Production plan generation method and device, electronic equipment and storage medium

Abstract

The invention discloses a production plan generation method and device, electronic equipment and a storage medium, and relates to the technical field of product manufacturing. The method comprises the steps that decision information corresponding to a production plan is acquired, and the decision information comprises order information, production line information, product information and production equipment information; determining a constraint condition and a plan target corresponding to the production plan based on the decision information; wherein the constraint condition represents a limiting condition which must be followed by the production plan; and on the basis of the constraint condition and the planning target, a production plan is generated through a hybrid rectification planning MIP model, and the production plan comprises the number and sequence of products produced by each production line every day. Thus, by means of the mixed integer programming model and the optimization algorithm, scientization, automation and intelligentization of the production plan are achieved, the accuracy and the resource utilization rate of the production plan are improved, the flexibility and adaptability of the production plan are enhanced, and the production efficiency and the market competitiveness of enterprises are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of production and manufacturing technology, and in particular to a production plan generation method, device, electronic device and storage medium. Background Art

[0002] In modern manufacturing, factories are faced with large-scale daily production planning problems with multiple product types and multiple production lines. How to reasonably allocate a large number of orders to different production lines, complete production tasks as quickly as possible, and meet customer delivery needs is the core challenge for factories.

[0003] In related technologies, the formulation of production plans mainly relies on the professional experience and judgment of on-site planners. With the on-site planners' understanding of historical production scheduling data and production processes, they comprehensively evaluate the production line's capacity and break down orders in detail to obtain relevant production plans.

[0004] However, the above production plan formulation scheme relies too much on the experience and subjective judgment of on-site planners and lacks unified standards. When dealing with complex large-scale orders and application scenarios with multiple production lines, the planning effect is difficult to guarantee. Summary of the invention

[0005] The embodiments of the present application provide a production plan generation method, device, electronic device and storage medium to solve the problem that the existing production plan formulation method is difficult to ensure the planning effect when dealing with complex large-scale orders and application scenarios of multiple production lines.

[0006] On the one hand, an embodiment of the present application provides a production plan generation method, including:

[0007] Obtain decision information corresponding to the production plan, where the decision information includes order information, production line information, product information, and production equipment information;

[0008] Based on the decision information, determine the constraints and planning objectives corresponding to the production plan; where the constraints represent the restrictions that the production plan must follow;

[0009] Based on the constraints and planning objectives, the production plan is generated through the mixed improvement planning MIP model. The production plan includes the quantity and sequence of products produced on each production line every day.

[0010] On the one hand, an embodiment of the present application provides a production plan generating device, including:

[0011] An acquisition module is used to acquire decision information corresponding to the production plan, wherein the decision information includes order information, production line information, product information and production equipment information;

[0012] A determination module is used to determine the constraints and planning objectives corresponding to the production plan based on the decision information; wherein the constraints represent the limiting conditions that the production plan must follow;

[0013] The generation module is used to generate a production plan based on constraints and planning objectives through the mixed rectification planning MIP model. The production plan includes the number and sequence of products produced on each production line every day.

[0014] In a possible embodiment, the acquisition module is used to: obtain order information, production line information, product information and production equipment information; wherein the order information includes product name, product quantity and product delivery time; the production line information includes production line name and production line type; the product information includes product name and product type corresponding to the product name; and the production equipment information includes mold change information between different product types in each production line.

[0015] In a possible embodiment, a determination module is used to: determine constraints corresponding to a production plan based on decision information; wherein the constraints include inventory constraints, delivery constraints, production line capacity constraints, and product constraints selected by the production line; determine planning objectives corresponding to the production plan based on the decision information; wherein the planning objectives are used to optimize the cost information of the production plan, and the cost information includes inventory costs, mold change costs, and penalty interest costs.

[0016] In one possible embodiment, the determination module is used to: determine the daily production and delivery volume of each product corresponding to the production plan based on the decision information; determine the daily inventory of each product based on the production volume and delivery volume, and limit the inventory within a preset range, wherein the preset range takes into account the allowable out-of-stock quantity.

[0017] In a possible embodiment, the determination module is used to: determine the daily demand, delivery, and unfinished production of each product corresponding to the production plan based on the decision information; wherein the sum of the demand for the product on the day and the unfinished production of the product the day before is equal to the sum of the unfinished production of the product on the day and the delivery on the day.

[0018] In a possible embodiment, the determination module is used to: determine the production volume, unit product production time, and average mold change time and maintenance time of each product on each production line per day corresponding to the production plan based on the decision information; determine the unavailability time of the production line based on the production volume, unit product production time, average mold change time, and maintenance time.

[0019] In a possible embodiment, the generation module is used to: generate an initial population of a production plan through a MIP model; wherein the initial population includes multiple individuals, each individual represents a possible plan, and the plan satisfies constraints and is close to the plan goal; calculate multiple fitnesses corresponding to the multiple individuals in the initial population, and the fitnesses represent the degree of closeness of the individuals to the plan goal; and generate a production plan based on the multiple fitnesses.

[0020] In a possible embodiment, the generation module is used to: determine whether multiple fitnesses meet preset conditions based on the number of iterations and iteration times corresponding to the multiple fitnesses; if so, generate a production plan based on the initial population; if not, update multiple individuals based on the perturbation operator to obtain a new population, until the fitnesses corresponding to the multiple individuals in the new population meet the preset conditions, and then generate a production plan based on the new population.

[0021] On the one hand, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes any one of the above-mentioned production plan generation methods.

[0022] On the one hand, the present application provides a computer-readable storage medium, which includes a program code. When the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute any of the above-mentioned production plan generation methods.

[0023] On the one hand, an embodiment of the present application provides a computer program product, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium; when a processor of an electronic device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, so that the electronic device executes any one of the above-mentioned production plan generation methods.

[0024] The beneficial effects of this application are as follows:

[0025] (1) Reduce manual calculation time and improve planning efficiency. This application obtains decision information corresponding to the production plan and generates a production plan using a mixed integer programming (MIP) model. This can significantly reduce the tedious calculation and analysis process in traditional manual planning and achieve a high degree of automation, thereby greatly shortening the time required to formulate production plans. Compared with traditional methods that require a lot of time for manual calculations and adjustments, this application can handle more complex constraints in a shorter time and quickly generate a production plan that includes the number of products produced on each production line every day and the order of the products, greatly improving production scheduling efficiency.

[0026] (2) Respond quickly to abnormal situations and improve production flexibility. During the production process, sudden abnormal situations (such as equipment failures, changes in order demand, etc.) often affect the production progress and the effectiveness of plan execution. The traditional manual adjustment plan has a slow response speed and is prone to ignoring some constraints. This application generates a production plan based on the MIP model, which can quickly recalculate and adjust the production plan in a short time, and provide the optimal solution or near-optimal solution based on the current production environment and constraints. This enables the production plan to respond to emergencies more flexibly, ensure the continuity of the production line, minimize production downtime, and improve overall production efficiency.

[0027] (3) Improve the scientificity and consistency of decision-making. This application generates a production plan based on the MIP model. Its optimization algorithm relies on mathematical models and computer simulations, which can provide a scientific basis for decision-making and avoid the uncertainty caused by human subjective judgment. Through standardized algorithm processes, the formulation of production plans has a high degree of consistency, reducing the risks that may be caused by subjective biases of individual planners. The results of automatic optimization of the system can ensure the coordination and predictability of the entire production process, thereby enhancing the reliability and execution effect of the production plan.

[0028] (4) Improve resource utilization and reduce production costs. After obtaining decision information including order information, production line information, product information and production equipment information, this application generates a production plan based on the MIP model, which can not only improve the efficiency of production planning in terms of time, but also optimize the production load of the production line by accurately matching the production capacity of the product and the production line, which can reduce equipment idle time and mold change time, reduce unnecessary resource waste, and effectively improve resource utilization.

[0029] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0031] Figure 1 This is a schematic diagram of an application scenario in an embodiment of the present application;

[0032] Figure 2This is a flowchart of a method for generating a production plan in an embodiment of the present application;

[0033] Figure 3 This is a schematic diagram of the structure of a production plan generating device in an embodiment of the present application;

[0034] Figure 4 The present invention is a schematic diagram of a hardware structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be arbitrarily combined with each other. In addition, although the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in an order different from that here.

[0036] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0037] The following is a brief introduction to the design concept of the embodiment of the present application:

[0038] In modern manufacturing, factories are faced with large-scale daily production planning problems for multiple product types and multiple production lines. How to reasonably allocate a large number of orders to different production lines, complete production tasks as quickly as possible, and meet customer delivery needs is the core challenge for factories. However, the formulation of production plans mainly depends on the professional experience and judgment of on-site planners. With the on-site planners' understanding of historical production scheduling data and production processes, they comprehensively evaluate the production line's capacity and perform detailed decomposition of orders to obtain relevant production plans. This production planning method relies too much on the experience and subjective judgment of on-site planners and lacks unified standards. When dealing with complex large-scale orders and multiple production lines, the planning effect is difficult to guarantee.

[0039] In view of this, the embodiments of the present application provide a production plan generation method, device, electronic device and storage medium. Among them, the production plan generation method includes: obtaining decision information corresponding to the production plan, wherein the decision information includes order information, production line information, product information and production equipment information; based on the decision information, determining the constraints and plan goals corresponding to the production plan; wherein the constraints represent the restrictions that the production plan must follow; based on the constraints and plan goals, a production plan is generated through a mixed rectification planning MIP model, and the production plan includes the number of products produced by each production line every day and the product sequence. In this way, through the mixed integer programming model and optimization algorithm, the scientific, automated and intelligent production plan is realized, which not only improves the accuracy and resource utilization of the production plan, but also enhances the flexibility and adaptability of the production plan, significantly improving the production efficiency and market competitiveness of the enterprise.

[0040] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0041] like Figure 1 , which is a schematic diagram of an application scenario provided by an embodiment of the present application. In the schematic diagram of the application scenario, a terminal device 101 and a server 102 are included. The terminal device 101 and the server 102 communicate with each other through a communication network.

[0042] The terminal device 101 is an electronic device used by the target object, and the electronic device may be a personal computer, a mobile phone, a tablet computer, a notebook, an e-book reader, a vehicle-mounted terminal, etc. In addition, a client related to production plan generation may be installed on the terminal device 101, and the client may be software (for example, an APP, a browser, etc.), or a web page, a small program, etc. The target object may use the above-mentioned client related to production plan generation through the terminal device 101 to perform operations related to production plan generation.

[0043] The server 102 may be an independent physical server or an edge device 102 in the field of cloud computing. It may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, cloud functions, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (English name: Content Delivery Network, abbreviated as CDN), as well as big data and artificial intelligence platforms.

[0044] There is no restriction on the number of the terminal devices 101 and / or servers 102 .

[0045] It should be noted that the production plan generation method in the embodiment of the present application can be executed by the terminal device 101 or the server 102 alone, or it can be executed by the terminal device 101 and the server 102 together. For example, when executed by the server 102 alone, the server 102 obtains the decision information corresponding to the production plan, wherein the decision information includes order information, production line information, product information and production equipment information; based on the decision information, the constraints and plan objectives corresponding to the production plan are determined; wherein the constraints represent the restrictions that the production plan must follow; based on the constraints and plan objectives, a production plan is generated through the mixed rectification planning MIP model, and the production plan includes the number of products produced by each production line every day and the order of products. After the server 102 generates the production plan, the production plan can be sent to the terminal device 101.

[0046] The following describes the production plan generation method provided by the exemplary embodiment of the present application in combination with the above-mentioned application scenarios and with reference to the accompanying drawings. It should be noted that the above-mentioned application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the implementation methods of the present application are not subject to any limitations in this regard.

[0047] refer to Figure 2 , is an implementation flow chart of a production plan generation method provided in an embodiment of the present application. Here, the server is used as the execution subject for introduction. The specific implementation process of the method is as follows:

[0048] S201, obtaining decision information corresponding to the production plan, wherein the decision information includes order information, production line information, product information and production equipment information.

[0049] In the embodiment of the present application, the decision information includes the order information, production line information, product information and production equipment information corresponding to the production plan; wherein the order information includes the product name, product quantity and product delivery time; the production line information includes the production line name and production line type; the product information includes the product name and the product type corresponding to the product name; and the production equipment information includes the mold change information between different product types in each production line, and the equipment repair and maintenance information. The decision information obtained above can be adjusted by the planning department and the IT department in accordance with the actual business situation and the degree of digitization in accordance with the business logic.

[0050] According to the above decision information, the corresponding production plan can be generated. By integrating multi-dimensional information such as orders, production lines, products and production equipment, comprehensive input data is provided for the generation algorithm of the production plan. These data are the basis for the generation of the production plan and directly affect the feasibility and optimization effect of the plan. For example, order information determines the urgency and delivery requirements of the production task, and the production line information and production equipment information determine the production capacity and resource constraints. Detailed production line information and production equipment information can ensure that the plan is operational in actual production. The production plan generated by integrating this information can more accurately reflect the actual production conditions, thereby improving the reliability and execution effect of the plan. In addition, by obtaining detailed decision information, the production plan can be flexibly adjusted to cope with different production scenarios. For example, when the order demand changes or the equipment fails, the system can regenerate the production plan according to the latest decision information to ensure the continuity and efficiency of production. Thirdly, through the obtained decision information, the production plan generation method can adapt to the expansion of production scale and changes in business. For example, when an enterprise adds a new production line or product, the system can quickly adjust the production plan by updating the decision information without redesigning the entire planning process.

[0051] S202, based on the decision information, determining the constraints and planning objectives corresponding to the production plan; wherein the constraints represent the limiting conditions that the production plan must comply with.

[0052] In the embodiment of the present application, the constraints and planning goals of the production plan interact with each other. The constraints are the basic requirements that the production plan must meet, and they limit the scope of achieving the planning goals; the planning goals are the direction of production plan optimization, which guides the setting and adjustment of constraints. The determination of constraints and planning goals is based on decision information, including:

[0053] Based on the decision information, determine the constraints corresponding to the production plan; the constraints include: inventory constraints, delivery constraints, production line capacity constraints, and product constraints selected by the production line.

[0054] In one embodiment, when the constraint condition is an inventory constraint, its implementation method is: by obtaining decision-based information, determine the daily production and delivery volume of each product corresponding to the production plan; based on the production volume and delivery volume, determine the daily inventory of each product, and limit the inventory to a preset range, wherein the preset range takes into account the allowable out-of-stock quantity.

[0055] The constraint formulas corresponding to inventory constraints include:

[0056]

[0057] INV it +INVE it≥INVL it i∈I,t∈T (2)

[0058] INV it ≤INVU it i∈I,t∈T (3)

[0059] Formula (1) is the inventory calculation formula, INV it : represents the inventory of product i on day t; X ilt : represents the production volume of product i on production line l on day t; it : represents the delivery quantity of product i on day t.

[0060] Formula (2) is the inventory lower limit constraint formula, INVE it : INVE represents the allowable out-of-stock quantity of product i on day t; INVL it : represents the lower limit of inventory for product i on day t. This formula is used to constrain the inventory of each product to not be lower than the set lower limit. The lower limit constraint is used to prevent the inventory of some products from being too low to meet customer order requirements. This constraint helps avoid production interruptions and emergency replenishment, especially during peak product demand, to ensure continuous supply.

[0061] Formula (3) is the inventory upper limit constraint formula, INVE it : INVE represents the allowable out-of-stock quantity of product i on day t; INVL it : represents the lower limit of the inventory of product i on day t. This formula is used to require that the inventory of each product must not exceed the set upper limit. The upper limit constraint is used to avoid inventory backlogs, reduce storage costs and expiration losses. Excessive inventory will occupy funds and storage space, and may even cause inventory to become obsolete or damaged, affecting product turnover.

[0062] The above formulas (1) to (3) together constitute the inventory constraint formula, which is used to ensure that the inventory level of all products fluctuates within a reasonable range in the production plan, that is, to avoid excess inventory or shortage of inventory. This constraint condition usually needs to consider the product's production cycle, demand fluctuations, and order delivery time to ensure that the inventory can be maintained at an appropriate level throughout the production process. Too much inventory will occupy resources and funds, and too little inventory may cause delayed delivery of orders.

[0063] In one embodiment, when the constraint condition is a delivery constraint, the implementation method is: based on the decision information, determine the daily demand, delivery, and unfinished production of each product corresponding to the production plan; wherein the sum of the demand for the product on the day and the unfinished production of the product the day before is equal to the sum of the unfinished production of the product on the day and the delivery on the day.

[0064] The constraint formula of the delivery constraint can be:

[0065] L it +O it =D it +L i,t-1 i∈I,t∈T (4)

[0066] In formula (4), L it : represents the unfinished quantity of product i on day t, D it : represents the demand for product i on day t. This constraint is used to ensure that all orders can be produced and delivered according to the scheduled delivery time. This constraint requires that the delivery time and priority of the order must be considered during the production process to ensure that high-priority orders can be processed and delivered on time. The work efficiency of the production line and the effective allocation of resources must also be considered to avoid production delays.

[0067] In one embodiment, when the constraint condition is the production capacity constraint of the production line, the implementation method is as follows: based on the decision information, determine the production volume of each product on each production line per day corresponding to the production plan, the unit product production time, and the average mold change time and maintenance time of the production line; based on the production volume, unit product production time, average mold change time, and maintenance time, determine the unavailable time of the production line.

[0068] The capacity constraint formula of the production line can be:

[0069]

[0070] In formula (5), TK il : represents the unit production time of product i on production line l; Y ilt : Indicates whether product i is being produced on production line l; CT l : represents the average membrane change time of production line l; C lt : Indicates the maintenance time and upkeep time of production line l; WT lt : represents the unavailable time of production line l. This constraint formula is used to constrain the production capacity of the production line by the standard mold change time between each product. In the production scheduling of multiple product types, the standard mold and color change time is an important influencing factor. Each mold and color change consumes a certain amount of time, which affects the production efficiency of the production line. Therefore, these times must be considered in the scheduling to ensure that the production tasks can be completed within the specified time.

[0071] In one embodiment, when the constraint condition is a product constraint for selecting production lines, the implementation method is: determine that the production volume of each product on each production line per day is less than or equal to the maximum production value of the product on the production line. The specific constraint formula is:

[0072] X ilt ≤Mil *Y ilt i∈I,l∈L,t∈T (6)

[0073] In formula (6), M il : represents the production volume of product i on the tth day of production line l. This constraint formula takes into account the product types produced by each production line and may involve the capacity, adaptability and configuration of the equipment. Production line selection constraints usually require that the selection of equipment must take into account the production capacity, availability and matching degree of the production line with the specific product type.

[0074] The above method for determining constraint conditions and planning objectives based on decision information further includes:

[0075] Based on the decision information, the planning objectives corresponding to the production plan are determined; wherein the planning objectives are used to optimize the cost information of the production plan, and the cost information includes inventory cost, mold change cost, and penalty interest cost.

[0076] Through the above method, the constraints and planning objectives of the production plan created based on the decision-making information are completed. Through the interaction between the constraints and planning objectives of the production plan, the production plan is guided to approach the planning objectives within the constraints.

[0077] S203, based on the constraints and planning objectives, a production plan is generated through a mixed improvement planning MIP model, and the production plan includes the quantity and sequence of products produced on each production line every day.

[0078] In the implementation of this application, after determining the constraints and planning objectives corresponding to the decision information, further, based on the constraints and planning objectives, a production plan is generated through a hybrid rectification planning MIP model. The specific implementation method includes:

[0079] Through the MIP model, the initial population of the production plan is generated, and multiple fitnesses corresponding to multiple individuals in the initial population are calculated. The initial population contains multiple individuals, each of which represents a possible plan. The plan satisfies the constraints and is close to the plan goal. The fitness represents the degree of closeness of the individual to the plan goal.

[0080] Generate a production plan based on multiple fitnesses. Specifically, based on the number of iterations and iteration times corresponding to the multiple fitnesses, determine whether the multiple fitnesses meet the preset conditions, that is, determine whether the multiple fitnesses continue to change after the number of iterations and iteration time exceed the set range; if so, generate a production plan based on the initial population; if not, update multiple individuals based on the perturbation operator to obtain a new population, until the fitness corresponding to multiple individuals in the new population meets the preset conditions, and then generate a production plan based on the new population.

[0081] In one embodiment, based on a perturbation operator, multiple individuals are updated to obtain a new population, including: executing a perturbation operator to add perturbations to multiple individuals in an initial population to achieve adding new individuals to the initial population; based on individual fitness, multiple individuals whose individual fitness is within a specified range are selected from the population after adding the new individuals, thereby obtaining a new population.

[0082] In one embodiment, after obtaining a new population, if the population size is P, a total of P individuals are selected as parents to form P / 2 pairs, and a crossover operation is performed on each pair of parents to obtain a pair of offspring; after performing a mutation operation on the obtained offspring, the offspring fitness is calculated, and based on the offspring fitness, the offspring is selected to join the new population; after performing a directed mutation on each individual in the new population, the offspring fitness is further calculated, and the best is selected to join the new population, so as to further update the new population, and based on the multiple fitnesses corresponding to the updated new population, it is determined whether the multiple fitnesses meet the preset conditions, that is, whether the multiple fitnesses continue to change after the number of iterations and the iteration time exceed the set range; if so, a production plan is generated based on the updated new population. In this way, the use of an adaptive and efficient optimization heuristic algorithm is realized, and the optimization solution is continuously found through the iterative process of the heuristic algorithm, thereby effectively improving the quality of the production plan.

[0083] The technical effects achieved by the embodiments of the present application are as follows:

[0084] (1) Reduce manual calculation time and improve planning efficiency. This application obtains decision information corresponding to the production plan and generates a production plan using a mixed integer programming (MIP) model. This can significantly reduce the tedious calculation and analysis process in traditional manual planning and achieve a high degree of automation, thereby greatly shortening the time required to formulate production plans. Compared with traditional methods that require a lot of time for manual calculations and adjustments, this application can handle more complex constraints in a shorter time and quickly generate a production plan that includes the number of products produced on each production line every day and the order of the products, greatly improving production scheduling efficiency.

[0085] (2) Respond quickly to abnormal situations and improve production flexibility. During the production process, sudden abnormal situations (such as equipment failures, changes in order demand, etc.) often affect the production progress and the effectiveness of plan execution. The traditional manual adjustment plan has a slow response speed and is prone to ignoring some constraints. This application generates a production plan based on the MIP model, which can quickly recalculate and adjust the production plan in a short time, and provide the optimal solution or near-optimal solution based on the current production environment and constraints. This enables the production plan to respond to emergencies more flexibly, ensure the continuity of the production line, minimize production downtime, and improve overall production efficiency.

[0086] (3) Improve the scientificity and consistency of decision-making. This application generates a production plan based on the MIP model. Its optimization algorithm relies on mathematical models and computer simulations, which can provide a scientific basis for decision-making and avoid the uncertainty caused by human subjective judgment. Through standardized algorithm processes, the formulation of production plans has a high degree of consistency, reducing the risks that may be caused by subjective biases of individual planners. The results of automatic optimization of the system can ensure the coordination and predictability of the entire production process, thereby enhancing the reliability and execution effect of the production plan.

[0087] (4) Improve resource utilization and reduce production costs. After obtaining decision information including order information, production line information, product information and production equipment information, this application generates a production plan based on the MIP model, which can not only improve the efficiency of production planning in terms of time, but also optimize the production load of the production line by accurately matching the production capacity of the product and the production line, which can reduce equipment idle time and mold change time, reduce unnecessary resource waste, and effectively improve resource utilization.

[0088] Based on the same inventive concept, the present application also provides a production plan generating device, such as Figure 3 FIG. 1 is a schematic diagram of a production plan generating device according to an embodiment of the present application, wherein the device comprises:

[0089] The acquisition module 301 is used to acquire decision information corresponding to the production plan, wherein the decision information includes order information, production line information, product information and production equipment information;

[0090] A determination module 302 is used to determine the constraint conditions and plan objectives corresponding to the production plan based on the decision information; wherein the constraint conditions represent the limiting conditions that the production plan must comply with;

[0091] The generation module 303 is used to generate a production plan based on constraints and planning objectives through a mixed improvement planning MIP model. The production plan includes the quantity and sequence of products produced by each production line every day.

[0092] In a possible embodiment, the acquisition module 301 is used to: obtain order information, production line information, product information and production equipment information; wherein the order information includes product name, product quantity and product delivery time; the production line information includes production line name and production line type; the product information includes product name and product type corresponding to the product name; and the production equipment information includes mold change information between different product types in each production line.

[0093] In a possible embodiment, the determination module 302 is used to: determine the constraints corresponding to the production plan based on the decision information; wherein the constraints include: inventory constraints, delivery constraints, production line capacity constraints, and product constraints selected by the production line; determine the planning objectives corresponding to the production plan based on the decision information; wherein the planning objectives are used to optimize the cost information of the production plan, and the cost information includes inventory costs, mold change costs, and penalty interest costs.

[0094] In one possible embodiment, the determination module 302 is used to: determine the daily production and delivery volume of each product corresponding to the production plan based on the decision information; determine the daily inventory of each product based on the production volume and delivery volume, and limit the inventory within a preset range, wherein the preset range takes into account the allowable out-of-stock quantity.

[0095] In a possible embodiment, the determination module 302 is used to: determine the daily demand, delivery, and unfinished production of each product corresponding to the production plan based on the decision information; wherein the sum of the demand for the product on the day and the unfinished production of the product the day before is equal to the sum of the unfinished production of the product on the day and the delivery on the day.

[0096] In a possible embodiment, the determination module 302 is used to: determine the production volume, unit product production time, and average mold change time and maintenance time of each product on each production line per day corresponding to the production plan based on the decision information; determine the unavailability time of the production line based on the production volume, unit product production time, average mold change time, and maintenance time.

[0097] In a possible embodiment, the generation module 303 is used to: generate an initial population of a production plan through a MIP model; wherein the initial population includes multiple individuals, each individual represents a possible plan, and the plan satisfies constraints and is close to the plan goal; calculate multiple fitnesses corresponding to multiple individuals in the initial population, and the fitnesses represent the degree of closeness of the individuals to the plan goal; and generate a production plan based on the multiple fitnesses.

[0098] In a possible embodiment, the generation module 303 is used to: determine whether multiple fitnesses meet preset conditions based on the number of iterations and iteration times corresponding to the multiple fitnesses; if so, generate a production plan based on the initial population; if not, update multiple individuals based on the perturbation operator to obtain a new population, until the fitnesses corresponding to the multiple individuals in the new population meet the preset conditions, and then generate a production plan based on the new population.

[0099] In some possible implementations, the production plan generation device according to the present application may include at least a processor and a memory. The memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the production plan generation method according to various exemplary implementations of the present application described in this specification. For example, the processor may execute the following steps: Figure 2 Follow the steps shown in .

[0100] Based on the same inventive concept, an electronic device is also provided in the embodiment of the present application. The electronic device can realize the functions of the aforementioned production plan generation method and device. Figure 4 , electronic equipment includes:

[0101] At least one processor 401, and a memory 402 connected to the at least one processor 401. The specific connection medium between the processor 401 and the memory 402 is not limited in the embodiment of the present application. Figure 4 In the example, the processor 401 and the memory 402 are connected via the bus 400. The bus 400 is Figure 4 The connection between other components is shown by bold lines, and is not intended to be limiting. The bus 400 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. Alternatively, the processor 401 can also be called a controller, and there is no limitation on the name.

[0102] In the embodiment of the present application, the memory 402 stores instructions that can be executed by at least one processor 401. The at least one processor 401 can execute the production plan generation method discussed above by executing the instructions stored in the memory 402. The processor 401 can implement Figure 3 The functions of each module in the device shown.

[0103] Among them, the processor 401 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 402 and calling the data stored in the memory 402, the various functions of the device and process data, the device can be monitored as a whole.

[0104] In one possible design, the processor 401 may include one or more processing units, and the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 401. In some embodiments, the processor 401 and the memory 402 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on separate chips.

[0105] Processor 401 can 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 device, a discrete gate or transistor logic device, a discrete hardware component, and can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the production plan generation method disclosed in the embodiments of the present application can be directly embodied as a hardware processor to execute, or a combination of hardware and software modules in the processor to execute.

[0106] The memory 402 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 402 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 402 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 402 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0107] By programming the processor 401, the code corresponding to the production plan generation method described in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 2The steps of the production plan generation method of the embodiment shown are as follows: How to design and program the processor 401 is a technique known to those skilled in the art and will not be described in detail here.

[0108] Based on the same inventive concept, an embodiment of the present application further provides a storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes the production plan generation method discussed above.

[0109] In some possible implementations, various aspects of the production plan generation method provided in the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of the production plan generation method according to various exemplary embodiments of the present application described above in this specification.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A production plan generation method, characterized in that: The method comprises: Obtaining decision information corresponding to the production plan, wherein the decision information includes order information, production line information, product information and production equipment information; Based on the decision information, determine the constraint conditions and plan objectives corresponding to the production plan; wherein the constraint conditions represent the limiting conditions that the production plan must follow; Based on the constraints and the planning objectives, the production plan is generated through a mixed improvement planning (MIP) model, and the production plan includes the quantity and sequence of products produced on each production line every day.

2. The method according to claim 1, characterized in that: The obtaining of decision information corresponding to the production plan includes: Obtain the order information, the production line information, the product information and the production equipment information; wherein the order information includes the product name, product quantity and product delivery time; the production line information includes the production line name and production line type; the product information includes the product name and the product type corresponding to the product name; and the production equipment information includes mold change information between different product types in each production line.

3. The method according to claim 1, characterized in that: Determining the constraint conditions and planning objectives corresponding to the production plan based on the decision information includes: Based on the decision information, determine the constraints corresponding to the production plan; wherein the constraints include: inventory constraints, delivery constraints, production line capacity constraints, and product constraints selected by the production line; Based on the decision information, a planning target corresponding to the production plan is determined; wherein the planning target is used to optimize the cost information of the production plan, and the cost information includes inventory cost, mold change cost, and penalty interest cost.

4. The method according to claim 3, characterized in that: The determining, based on the decision information, constraints corresponding to the production plan includes: Based on the decision information, determine the daily production and delivery volume of each product corresponding to the production plan; Based on the production volume and the delivery volume, the daily inventory of each product is determined, and the inventory is limited to a preset range, wherein the preset range takes into account an allowable stock-out quantity.

5. The method according to claim 3, characterized in that: The determining, based on the decision information, constraints corresponding to the production plan includes: Based on the decision information, determine the daily demand, delivery, and unfinished production of each product corresponding to the production plan; wherein the sum of the demand for the product on that day and the unfinished production of the product the day before is equal to the sum of the unfinished production of the product on that day and the delivery on that day.

6. The method according to claim 3, characterized in that: The determining, based on the decision information, constraints corresponding to the production plan includes: Based on the decision information, determine the production volume of each product on each production line per day, the unit product production time, and the average mold change time and maintenance time of the production line corresponding to the production plan; The unavailability time of the production line is determined based on the production volume, the unit product production time, the average mold change time, and the maintenance time.

7. The method according to claim 1, characterized in that: The generating of the production plan based on the constraint conditions and the planning objectives through a hybrid reform planning MIP model includes: Generate an initial population of the production plan through the MIP model; wherein the initial population includes a plurality of individuals, each of which represents a possible plan, and the plan satisfies the constraint conditions and is close to the plan target; Calculating a plurality of fitnesses corresponding to the plurality of individuals in the initial population, wherein the fitnesses represent the degree of proximity between the individuals and the planned target; Based on the multiple fitness levels, the production plan is generated.

8. The method according to claim 7, characterized in that: The step of generating the production plan based on the multiple fitness levels comprises: Based on the number of iterations and iteration times respectively corresponding to the multiple fitnesses, determining whether the multiple fitnesses meet preset conditions; If yes, generating the production plan based on the initial population; If not, the multiple individuals are updated based on the disturbance operator to obtain a new population, until the fitness corresponding to the multiple individuals in the new population meets the preset condition, and then the production plan is generated based on the new population.

9. A production plan generating device, characterized in that: include: An acquisition module, used to acquire decision information corresponding to the production plan, wherein the decision information includes order information, production line information, product information and production equipment information; A determination module, used to determine the constraint conditions and plan objectives corresponding to the production plan based on the decision information; wherein the constraint conditions represent the limiting conditions that the production plan must comply with; A generation module is used to generate the production plan based on the constraints and the planning objectives through a mixed improvement planning MIP model, wherein the production plan includes the quantity and sequence of products produced by each production line every day.

10. An electronic device, characterized in that: The device comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes any one of the methods in claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The storage medium comprises a program code, and when the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute any one of the methods of claims 1 to 8.

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