Production plan optimization method and device based on integer programming and electronic equipment

Abstract

The invention provides a production plan optimization method and apparatus based on integer programming, and an electronic device. The production plan optimization method based on integer programming comprises the steps of obtaining demand information, production line information and a preset optimization target; according to the demand information, the production line information and the optimization target, an integer programming regulation and control model is constructed, and the integer programming regulation and control model at least comprises a target function and a model constraint condition; and on the basis of the integer programming regulation and control model, production plans of all production line bodies in the production line are solved, and the production data at least comprise the production yield of each production line body and the corresponding product order. According to the invention, the regulation and control accuracy can be improved, and the production efficiency can also be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production management, and particularly relates to a production plan optimization method based on integer programming, a production plan optimization device based on integer programming, and an electronic device. Background Art

[0002] In the manufacturing industry, especially in the process of TV production, the formulation of production plans is a core link to ensure that production meets market demand and optimize resource allocation. In the related art, most production plans adopt the method of manual scheduling. However, this method mainly relies on the personal experience and intuitive judgment of production management personnel. When facing a large amount of data and complex conditions, the scheduling efficiency and accuracy are often difficult to guarantee. Secondly, when facing market changes and order fluctuations, the flexibility and response speed of manual scheduling are relatively slow, and it is difficult to adjust production plans in real time to meet new production requirements, which will not only affect production efficiency, but may also lead to an increase in production costs and a decrease in customer satisfaction. Summary of the Invention

[0003] The present invention is made based on the inventor's discovery and recognition of the following facts and problems:

[0004] In the process of TV production, when facing a large amount of production data and complex and changeable production conditions, due to the limited speed and accuracy of manual information processing, when the data volume surges or the production conditions become complex, production management personnel may be difficult to make optimal scheduling decisions in a short time, which not only affects the timeliness of production plans, but may also lead to misallocation and waste of production resources.

[0005] Moreover, when dealing with market changes and order fluctuations, factors such as customer demand, order quantity, and raw material supply may change in real time. And manual scheduling often requires a long time to re-evaluate and adjust production plans to meet these new production requirements. This lag may not only lead to a decrease in production efficiency, but may also increase production costs due to the inability to adjust production strategies in time, and even affect the on-time delivery of products, thereby damaging customer satisfaction and the market competitiveness of enterprises.

[0006] Therefore, an embodiment of the present invention provides a production plan optimization method based on integer programming, a production plan optimization device based on integer programming, and an electronic device, which can transform complex scheduling problems into mathematical problems, not only improving the accuracy of regulation, but also improving production efficiency.

[0007] The production plan optimization method based on integer programming provided by the present invention includes the following steps:

[0008] Obtain demand information, production line information, and a preset optimization goal;

[0009] Construct an integer programming regulation model according to the demand information, production line information and the optimization objectives, where the integer programming regulation model includes at least an objective function and model constraint conditions;

[0010] Based on the integer programming regulation model, solve to obtain the production plans of all production line bodies in the production line. The production data includes at least the production output of each production line body and the corresponding product orders.

[0011] In summary, the production plan optimization method based on integer programming provided by the present invention transforms the complex constraints in the production scheduling problem into optimizable problems through the integer programming regulation model, and uses the solver to dynamically adjust the model during the solution process, so that the global optimal or high-quality scheduling plan can be quickly found, ensuring that the scheduling result meets the actual production requirements, and greatly improving the production efficiency and plan accuracy. Moreover, the integer programming regulation model can comprehensively consider multi-objective requirements, balance the conflicts between multiple objectives, and improve the overall production efficiency.

[0012] In some embodiments, the optimization objectives include minimizing the number of production line body changeovers in the production line and minimizing the remaining production capacity of the production line, maximizing the order fulfillment rate at the end of the shift and minimizing the number of batches for order batch production; the objective function includes the following formula:

[0013]

[0014] In the formula: T is the set of all shifts of the production line; t is the production shift number on the production line; L is the set of all production line bodies on the production line; l is the production line body number on the production line; I is the set of all order data in the demand information; i is the product number in the order data; u t,l is the number of production line body changeovers of production line body l in production shift t; c1 is the unit penalty value of the preset number of production line body changeovers; s t,l is the slack variable of production line body l in production shift t; c2 is the unit penalty value of the remaining production capacity; e t,i is the demand gap of order data i after completion in production shift t; is the unit penalty value of the product demand exceeding order data i after completion in production shift t; ε t,i is the Boolean value indicating whether order data i is satisfied after completion in production shift t; is the unit penalty value of order data i not being satisfied after completion in production shift t; y t,l,i is the Boolean value indicating whether production line body l produces the product in order data i in production shift t; c3 is the unit penalty value of the number of batches for order batch production.

[0015] In some embodiments, the steps of obtaining the demand information, production line information and preset optimization objectives include:

[0016] Determine the quantity of product types expected to be produced by each production line body in each production shift according to the production line information;

[0017] Calculate the number of production changeovers of the production line body in each production shift according to the quantity of product types, where the number of production changeovers is equal to the quantity of product types minus 1.

[0018] In some embodiments, the model constraint conditions further include production capacity constraint conditions. The steps of solving for the production plans of all production line bodies in the production line based on the integer programming regulation model include:

[0019] Obtain the attendance working hours of each production line body in each production shift, the unit consumption time of the produced products, the number of production changeovers of each production line body in each production shift, and the product output expected to be produced by each production line body in each production shift;

[0020] Process the unit consumption time, the number of production changeovers, and the product output according to a preset production capacity operation model, and calculate the total production time of the production line body in each production shift;

[0021] Judge whether the total production time is less than or equal to the attendance working hours;

[0022] Until the total production time is equal to the attendance working hours, obtain the product output that meets the production capacity constraint conditions and the slack variables of each production line body in each production shift.

[0023] In some embodiments, the model constraint conditions include product satisfaction rate constraint conditions. The steps of solving for the production plans of all production line bodies in the production line based on the integer programming regulation model include:

[0024] Obtain the product demand quantity in each order data in the demand information and the product output expected to be produced by all production line bodies after each production shift;

[0025] Calculate the demand gap quantity of the order data after each production shift according to the product demand quantity and the output;

[0026] Judge whether the demand gap quantity is less than or equal to zero;

[0027] If the demand gap is greater than zero, it is determined that the product demand is not met after the production shift is completed, and the Boolean value indicating that each order data is not met after each production shift is 1; if the demand gap is less than or equal to zero, it is determined that the product demand is met after the production shift is completed, and the product output that the production line is expected to produce in each production shift meeting the product satisfaction rate constraint condition is obtained, and the Boolean value indicating that each order data is not met after each production shift is 0.

[0028] In some embodiments, the steps of constructing an integer programming control model according to the demand information, production line information, and the optimization goal include:

[0029] Obtain each order data in the demand information and the priority corresponding to the order data, where the demand information contains at least one order data;

[0030] According to the priority of the order data, determine the demand penalty parameter corresponding to the order data, where the demand penalty parameter of the order data is proportional to the priority of the order data, and the demand penalty parameter includes the unit penalty value for each order data exceeding the product demand after each production shift is completed and the unit penalty value for the product demand of each order data not being met after each production shift is completed.

[0031] In some embodiments, the steps of constructing an integer programming control model according to the demand information, production line information, and the optimization goal further include:

[0032] According to the production line information, determine the product type data that each production line is expected to produce in each production shift;

[0033] According to the product type data, determine whether production line l produces the product in order data i in production shift t;

[0034] If production line l produces the product in order data i in production shift t, then the Boolean value y t,l,i is equal to 1;

[0035] If production line l does not produce the product in order data i in production shift t, then the Boolean value y t,l,i is equal to 0;

[0036] Accumulate the Boolean value y t,l,i to obtain the number of production batches in which order i is batched.

[0037] In some embodiments, the production lines include T lines and C lines, the model constraint conditions at least include docking line matching constraint conditions, and the steps of solving the production plans of all production lines in the production line based on the integer programming control model include:

[0038] Obtain the product output expected to be produced by the T line in each production shift and the module output expected to be produced by the C line in each production shift, where the module matches the product;

[0039] Determine whether the product output expected to be produced by the T line in the current production shift is the same as the module output expected to be produced by the C line in the corresponding production shift;

[0040] Until the product output expected to be produced by the T line in the current production shift is the same as the module output expected to be produced by the C line in the corresponding production shift, the production output that meets the docking line matching constraint condition and the corresponding product order are obtained.

[0041] In some embodiments, the production line includes a T line and a C line, the model constraint condition at least includes a non-docking line matching constraint condition, and the step of solving for the production plans of all production lines in the production line based on the integer programming regulation model includes:

[0042] Obtain the product output expected to be produced by the T line in each production shift and the module output expected to be produced by the C line in each production shift, where the module matches the product;

[0043] Calculate the output difference between the product output expected to be produced by the T line in the current production shift and the module output expected to be produced by the C line in the corresponding production shift according to the product output expected to be produced by the T line in each production shift and the module output expected to be produced by the C line in each production shift;

[0044] Determine whether the module output produced by the C line in the previous production shift is greater than or equal to the output difference;

[0045] Until the module output produced by the C line in the previous production shift is greater than or equal to the output difference, the production output that meets the non-docking line matching constraint condition and the corresponding product order are obtained.

[0046] In addition, the production plan optimization device based on integer programming provided by the present invention includes:

[0047] An acquisition module, which is used to acquire demand information, production line information, and a preset optimization target;

[0048] A model construction module, which is used to construct an integer programming regulation model according to the demand information, production line information, and the optimization target, where the integer programming regulation model at least includes an objective function and a model constraint condition;

[0049] A solution module is provided with a solver inside. The solution module is used to solve and obtain the production plans of all production lines in the production line according to the integer programming control model. The production data at least includes the production output of each production line and the corresponding product orders.

[0050] In addition, the electronic device provided in the embodiment of the present invention includes a processor and a memory. The memory stores machine-readable instructions executable by the processor. When the machine-readable instructions are executed by the processor, the steps in the production plan optimization method based on integer programming provided in any of the above embodiments are executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. They are used together with the following specific embodiments to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0052] Figure 1 is a flowchart of the production plan optimization method based on integer programming provided in an embodiment of the present invention.

[0053] Figure 2 is a structural diagram of the production plan optimization device based on integer programming provided in an embodiment of the present invention.

[0054] Figure 3 is a hardware structural diagram of the electronic device provided in an embodiment of the present invention.

[0055] Reference numerals: 100, production plan optimization device based on integer programming; 110, acquisition module; 120, model construction module; 130, solution module;

[0056] 210, processor; 220, memory; 230, communication interface; 240, communication bus. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present invention, but should not be construed as a limitation to the present invention.

[0058] Refer to Figure 1 , which is a flowchart of the production plan optimization method based on integer programming provided in an embodiment of the present invention. For the convenience of explaining the execution process of the steps in the production plan optimization method, the set symbols in the execution process are described:

[0059]

[0060] The parameters are described as follows:

[0061]

[0062]

[0063] In this embodiment, the production plan optimization method based on integer programming includes the following steps:

[0064] S10. Obtain demand information, production line information, and a preset optimization goal.

[0065] Specifically, the demand information may include at least one order data, and each order data contains the product demand quantity of each product. The production line information may include information such as the attendance hours of each production line in each production shift, the unit time consumption of producing products, and the changeover time. The optimization goal may include minimizing the changeover times of the production lines in the production line, minimizing the remaining production capacity of the production lines, maximizing the order fulfillment rate at the end of the shift, and minimizing the number of batches for batch production of orders.

[0066] S20. According to the demand information, production line information, and the optimization goal, construct an integer programming regulation model, and the integer programming regulation model includes at least an objective function and model constraint conditions.

[0067] Specifically, the objective function may include the following formula:

[0068]

[0069] In the formula, the first term is used to calculate the optimization goal of minimizing the changeover times of the production lines in the production line and minimizing the remaining production capacity of the production lines, the second term is used to calculate the optimization goal of maximizing the order fulfillment rate at the end of the shift; the third term is used to calculate the optimization goal of minimizing the number of batches for batch production of orders. That is, this objective function can comprehensively consider multiple optimization goals, balance the conflicts between the optimization goals, and thus can improve the overall production efficiency.

[0070] S30. Based on the integer programming regulation model, solve to obtain the production plans of all production lines in the production line, and the production data includes at least the production output of each production line and the corresponding product orders.

[0071] Specifically, the solver can solve the objective function and constraint conditions in the integer programming regulation model to obtain the production plans of all production lines in the production line.

[0072] In summary, the production plan optimization method based on integer programming provided by the present invention transforms the complex constraints in the production scheduling problem into optimizable problems through an integer programming regulation model, and uses a solver to dynamically adjust the model during the solution process, so as to quickly find a globally optimal or high-quality scheduling plan, ensure that the scheduling result meets the actual production requirements, and greatly improve production efficiency and planning accuracy. Moreover, the integer programming regulation model can comprehensively consider multi-objective requirements, balance the conflicts between multiple objectives, and enhance the overall production benefit.

[0073] In some embodiments, the step of obtaining demand information, production line information, and a preset optimization objective includes:

[0074] According to the production line information, determine the number of product types expected to be produced by each production line body in each production shift;

[0075] According to the number of product types, calculate the number of production line changeovers of the production line body in each production shift, where the number of production line changeovers is equal to the number of product types minus 1.

[0076] Further, the number of production line changeovers can be calculated using the following formula:

[0077]

[0078] Specifically, for each production line body, if the production line body l does not produce or only produces a product with product type number k = 1 in the production shift t, then the number of production line changeovers is 0; otherwise, the number of production line changeovers is equal to the number of product types produced by the production line body l in the production shift t minus 1.

[0079] In some embodiments, the model constraint conditions further include production capacity constraint conditions. The step of obtaining the production plans of all production line bodies in the production line based on the integer programming regulation model includes:

[0080] Obtain the attendance working hours of each production line body in each production shift, the unit consumption time of the produced products, the number of production line changeovers of each production line body in each production shift, and the product output expected to be produced by each production line body in each production shift;

[0081] According to a preset production capacity operation model, process the unit consumption time, the number of production line changeovers, and the product output to calculate the total production time of the production line body in each production shift;

[0082] Judge whether the total production time is less than or equal to the attendance working hours;

[0083] Until the total production time is equal to the attendance working hours, the product output that meets the production capacity constraint conditions and the slack variables of each production line body in each production shift are obtained.

[0084] Further, the production capacity constraint condition may include the following formula:

[0085]

[0086] Specifically, during the production process of order data i, the unit time consumption of producing product i of production line l within production shift t is multiplied by the quantity of product i produced by production line l within production shift t, obtaining the actual production time of production line l within production shift t. Then, the actual production times of all production lines for production order i within all production shifts are accumulated. Next, through the number of production changeovers and the production changeover time (in this application, the production changeover time is set to 0.18), the production changeover time is obtained. Furthermore, the actual production times of all production lines for production order i within all production shifts, the production changeover time, and the slack variable of production line l within production shift t are accumulated to obtain the total production time. The production capacity constraint condition is used to ensure that the total production time is equal to the attendance working hours of production line l within production shift t, avoiding over-arrangement of the production plan; on the other hand, it can also avoid the idleness of resources, resulting in waste of resources such as equipment and workers.

[0087] In some embodiments, the model constraint condition includes a product satisfaction rate constraint condition. The steps of solving for the production plans of all production lines within the production line based on the integer programming regulation model include:

[0088] Obtain the product demand quantity within each order data in the demand information and the product output expected to be produced by all production lines after each production shift;

[0089] Calculate the demand gap quantity of the order data after each production shift according to the product demand quantity and the output;

[0090] Determine whether the demand gap quantity is less than or equal to zero;

[0091] If the demand gap quantity is greater than zero, it is determined that the product demand quantity has not been satisfied after this production shift, and the Boolean value of non-satisfaction of each order data after each production shift is 1; if the demand gap quantity is less than or equal to zero, it is determined that the product demand quantity has been satisfied after this production shift, obtaining the product output expected to be produced by the production lines that meet the product satisfaction rate constraint condition after each production shift, and the Boolean value of non-satisfaction of each order data after each production shift is 0.

[0092] That is to say, the demand gap of the order data is equal to the product demand minus the product output expected to be produced in this production shift, which can directly reflect whether the production plan can meet the customer demand. If the demand gap is greater than zero, it means that after this production shift is completed, the product demand has not been met yet. At this time, the Boolean value of each unmet order data after each production shift is set to 1 to identify this state. On the contrary, if the demand gap is less than or equal to zero, it indicates that after this production shift is completed, the product demand has been met. At this time, the product output expected to be produced by the production line in each production shift meets the product satisfaction rate constraint condition. Therefore, the Boolean value of each unmet order data after each production shift is set to 0.

[0093] Moreover, the present invention uses Boolean values to reflect the satisfaction of order data, and can adjust the production plan of the production line according to the distribution of Boolean values, so as to ensure that the needs of all customers can be met in a timely and effective manner. At the same time, this process also reflects the flexibility and practicality of the integer programming control model in a complex production environment.

[0094] Furthermore, the product satisfaction rate constraint condition may include the following formula:

[0095]

[0096] Specifically, when the order data i is not satisfied after the production shift t is completed, e t,i is a positive value. At this time, ε t,i is equal to 1, which can make demand i *ε t,i ≥e t,i , hold; otherwise, ε t,i is equal to 0. Thus, the satisfaction of the order data can be converted into a Boolean value, which is convenient to use in the solution process of the optimization goal of maximizing the order satisfaction rate at the end of the shift. Furthermore, the production plan of the production line can be adjusted specifically according to the distribution of the Boolean values.

[0097] In some embodiments, the steps of constructing an integer programming control model according to the demand information, production line information, and the optimization goal include:

[0098] Obtain each order data in the demand information and the priority corresponding to the order data, and the demand information contains at least one order data;

[0099] According to the priority of the order data, determine the demand penalty parameter corresponding to the order data, where the demand penalty parameter of the order data is directly proportional to the priority of the order data, and the demand penalty parameter includes the unit penalty value for each order data exceeding the product demand after each production shift and the unit penalty value for the product demand of each order data not being met after each production shift.

[0100] Among them, the unit penalty value for each order data exceeding the product demand after each production shift can prevent resource waste and inventory backlog caused by overproduction. The unit penalty value for the product demand of each order data not being met after each production shift can ensure the timely satisfaction of customer needs and avoid customer loss and reputation loss caused by out-of-stock or delayed delivery.

[0101] According to the production line information, determine the product type data expected to be produced by each production line in each production shift;

[0102] According to the product type data, determine whether production line l produces the product in order data i during production shift t;

[0103] If production line l produces the product in order data i during production shift t, then the Boolean value y t,l,i is equal to 1;

[0104] If production line l does not produce the product in order data i during production shift t, then the Boolean value y t,l,i is equal to 0;

[0105] Accumulate the Boolean value y t,l,i to obtain the number of production batches in which order i is batch-produced.

[0106] That is to say, in this embodiment, by introducing the Boolean value y t,l,i , the number of batches in which the product in order data i is batch-produced can be recorded. Furthermore, in the function for calculating the optimization objective of minimizing the number of batches in which orders are batch-produced, minimizing the number of batches in which all orders are batch-produced can improve the order completion efficiency, avoid situations such as mold change and production change, and improve production efficiency.

[0107] In addition, it should be noted that in the television manufacturing industry, production lines can be divided into two types: module production lines (C lines, Component Production Lines, CPL) and overall production lines (T lines, Total Assembly Lines, TAL). The C lines only produce modules, while the T lines can produce both complete machines and spare parts.

[0108] Among them, the C line body and the T line body form a docking line and a non-docking line through a combination relationship (docking relationship and non-docking relationship). The docking line docks the production module and the whole machine, that is, after the module is produced, the whole machine can be produced without connection. If a C line body and a T line body are a docking line, they can be regarded as one line body. The docking relationship refers to the docking production of the whole machine and the module, and there is no connection time in the process. After the module is produced, the production of the whole machine can be directly arranged. The production rule of the docking line is to produce one product at a time.

[0109] The non-docking line is for separate production of the module and the whole machine by different line bodies. If a C line body and a T line body are a non-docking line, after the C line body finishes production, it takes a certain amount of time to transport the module to the T line body for the production of the whole machine. The non-docking relationship means that the module and the whole machine are produced by separate line bodies. After the module is produced, it needs to wait for 1 shift or 2 shifts before it can be sent to the whole machine workshop for production on the line.

[0110] In some embodiments, the model constraint conditions at least include the docking line matching constraint conditions. The steps of solving the production plans of all production line bodies in the production line based on the integer programming regulation model include:

[0111] Obtain the product output that the T line body is expected to produce in each production shift, and the module output that the C line body is expected to produce in each production shift, where the module matches the product;

[0112] Judge whether the product output that the T line body is expected to produce in the current production shift is the same as the module output that the C line body is expected to produce in the corresponding production shift;

[0113] Until the product output that the T line body is expected to produce in the current production shift is the same as the module output that the C line body is expected to produce in the corresponding production shift, the production output that meets the docking line matching constraint conditions and the corresponding product orders are obtained.

[0114] Further, the docking line matching constraint conditions may include the following formula:

[0115]

[0116] Specifically, in the process of solving the production plan in the production line based on the integer programming regulation model, it is first necessary to obtain the product output that the T line body is expected to produce in each production shift. The T line body represents the part of the production line responsible for the final assembly or processing of the product, and its output directly determines the number of products that can meet customer needs. At the same time, it is also necessary to obtain the module output that the C line body is expected to produce in each production shift. The C line body is responsible for producing the modules or components required for the product, and these modules must match the products produced by the T line body to ensure the integrity and functionality of the final product.

[0117] Next, it is determined whether the product output expected to be produced by the T line in the current production shift is the same as the module output expected to be produced by the C line in the corresponding production shift. If the two are equal, it means that the production plan for this production shift meets the docking line matching constraint conditions; if not, then the production plan needs to be adjusted to ensure that the two can match. This comparison process is crucial because any mismatch in output may lead to problems such as production line stoppages, product shortages, or excess inventory.

[0118] In some embodiments, the model constraint conditions at least include non-docking line matching constraint conditions. The steps of solving the production plans of all production lines in the production line based on the integer programming regulation model include:

[0119] Obtain the product output expected to be produced by the T line in each production shift and the module output expected to be produced by the C line in each production shift, where the module matches the product;

[0120] According to the product output expected to be produced by the T line in each production shift and the module output expected to be produced by the C line in each production shift, calculate the output difference between the product output expected to be produced by the T line in the current production shift and the module output expected to be produced by the C line in the corresponding production shift;

[0121] Determine whether the module output produced by the C line in the previous production shift is greater than or equal to the output difference;

[0122] Until the module output produced by the C line in the previous production shift is greater than or equal to the output difference, the production output that meets the non-docking line matching constraint conditions and the corresponding product orders are obtained.

[0123] Furthermore, the non-docking line matching constraint conditions may include the following formula:

[0124]

[0125] Specifically, in the process of solving the production plans of all production lines in the production line based on the integer programming regulation model, it is first necessary to obtain the product output expected to be produced by the T line in each production shift. The T line, as the part of the production line responsible for the final product assembly or the core processing link, its output planning is directly related to the product delivery ability and customer satisfaction. At the same time, it is also necessary to obtain the module output expected to be produced by the C line in each production shift. The C line is responsible for producing the modules or components necessary for the final product, and there is a clear matching relationship between these modules and the products produced by the T line.

[0126] After obtaining these basic data, according to the product output expected to be produced by the T line in each production shift and the module output expected to be produced by the C line in each production shift, the output difference between the product output expected to be produced by the T line in the current production shift and the module output expected to be produced by the C line in the corresponding production shift is calculated. This difference reflects the difference between the number of modules required by the T line and the actual number of modules provided by the C line in the current production shift.

[0127] Because in a non-docking line production environment, there may be time lags or inventory buffers between production lines, such that the modules required by the T line in the current production shift may partially or fully come from the output of the C line in the previous or several previous production shifts. Therefore, it is determined whether the module output produced by the C line in the previous production shift is greater than or equal to the above-calculated output difference.

[0128] If the module output produced by the C line in the previous production shift is indeed greater than or equal to the output difference, it means that even with time lags or inventory buffers, the output of the C line is sufficient to meet the demand of the T line in the current production shift, thus meeting the non-docking line matching constraint conditions. On the contrary, if the output of the C line is not sufficient to make up for the output difference, then the production plan needs to be adjusted until the module output produced by the C line in the previous production shift is greater than or equal to the output difference, and the production output and corresponding product orders that meet the non-docking line matching constraint conditions can be obtained. These orders will be produced according to the adjusted production plan, ensuring the continuity of production and the integrity of products even between production lines that are not fully synchronized.

[0129] In some embodiments, the model constraint conditions at least include reserve period output constraint conditions. The steps of solving for the production plans of all production lines in the production line based on the integer programming regulation model include:

[0130] Obtain the number of modules produced by the production lines in the production line during the reserve period and the number of products expected to be produced by the production lines in the production line that are expected to rely on the reserve period modules;

[0131] Until the number of products expected to be produced by the production lines in the production line that are expected to rely on the reserve period modules is less than or equal to the number of modules produced by the production lines in the production line during the reserve period, output the number of products produced by the production lines in the production line that rely on the reserve period modules and meet the reserve period output constraint conditions.

[0132] Further, the reserve period output constraint conditions may include the following formula:

[0133]

[0134] Specifically, in the process of solving the production plans of all production lines in the production line based on the integer programming control model, it is first necessary to obtain the number of modules produced by the production lines in the production line during the reserve period. The reserve period refers to a period of time during which modules are produced and stored in advance to meet future production demands. At the same time, it is also necessary to obtain the number of products that the production lines in the production line are expected to produce relying on the modules in the reserve period, so as to reflect the number of products that the production lines plan to produce using the modules in the reserve period within a certain period in the future.

[0135] Next, it is necessary to ensure that the number of products that the production lines in the production line are expected to produce relying on the modules in the reserve period is less than or equal to the number of modules produced by the production lines in the production line during the reserve period. If the number of products expected to be produced relying on the modules in the reserve period exceeds the number of modules produced during the reserve period, then the production plan needs to be adjusted. Through continuous adjustment and optimization, until the number of products that the production lines in the production line are expected to produce relying on the modules in the reserve period is less than or equal to the number of modules produced by the production lines in the production line during the reserve period, the number of products that the production lines in the production line rely on the modules in the reserve period to produce can be output, meeting the production quantity constraint conditions of the reserve period. This result not only ensures the feasibility of the production plan, but also maximizes the utilization efficiency of inventory resources and reduces the risk of production interruption.

[0136] In some embodiments, the model constraint conditions further include production product constraint conditions, and the production product constraint conditions include the following formula:

[0137]

[0138] Specifically, the production product constraint conditions determine whether production line l produces the product of order i in production shift t by determining whether production shift t produces the product of order i. When production line l produces the product within order data i in production shift t, the Boolean value y t,l,i is equal to 1; when production line l does not produce the product within order data i in production shift t, the Boolean value y t,l,i is equal to 0.

[0139] Furthermore, when production line l produces the product of order i in production shift t, the output of the product of order i that production line l does not rely on the initial reserve of modules to produce in production shift t and the output of the product of order i that production line l relies on the initial reserve of modules to produce in production shift t should be less than or equal to the demand for the product within order i, thus avoiding excessive waste of production resources.

[0140] In some embodiments, the model constraint conditions further include product type constraint conditions, and the product type constraint conditions include the following formula:

[0141]

[0142] Specifically, product type k can have multiple products, and there is no mold change situation among the multiple products of product type k. The product type constraint condition is obtained by counting the products produced by production line l during production shift t. If the products belong to the same product type, then z t,l,k is recorded as 1; otherwise, z t,l,k is recorded as 0. In this way, the number of products of product type k produced by production line l during production shift t is counted, so as to centrally process the multiple products of product type k on the same production line, avoid the occurrence of production conversion and mold change situations, and improve production efficiency.

[0143] In addition, it should be clear that Figure 1 the flow diagram of the production plan optimization method based on integer programming shown in is only shown based on the logical order provided by one of the numerous embodiments of the present invention. This order is intended to clearly illustrate the execution process of the method in a specific application scenario. However, in actual applications, the flexibility and scalability of the present invention allow technicians to adjust the order of the above steps according to different actual requirements and environmental conditions. For example, there are parallel or juxtaposed relationships between some steps, which will not be elaborated here one by one.

[0144] Refer to Figure 2 , which is a schematic structural diagram of a production plan optimization device 100 based on integer programming provided by an embodiment of the present invention. The production plan optimization device 100 based on integer programming includes an acquisition module 110, a model construction module 120, and a solution module 130. The above modules are electrically connected to each other to realize the transmission and reception of information. Of course, in some embodiments, the acquisition module 110, the model construction module 120, and the solution module 130 can also be integrated into one body, making the overall structure more compact.

[0145] Among them, the acquisition module 110 is used to acquire demand information, production line information, and a preset optimization goal; the model construction module 120 is used to construct an integer programming regulation model according to the demand information, production line information, and the optimization goal. The integer programming regulation model at least includes an objective function and model constraint conditions; a solver is provided in the solution module 130, and the solution module is used to solve the production plan of all production lines in the production line according to the integer programming regulation model. The production data at least includes the production output of each production line and the corresponding product order.

[0146] It should be noted that the production plan optimization device based on integer programming provided in the embodiments of the present application has the same implementation principle and technical effects as those in the foregoing embodiments of the production plan optimization method based on integer programming. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing embodiments of the production plan optimization method based on integer programming.

[0147] Reference Figure 3 , which is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. The electronic device provided in this embodiment includes a processor 210 and a memory 220. The memory 220 stores machine-readable instructions executable by the processor 210. When the machine-readable instructions are executed by the processor 210, the steps in the production plan optimization method based on integer programming described in any of the foregoing embodiments are executed. Among them, there is at least one of the processor 210 and the memory 220.

[0148] In this embodiment, the electronic device further includes a communication interface 230 and a communication bus 240. Among them, the processor 210, the memory 220, and the communication interface 230 are connected to each other through the communication bus 240. The communication bus 240 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 3 only a thick line is used to represent the communication bus 240 in the figure, but it does not mean that there is only one communication bus 240 or one type of communication bus 240. The processor 210 can also be called a controller, and there is no limitation on the name.

[0149] In the embodiments of the present application, the memory 220 stores instructions executable by at least one processor 210. By executing the instructions stored in the memory 220, at least one processor 210 can execute the steps in the production plan optimization method based on integer programming discussed above. The processor 210 can implement Figure 3 the functions of each module in the device shown in the figure.

[0150] Among them, the processor 210 is the control center of the device. It can connect various parts of the entire control device through various interfaces and lines. By running or executing the instructions stored in the memory 220 and calling the data stored in the memory 220, various functions of the device and process data, so as to monitor the device as a whole.

[0151] In a possible design, the processor 210 may include one or more processing units. The processor 210 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, the operating body interface, application programs, etc., and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor. In some embodiments, the processor 210 and the memory 220 may be implemented on the same chip or separately on independent chips.

[0152] The processor 210 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the production plan optimization method based on integer programming disclosed in combination with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0153] As a non-volatile computer-readable storage medium, the memory 220 can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The memory 220 may include at least one type of storage medium. For example, it may include flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (RAM), a static random access memory (SRAM), a programmable read-only memory (PROM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 220 is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 220 in the embodiments of the present application may also be a circuit or any other device that can implement a storage function, for storing program instructions and / or data.

[0154] By programming the design of the processor 210, the code corresponding to the production plan optimization method based on integer programming introduced in the foregoing embodiments can be solidified into the chip, so that the chip can execute when running Figure 2Steps of the production plan optimization method based on integer programming in the illustrated embodiment. How to design and program the processor 210 is a well-known technology to those skilled in the art and will not be elaborated here.

[0155] The embodiments of the present application also provide a computer-readable storage medium storing computer-executable instructions, which are used to implement the production plan optimization method based on integer programming described in any of the previous embodiments when executed by the processor 210. Therefore, it will not be elaborated here. In addition, the beneficial effects of using the same method will not be described either. For the technical details not disclosed in the embodiments of the computer storage medium involved in the present invention, please refer to the description of the method embodiments of the present invention.

[0156] In some possible implementation manners, each aspect of the production plan optimization method based on integer programming provided by the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a device, the program code is used to cause the control device to execute the steps in the production plan optimization method based on integer programming according to various exemplary embodiments of the present application described above in this specification.

[0157] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0158] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0159] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in one or more blocks and / or steps of the flowchart. Figure 1 one or more blocks and / or steps Figure 1 specified in the flowchart.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in one or more blocks and / or steps of the flowchart. Figure 1 one or more blocks and / or steps Figure 1 specified in the flowchart.

[0161] Additionally, any process or method description in the flowchart or otherwise described herein can be understood to represent code including one or more executable instructions of a module, segment, or portion that implements a custom logic function or process, and the scope of the preferred embodiments of the present invention includes additional implementations in which functions may be executed out of the order shown or discussed, including in a substantially simultaneous manner or in reverse order depending on the functions involved, as would be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0162] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and those of ordinary skill in the art may make changes, modifications, substitutions, and variations within the scope of the present invention.

Claims

1. A production plan optimization method based on integer programming, characterized in that, It includes the following steps: Obtain demand information, production line information, and a preset optimization goal; Construct an integer programming regulation model according to the demand information, production line information, and the optimization goal, where the integer programming regulation model at least includes an objective function and model constraint conditions; Based on the integer programming regulation model, solve to obtain the production plans of all production line bodies in the production line, and the production data at least includes the production output of each production line body and the corresponding product orders.

2. The production plan optimization method based on integer programming according to claim 1, wherein, The optimization goal includes minimizing the number of production line body changeovers in the production line, minimizing the remaining production capacity of the production lines, maximizing the order fulfillment rate at the end of the shift, and minimizing the number of production batches for order batching; the objective function includes the following formula: Where: T is the set of all shifts on the production line; t is the production shift number on the production line; L is the set of all production line bodies on the production line; l is the production line body number on the production line; I is the set of all order data in the demand information; i is the product number in the order data; u t,l is the number of production changeovers of production line body l in production shift t; c1 is the unit penalty value of the preset number of production changeovers; s t,l is the slack variable of production line body l in production shift t; c2 is the unit penalty value of the preset remaining production capacity; e t,i is the demand gap of order data i after completion in production shift t; is the unit penalty value for exceeding the product demand of order data i after completion in production shift t; ε t,i A Boolean value indicating whether order data i is satisfied after production shift t is completed; The unit penalty value when order data i is not satisfied after production shift t is completed; y t,l,i A Boolean value indicating whether production line l produces the products within order data i during production shift t; c3 is the unit penalty value for the number of batches in batch production of orders.

3. The production plan optimization method based on integer programming according to claim 2, characterized in that The step of obtaining demand information, production line information, and a preset optimization goal includes: According to the production line information, determine the number of product types expected to be produced by each production line body in each production shift; According to the number of product types, calculate the number of production line body changeovers of the production line body in each production shift, where the number of production line body changeovers is equal to the number of product types minus 1.

4. The production plan optimization method based on integer programming according to claim 2, characterized in that The model constraint conditions further include production capacity constraint conditions. The step of, based on the integer programming regulation model, solving to obtain the production plans of all production line bodies in the production line includes: Obtain the attendance working hours of each production line body in each production shift, the unit time consumption of the produced products, the number of production line body changeovers of each production line body in each production shift, and the product output expected to be produced by each production line body in each production shift; According to a preset production capacity operation model, process the unit time consumption, the number of production line body changeovers, and the product output, and calculate the total production time of the production line body in each production shift; Judge whether the total production time is less than or equal to the attendance working hours; Until the total production time is equal to the attendance working hours, obtain the product output that meets the production capacity constraint conditions and the slack variables of each production line body in each production shift.

5. The production plan optimization method based on integer programming according to claim 2, wherein The model constraint conditions include product fulfillment rate constraint conditions. The step of, based on the integer programming regulation model, solving to obtain the production plans of all production line bodies in the production line includes: Obtain the product demand quantity in each order data in the demand information and the product output expected to be produced by all production line bodies after each production shift; According to the product demand quantity and the output, calculate the demand gap quantity of the order data after each production shift; Judge whether the demand gap quantity is less than or equal to zero; If the demand gap quantity is greater than zero, it is determined that the product demand quantity is not met after this production shift, and the Boolean value of non - fulfillment of each order data after each production shift is 1; if the demand gap quantity is less than or equal to zero, it is determined that the product demand quantity is met after this production shift, obtain the product output expected to be produced by the production line body that meets the product fulfillment rate constraint conditions after each production shift, and the Boolean value of non - fulfillment of each order data after each production shift is 0.

6. The production plan optimization method based on integer programming according to claim 2, wherein The step of constructing an integer programming regulation model according to the demand information, production line information, and the optimization goal includes: Obtain each order data in the demand information and the priority corresponding to the order data, where there is at least one order data in the demand information; According to the priority of the order data, determine the demand penalty parameter corresponding to the order data, where the demand penalty parameter of the order data is directly proportional to the priority of the order data, and the demand penalty parameter includes the unit penalty value for each order data exceeding the product demand after each production shift is completed and the unit penalty value for each order data where the product demand is not met after each production shift is completed; And / or, the step of constructing an integer programming regulation model according to the demand information, production line information and the optimization target further includes: According to the production line information, determine the product type data expected to be produced by each production line body in each production shift; According to the product type data, determine whether the production line body l produces the product in the order data i in the production shift t; If production line l produces the product within production order data i during production shift t, then the Boolean value y t,l,i equals 1; If the production line l does not produce the product within the order data i during the production shift t, then the boolean value y t,l,i is equal to 0; Accumulate the Boolean value y t,l,i , to obtain the number of production batches in which order i is batch-produced.

7. The production plan optimization method based on integer programming according to claim 1, characterized in that The production line body includes a T line body and a C line body, and the model constraint conditions at least include a docking line matching constraint condition. The step of solving the production plans of all production line bodies in the production line based on the integer programming regulation model includes: Obtain the product output expected to be produced by the T line body in each production shift and the module output expected to be produced by the C line body in each production shift, where the module matches the product; Determine whether the product output expected to be produced by the T line body in the current production shift is the same as the module output expected to be produced by the C line body in the corresponding production shift; Until the product output expected to be produced by the T line body in the current production shift is the same as the module output expected to be produced by the C line body in the corresponding production shift, the production output and the corresponding product order that meet the docking line matching constraint condition are obtained.

8. The production plan optimization method based on integer programming according to claim 1, characterized in that The production line body includes a T line body and a C line body, and the model constraint conditions at least include a non-docking line matching constraint condition. The step of solving the production plans of all production line bodies in the production line based on the integer programming regulation model includes: Obtain the product output expected to be produced by the T line body in each production shift and the module output expected to be produced by the C line body in each production shift, where the module matches the product; According to the product output expected to be produced by the T line body in each production shift and the module output expected to be produced by the C line body in each production shift, calculate the output difference between the product output expected to be produced by the T line body in the current production shift and the module output expected to be produced by the C line body in the corresponding production shift; Determine whether the module output produced by the C line body in the previous production shift is greater than or equal to the output difference; Until the module output produced by the C line body in the previous production shift is greater than or equal to the output difference, the production output and the corresponding product order that meet the non-docking line matching constraint condition are obtained.

9. An optimization device for production planning based on integer programming, characterized in that, Include: An acquisition module, where the acquisition module is used to acquire demand information, production line information and a preset optimization target; A model construction module, which is used to construct an integer programming regulation model according to demand information, production line information and the optimization objective, and the integer programming regulation model at least includes an objective function and model constraint conditions; A solving module, which is provided with a solver, and is used to solve the production plans of all production lines in the production line according to the integer programming regulation model, and the production data at least includes the production output of each production line and the corresponding product order.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the steps in the production plan optimization method based on integer programming according to any one of claims 1-8 are executed.

Images