equipment order information to obtain the set of outsourced orders.

[0128] As Figure 2 shown, in this step, specifically calculate the current temperature T, where and check whether the current temperature T is greater than the iteration termination temperature T f , if so, return to S3; otherwise, output the global optimal solution x best , and for the global optimal solution x best perform decoding to end the algorithm.

[0129] Corresponding to the encoding rule, the embodiment of the present invention designs the following decoding rule:

[0130] For the research team allocation sequence in the research layer, obtain the orders assigned to each research team. When multiple orders are assigned to research team r, compare the research time YT of different orders j in this research team r jr , select the order j with a longer research time to be developed first. If the research times of the orders are the same, then preferentially select the order j with a higher income u j for development;

[0131] For the production line allocation sequence at the batch production level, obtain the orders allocated to each production line; when multiple orders are allocated to production line i, compare the earliest completion time min(WT) of different orders j. jr For each order, the order with the earliest completion time is selected for mass production. If the completion times of orders are the same, the order with the earliest revenue (u) is given priority. j Higher orders will lead to mass production;

[0132] By combining the decoding results from both parts, we can obtain the set of self-developed mass production orders and the corresponding R&D teams and production lines.

[0133] Therefore, this step can obtain the set of self-developed mass production orders and their corresponding R&D teams and production lines, and combine this with the original high-end equipment order information, i.e., the total order set, while also obtaining the outsourced order set. Thus, this embodiment of the invention completes the entire process of the collaborative scheduling method for high-end equipment R&D and mass production based on production outsourcing.

[0134] Example 2:

[0135] This invention provides a collaborative scheduling system for the development and mass production of high-end equipment based on production outsourcing, comprising:

[0136] The acquisition module is used to acquire information on high-end equipment orders, as well as data on manufacturers' R&D teams and production lines.

[0137] An initialization module is used to generate an initialization population including multiple initial solutions by adopting a three-segment coding rule based on the order layer, R&D layer, and mass production layer; wherein, by comparing the outsourcing revenue of an order with the self-developed mass production revenue, the order allocation sequence of the order layer of the initial solution is generated.

[0138] The comparison module is used to destroy each initial solution, and then compare the outsourcing revenue of the order with the self-developed mass production revenue, and obtain multiple new solutions with the optimization objective of maximizing the manufacturer's total revenue;

[0139] The update module is used to perform a partial search and update of the manufacturer's production orders in the new solution;

[0140] The output module is used to determine whether the temperature is greater than the iteration termination temperature. If so, the local search update operation is re-executed until the condition is met; otherwise, the global optimal solution is output, the self-developed batch production order set and its corresponding R&D team and production line are decoded and obtained, and the outsourced order set is obtained by combining the high-end equipment order information.

[0141] Example 3:

[0142] This invention provides a storage medium storing a computer program for collaborative scheduling of high-end equipment development and mass production based on production outsourcing, wherein the computer program causes a computer to execute the collaborative scheduling method for high-end equipment development and mass production as described in Embodiment 1.

[0143] Example 4:

[0144] This invention provides an electronic device, comprising:

[0145] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing the collaborative scheduling method for high-end equipment development and mass production as described in Example 1.

[0146] It is understood that the high-end equipment development and mass production collaborative scheduling system, storage medium and electronic device based on production outsourcing provided in the embodiments of the present invention correspond to the high-end equipment development and mass production collaborative scheduling method based on production outsourcing provided in the embodiments of the present invention. The explanation, examples and beneficial effects of the relevant contents can be referred to the corresponding parts of the high-end equipment development and mass production collaborative scheduling method, which will not be repeated here.

[0147] In summary, compared with existing technologies, it has the following beneficial effects:

[0148] 1. The embodiments of the present invention coordinate the two stages of research and development and mass production of high-end equipment manufacturers. The fitness function is set to comprehensively consider the research and development, mass production and overdue costs, so that the scheduling result conforms to the global optimum.

[0149] 2. The implementation of this invention focuses on the scenario where manufacturers outsource the production of orders, making the solution analysis consistent with the actual situation and better guiding the research and mass production work of high-end equipment manufacturers.

[0150] 3. This invention improves the iterative greedy algorithm by using a more efficient initialization method to accelerate the algorithm's iteration speed when finding the initial solution. In the cycle-breaking phase, a new breaking method is designed based on the breaking rate, increasing the diversity of solutions and expanding the algorithm's search range.

[0151] 4. The present invention designs four neighborhood structures based on the actual problem background, including changes in the order allocation method and adjustments and transformations in the order of the corresponding research and development teams and production lines. These can improve the quality of the solution under the condition of satisfying the problem constraints, thereby improving the algorithm's ability to search for the global optimal solution.

[0152] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0153] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for collaborative scheduling of high-end equipment development and mass production based on production outsourcing, characterized in that, Including: Obtain the order information of high-end equipment, the R & D team and production line data of the manufacturer; Adopt a three-stage coding rule based on the order layer - R & D layer - mass production layer to generate an initial population including multiple initial solutions; among them, by comparing the outsourcing revenue of the order with the mass production revenue of self-developed production, generate the order allocation sequence of the order layer of the initial solution; Perform a destruction operation on each initial solution, and again by comparing the outsourcing revenue of the order with the mass production revenue of self-developed production, and with the goal of maximizing the total revenue of the manufacturer, obtain multiple new solutions; Perform local search and update on the orders of the manufacturer's mass production in the new solutions; Judge whether it is greater than the iteration termination temperature. If so, re-execute the local search and update operation until the condition is met; otherwise, output the global optimal solution, decode to obtain the set of self-developed mass production orders and their corresponding R & D teams and production lines, and combine the high-end equipment order information to obtain the set of outsourcing orders; Among them, the adoption of a three-stage coding rule based on the order layer - R & D layer - mass production layer to generate an initial population including multiple initial solutions includes: Based on the high-end equipment order information, sequentially generate the order allocation sequence of the order layer, and initialize the order index j of the order allocation sequence to 1; Calculate the outsourcing revenue NP for order j oj Compared with the revenue from self-developed mass production NP aj ; Comparing outsourcing benefits NP oj and the revenue from self-developed and mass-produced products (NP) aj The size of NP; if NP oj ≥NP aj Order j will be outsourced; otherwise, the manufacturer will develop and mass-produce the product and allocate it to the corresponding R&D team r and production line i. Update order j at the earliest start time (min) of development team r. jr ), and the earliest start time min(BT) of batch production at production line i. ij ); Let j = j + 1. If j is less than or equal to the total number of orders n, then recalculate the outsourcing revenue NP for order j. oj Compared with the revenue from self-developed mass production NP aj Continue until the conditions are met; otherwise, terminate population initialization. Outsourcing revenue NP for order j oj Compared with the revenue from self-developed mass production NP aj The calculation method is as follows: NP oj =u j -oc j (1) NP aj =u j -max{FT j -d j ,0}·yc j -PC r ·YT jr -PC i ·PT ij (2) Among them, u j Represents the revenue of order j; oc j This represents the outsourcing cost for order j; Max is the maximization function; FT j This indicates the completion time for order j, which is developed and mass-produced by the manufacturer; d j This indicates the delivery date for order j; yc j PC represents the overdue unit penalty cost for order j. r YT represents the research and development cost of the research and development team r; jr Indicates the development time of order h within the development team r; PC i Batch production cost of production line i; PT ij This indicates the batch production time of order h on production line i; The destruction operation on each initial solution, and again by comparing the outsourcing revenue of the order with the mass production revenue of self-developed production, and with the goal of maximizing the total revenue of the manufacturer, obtaining multiple new solutions includes: Based on the initial solution x 0 The number of orders for domestically developed and mass-produced products Given the destruction rate θ, generate a corresponding number of position coordinates and remove the initial solution x. 0 The order at the corresponding location and its corresponding R&D team and production line yield an incomplete solution x. ′ And the removed orders are placed into the set δ of the orders to be inserted; Randomly select an order j from set δ, remove it from the set, and insert it into c. ′ All possible locations; After updating order j, the completion time of the manufacturer's research and development batch production for each order is updated, and the revenue NP of the self-developed batch production for each order is recalculated. aj If we develop and mass-produce our own products, the NP (NP) revenue will be... aj Lower than the outsourcing revenue NP oj If so, the order processing will be outsourced. Calculate the total revenue TP of the manufacturer at different insertion positions, and select the plan with the largest total revenue TP of the manufacturer as the order j insertion plan; the calculation method of the total revenue TP of the manufacturer is as follows: Where s is the total number of research and development teams; m is the total number of production lines; X j For decision variables; Determine if set δ is empty; if not, select a new order j from set δ. ′ Perform the insertion operation until the condition is met; if so, end the reconstruction operation and output the new solution x. 1 .

2. The method for coordinated scheduling of high-end equipment development and mass production as described in claim 1, characterized in that, The earliest start time (min) of order j at research team r. jr ) and the earliest start time min (BT) of batch production at production line i. ij The calculation method for ) is as follows: Where min is the minimization function; CS jr Indicates the start time of order j within the research and development team r; This indicates the time required to develop the order that precedes order j and is handled by development team r. BT ij Indicates the start time of order j in production line i; WT jr This indicates the completion time of order j within the research and development team r; This indicates the time required for the batch production of the order that precedes order j and is handled by production line i.

3. The method for coordinated scheduling of high-end equipment development and mass production as described in claim 1, characterized in that, Design the following multiple neighborhood structures to perform local search and update operations: Neighborhood structure 1: Define a variable h1, randomly generate an integer in the interval [1, n] and assign it to h1. For the order allocation sequence PS of the order layer, judge whether the orders at the h1-th position and the 1st position are both mass-produced by the manufacturer. If so, swap the order codes at the h1-th position and the 1st position, otherwise do not change; Neighborhood structure 2: Define variables h1 and h2, randomly generate two integers in the interval [1, n], assign them to h1 and h2, such that h1 < h2. For the R & D team allocation sequence RS of the R & D layer, judge whether the orders at the h1-th position and the h2-th position are both developed by the manufacturer. If so, swap the order codes at the h1-th position and the h2-th position, otherwise do not change; Neighborhood structure 3: Define variables h1 and h2, randomly generate two integers in the interval [1, n], assign them to h1 and h2, such that h1 < h2. For the production line allocation sequence BS of the mass production layer, swap the order codes of the orders mass-produced by the manufacturer on the left side of the h1-th position and on the right side of the h2-th position; Neighborhood structure 4: Define variables h1, h2, and h3. Randomly generate three integers in the interval [1, n] and assign them to h1, h2, and h3 such that h1 < h2 < h3. For the research and development team assignment sequence RS in the research and development layer, reverse the order of the order codes for mass production by the manufacturer on the left side of the h1-th position and on the right side of the h3-th position. For the production line assignment sequence BS in the mass production stage, reverse the order of the order codes for mass production by the manufacturer between the h1-th position and the h2-th position.

4. The method for coordinated scheduling of high-end equipment development and mass production as described in claim 1, characterized in that, The decoding rules include: For the research and development team allocation sequence at the research and development level, obtain the orders allocated to each research and development team; when multiple orders are allocated to research and development team r, compare the research and development time YT of different orders j within this research and development team r. jr Prioritize orders with longer development times (j). If the development times for orders are the same, prioritize orders with higher revenue (u). j Higher orders will drive research and development; For the production line allocation sequence at the batch production level, obtain the orders allocated to each production line; when multiple orders are allocated to production line i, compare the earliest completion time min(WT) of different orders j. jr For each order, the order with the earliest completion time is selected for mass production. If the completion times of orders are the same, the order with the earliest revenue (u) is given priority. j Higher orders will lead to mass production; Combining the decoding results of the two parts to obtain the set of orders for self-developed mass production and the corresponding research and development teams and production lines.

5. A high-end equipment development and mass production collaborative scheduling system based on production outsourcing, characterized in that, It includes: An acquisition module for acquiring high-end equipment order information, data on the research and development teams and production lines of the manufacturer. An initialization module for generating an initial population including multiple initial solutions using a three-stage coding rule based on the order layer - research and development layer - mass production layer. Among them, by comparing the outsourcing revenue and the self-developed mass production revenue of the orders, generate the order assignment sequence of the order layer of the initial solution. A comparison module for performing a destruction operation on each initial solution, and again by comparing the outsourcing revenue and the self-developed mass production revenue of the orders, and taking the maximization of the total revenue of the manufacturer as the optimization goal, obtain multiple new solutions. An update module for performing local search and update on the orders for mass production by the manufacturer in the new solutions. An output module for determining whether it is greater than the iteration termination temperature. If so, re-execute the local search and update operation until the condition is satisfied. Otherwise, output the global optimal solution, decode to obtain the set of orders for self-developed mass production and their corresponding research and development teams and production lines, and combine the high-end equipment order information to obtain the set of outsourcing orders. Among them, the use of a three-stage coding rule based on the order layer - research and development layer - mass production layer to generate an initial population including multiple initial solutions includes: Based on the high-end equipment order information, sequentially generate the order assignment sequence of the order layer, and initialize the order index j = 1 of the order assignment sequence. Calculate the outsourcing revenue NP for order j oj Compared with the revenue from self-developed mass production NP aj ; Comparing outsourcing benefits NP oj and the revenue from self-developed and mass-produced products (NP) aj The size of NP; if NP oj ≥NP aj Order j will be outsourced; otherwise, the manufacturer will develop and mass-produce the product and allocate it to the corresponding R&D team r and production line i. Update order j at the earliest start time (min) of development team r. jr ), and the earliest start time min(BT) of batch production at production line i. ij ); Let j = j + 1. If j is less than or equal to the total number of orders n, then recalculate the outsourcing revenue NP for order j. oj Compared with the revenue from self-developed mass production NP aj Continue until the conditions are met; otherwise, terminate population initialization. Outsourcing revenue NP for order j oj Compared with the revenue from self-developed mass production NP aj The calculation method is as follows: NP oj =u j -oc j (1) NP aj =u j -max{FT j -d j ,0}·yc j -PC r ·YT jr -PC i ·PT ij (2) Among them, u j Represents the revenue of order j; oc j This represents the outsourcing cost for order j; Max is the maximization function; FT j This indicates the completion time for order j, which is developed and mass-produced by the manufacturer; d j This indicates the delivery date for order j; yc j PC represents the overdue unit penalty cost for order j. r YT represents the research and development cost of the research and development team r; jr PC represents the development time of order j within the development team r. i Batch production cost of production line i; PT ij This indicates the batch production time of order j on production line i; The performing a destruction operation on each initial solution, and again by comparing the outsourcing revenue and the self-developed mass production revenue of the orders, and taking the maximization of the total revenue of the manufacturer as the optimization goal, obtain multiple new solutions includes: Based on the initial solution x 0 The number of orders for domestically developed and mass-produced products Given the destruction rate θ, generate a corresponding number of position coordinates and remove the initial solution x. 0 The order at the corresponding location and its corresponding R&D team and production line yield an incomplete solution x. ′ And the removed orders are placed into the set δ of the orders to be inserted; Randomly select an order j from set δ, remove it from the set, and insert it into x. ′ All possible locations; After updating order j, the completion time of the manufacturer's research and development batch production for each order is updated, and the revenue NP of the self-developed batch production for each order is recalculated. aj If we develop and mass-produce our own products, the NP (NP) revenue will be... aj Lower than the outsourcing revenue NP oj If so, the order processing will be outsourced. Calculate the total revenue TP of the manufacturer at different insertion positions, and select the plan with the largest total revenue TP of the manufacturer as the order j insertion plan. The calculation method of the total revenue TP of the manufacturer is as follows: Where s is the total number of research and development teams; m is the total number of production lines; X j For decision variables; Determine if set δ is empty; if not, select a new order j from set δ. ′ Perform the insertion operation until the condition is met; if so, end the reconstruction operation and output the new solution x. 1 .

6. A storage medium, characterized in that, It stores a computer program for the collaborative scheduling of high-end equipment research and development and mass production based on the production outsourcing situation. Among them, the computer program enables the computer to execute the high-end equipment research and development and mass production collaborative scheduling method according to any one of claims 1 to 4.

7. An electronic device, characterized in that, It includes: One or more processors; A memory; And one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the one or more processors. The programs include those for executing the high-end equipment research and development and mass production collaborative scheduling method according to any one of claims 1 to 4.

Images