A workshop production progress control method and system

Abstract

The present invention relates to the technical field of workshop production schedule control, and discloses a workshop production schedule control method and system, the method comprising: obtaining first target automobile parts demand data and a first constraint time corresponding to the first target automobile parts demand data, and establishing a first workshop production schedule strategy in combination with a first planning and scheduling algorithm; judging whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy, and controlling in different ways according to the judgment result. The present invention can flexibly adjust the workshop production schedule according to the changing needs of customers, and ensure production efficiency and product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop production progress control, and in particular to a workshop production progress control method and system. Background Art

[0002] With the development of the manufacturing industry, workshop production schedule control has become particularly important. Traditional workshop production schedule control methods often only schedule production based on the current confirmed demand for automotive parts. However, there are often many uncertain factors in actual production, such as changes in customer demand and production line failures, which may cause the production schedule to fail to proceed as planned.

[0003] Therefore, how to flexibly adjust the workshop production schedule according to actual needs to ensure production efficiency and product quality has become a problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a workshop production progress control method and system, which can solve the problem in the prior art that the workshop production progress cannot be adjusted according to the changing needs of customers.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a workshop production schedule control method, comprising:

[0009] Acquire first target automobile parts demand quantity data and a first constraint time corresponding to the first target automobile parts demand quantity data, wherein the first target automobile parts demand quantity data is currently determined automobile parts demand quantity data;

[0010] Establishing a first workshop production schedule strategy based on the first target automobile parts demand data and a first constraint time corresponding to the first target automobile parts demand data in combination with a first planning and scheduling algorithm;

[0011] Determine whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy, and if so, use the first workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule;

[0012] If not, the second workshop production schedule strategy and the preset first resource allocation logic are used to control the workshop production schedule.

[0013] As a preferred solution of the workshop production schedule control method of the present invention, the second workshop production schedule strategy includes:

[0014] Establishing a second workshop production schedule strategy based on the first target automobile parts demand data, the first constraint time corresponding to the first target automobile parts demand data, the second target automobile parts demand data, and the second constraint time corresponding to the second target automobile parts demand data in combination with a second planning and scheduling algorithm;

[0015] The second target automobile parts demand data is the new automobile parts demand.

[0016] As a preferred solution of the workshop production progress control method of the present invention, the method of obtaining the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data includes:

[0017] Let X = {x1, x2, ..., x i ,...,x n} is the first target automobile parts demand data set, where x i represents the demand of the i-th customer, and n represents the total number of customers corresponding to the currently determined demand for auto parts;

[0018] Let T = {t1, t2, ..., t i ,...,t n} is the first constraint time data set corresponding to the first target automobile parts demand data, where t i Represents the agreed completion time of the demand of the i-th customer.

[0019] As a preferred solution of the workshop production progress control method described in the present invention, the first planning and scheduling algorithm includes:

[0020] The first planning and scheduling algorithm includes establishing a first objective function and a first preset constraint condition, wherein the first objective function is expressed as follows:

[0021]

[0022] Among them, c i is the basic cost of producing the i-th customer parts in the first target automobile parts demand data, x i It is represented as the demand of the i-th customer in the first target automobile parts demand data, o i is the overtime production cost coefficient of the i-th customer parts in the first target automobile parts demand data, y i Indicates whether the i-th customer parts in the first target automobile parts demand data are produced overtime, and the value is 0 or 1. If it is overtime production, the value is 1, otherwise it is 0. i is the penalty cost for delayed delivery in the first target automotive parts demand data, z i Indicates whether the delivery of the i-th customer parts in the first target automobile parts demand data is delayed, and the value is 0 or 1. If it is delayed, the value is 1, otherwise it is 0;

[0023] The first preset constraint condition includes demand satisfaction constraint, time window constraint, and production rate and resource constraint, which are expressed as follows:

[0024] The requirement satisfaction constraint is expressed as:

[0025]

[0026] Among them, S1 represents the time point when the production of the first customer parts in the first target automobile parts demand data begins. It represents the production volume from the time when the first target automobile parts demand data is started to the agreed completion time of the first customer’s demand, C L The production volume calculation formula is max(t1,t2,...,t n ) represents the maximum agreed completion time of the customer's demand;

[0027] The time window constraint is expressed as:

[0028] S i +TS i ·x i ≤t i

[0029] Among them, S i is the time point when the production of the i-th customer parts begins, TS i is the time required to produce a single component, t i is the agreed completion time of the demand of the i-th customer;

[0030] The production rate and resource constraints include normal production rate constraints, overtime production rate constraints and resource constraints, wherein the normal production rate constraints are:

[0031] x i ≤r i ×L i ×M i

[0032] Among them, r i is the normal production speed when producing the i-th customer parts, L i is the number of man-hours available to produce the i-th customer component, M i is the number of available production lines when producing the i-th customer component;

[0033] The overtime production rate constraint is:

[0034] γ i ≤ρ i ×O i ×M i

[0035] Among them, γ i represents the overtime production volume when producing the i-th customer parts, ρ i is the overtime production speed when producing parts for the i-th customer, and ρ i <r i , O i is the number of overtime hours in producing parts for the i-th customer;

[0036] The resource constraints are:

[0037] Maximum use of labor and production lines: L i +O i ≤L max ,M i ≤M max , where L max Indicates the maximum man-hours, M max Indicates the maximum number of production lines.

[0038] As a preferred solution of the workshop production schedule control method of the present invention, wherein: the judging whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy includes:

[0039] Recombining the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data with the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data;

[0040] Determine whether the reorganized data meets the production schedule strategy of the first workshop. If so, the production schedule strategy of the first workshop determines whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule.

[0041] If not satisfied, the second workshop production schedule strategy and the preset first resource allocation logic are used to control the workshop production schedule.

[0042] As a preferred solution of the workshop production progress control method described in the present invention, it also includes:

[0043] When the second target automobile parts demand is greater than the first preset demand within the first preset time control threshold, judging whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy;

[0044] When the second target automobile parts demand quantity is not greater than the first preset demand quantity within the first preset time control threshold, and the second constraint time corresponding to the second target automobile parts demand quantity data is greater than the first preset time control threshold, judging whether the second target automobile parts demand quantity data and the second constraint time corresponding to the second target automobile parts demand quantity data meet the workshop production schedule according to the first workshop production schedule strategy;

[0045] When the customer corresponding to the second target automobile parts demand is a customer in the first preset customer list, the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy.

[0046] As a preferred solution of the workshop production progress control method described in the present invention, it also includes:

[0047] Determine whether the Nth target automobile parts demand data and the Nth constraint time corresponding to the Nth target automobile parts demand data meet the workshop production schedule according to the N-1th workshop production schedule strategy, and if so, use the N-1th workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule;

[0048] If not, the production schedule strategy of the Nth workshop and the preset first resource allocation logic are used to control the production schedule of the workshop.

[0049] In a second aspect, the present invention provides a workshop production progress control system, comprising:

[0050] A data acquisition module, used to acquire first target automobile parts demand data and a first constraint time corresponding to the first target automobile parts demand data, wherein the first target automobile parts demand data is currently determined automobile parts demand data;

[0051] A schedule strategy acquisition module, used to establish a first workshop production schedule strategy based on the first target automobile parts demand data and a first constraint time corresponding to the first target automobile parts demand data in combination with a first planning and scheduling algorithm;

[0052] a judgment module, used for judging whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy, and if so, using the first workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule;

[0053] The control module is used to use the second workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule if the conditions are not met.

[0054] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0055] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes a workshop production schedule control method and system, obtains first target automobile parts demand data and a first constraint time corresponding to the first target automobile parts demand data, the first target automobile parts demand data is the currently determined automobile parts demand data; according to the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data, combined with the first planning and scheduling algorithm, establishes a first workshop production schedule strategy; according to the first workshop production schedule strategy, judges whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule, if so, uses the first workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule; if not, uses the second workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule. The present invention can flexibly adjust the workshop production schedule according to the changing needs of customers to ensure production efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0058] Figure 1 A method flow chart of a workshop production schedule control method and system provided by an embodiment of the present invention;

[0059] Figure 2 An internal structural diagram of a computer device of a workshop production schedule control method and system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0061] Reference Figure 1-Figure 2 , which is the first embodiment of the present invention, provides a workshop production schedule control method;

[0062] Before describing the embodiments of the present application in detail, related concepts are first explained.

[0063] Workshop production schedule: Workshop production schedule refers to the time sequence and completion status of each production task or process from start to finish in the manufacturing workshop according to the production plan. It reflects the actual execution of production activities, including but not limited to the start and end time, current status, completion percentage, resources consumed, etc. of each production stage. Good production schedule management helps ensure that orders are completed on time, improve production efficiency, reduce inventory backlogs, and detect and solve problems in a timely manner.

[0064] Resource allocation logic: Resource allocation logic refers to the principles and methods for determining how to reasonably allocate limited production resources (such as workers, equipment, raw materials, production lines, etc.) to different production tasks or processes during production planning and scheduling. This logic aims to maximize resource utilization, meet production needs, reduce costs and ensure production efficiency. Factors that may be considered in resource allocation logic include task priority, worker skill matching, equipment availability and efficiency, material supply, etc. Common resource allocation logics include but are not limited to earliest completion time priority, minimum slack time priority, minimum cost principle, etc.

[0065] Workshop production schedule strategy: Workshop production schedule strategy refers to a series of planning, scheduling and control measures formulated to achieve efficient and orderly production activities. These strategies are designed to optimize the production process, ensure that production tasks proceed smoothly as planned, and have the ability to respond to emergencies and adjust production plans.

[0066] In related technologies, the control of workshop production progress often relies on fixed production plans and resource allocation logic. However, with the continuous changes in market demand and the diversification of customer needs, this fixed control method often cannot respond and adjust in time, resulting in low production efficiency and unstable product quality.

[0067] In order to solve the above problems, the present invention proposes a more flexible and efficient workshop production schedule control method and system.

[0068] This application provides a method that can effectively solve the above problems. Next, we will combine multiple embodiments to explain in detail how to implement the workshop production progress control method;

[0069] Figure 1 A method flow chart of a workshop production progress control method and system is shown, including:

[0070] S101, obtaining first target automobile parts demand data and a first constraint time corresponding to the first target automobile parts demand data, where the first target automobile parts demand data is currently determined automobile parts demand data;

[0071] Wherein, obtaining the first target automobile parts demand quantity data and the first constraint time corresponding to the first target automobile parts demand quantity data includes: X={x1,x2,...,x i ,...,x n} is the first target automobile parts demand data set, where x i represents the demand of the i-th customer, and n represents the total number of customers corresponding to the currently determined demand for auto parts;

[0072] Let T = {t1, t2, ..., ti ,...,t n} is the first constraint time data set corresponding to the first target automobile parts demand data, where t i Represents the agreed completion time of the demand of the i-th customer.

[0073] In this embodiment, the first target automobile parts demand data set can be arranged in the order of customer order chronology, and can also be sorted from large to small or from small to large according to the first constraint time corresponding to the first target automobile parts demand data. It can also be sorted according to the size of customer demand, and can also be sorted according to the importance of the customer, and there is no limitation here.

[0074] In this embodiment, the agreed completion time for the demand of the i-th customer can be in the form of a specific date, for example, "completed before May 30, 2024", or in the form of a delivery period, for example, "delivery within 30 days after order confirmation", or in the form of a deadline + buffer period, for example, "June 15, 2024 is the latest delivery date, but delivery before June 20 is acceptable", etc.

[0075] It should be noted that the currently determined automobile parts demand data may be all automobile parts demand data before the use of the method of the present application, or all automobile parts demand data within a fixed time period before the use of the method of the present application, or may be a no-data state. For example, if the currently determined automobile parts demand data is a no-data state, it proves that no customer placed an order before the use of the method of the present application.

[0076] In an optional embodiment, the target automobile parts may include automobile parts such as shock absorbers, springs, stabilizer bars, control arms, doors, windows, roofs, fenders, bumpers, etc.;

[0077] It should be noted that by obtaining the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data, the current determined automobile parts demand situation can be understood, providing data support for subsequent production plan formulation.

[0078] S102, establishing a first workshop production schedule strategy based on the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data in combination with a first planning and scheduling algorithm;

[0079] The first planning and scheduling algorithm includes: the first planning and scheduling algorithm includes establishing a first objective function and a first preset constraint condition, and the first objective function is expressed as follows:

[0080]

[0081] Among them, c i is the basic cost of producing the i-th customer parts in the first target automobile parts demand data, x i It is represented as the demand of the i-th customer in the first target automobile parts demand data, o i is the overtime production cost coefficient of the i-th customer parts in the first target automobile parts demand data, y i Indicates whether the i-th customer parts in the first target automobile parts demand data are produced overtime, and the value is 0 or 1. If it is overtime production, the value is 1, otherwise it is 0. i is the penalty cost for delayed delivery in the first target automotive parts demand data, z i Indicates whether the delivery of the i-th customer parts in the first target automobile parts demand data is delayed, and the value is 0 or 1. If it is delayed, the value is 1, otherwise it is 0;

[0082] Furthermore, the first preset constraint condition includes demand satisfaction constraint, time window constraint, and production rate and resource constraint, which are expressed as follows:

[0083] The requirement satisfaction constraint is expressed as:

[0084]

[0085] Among them, S1 represents the time point when the production of the first customer parts in the first target automobile parts demand data begins. It represents the production volume from the time when the first target automobile parts demand data is started to the agreed completion time of the first customer’s demand, C L The production volume calculation formula is max(t1,t2,...,t n ) represents the maximum agreed completion time of the customer's demand;

[0086] The time window constraint is expressed as:

[0087] S i +TS i ·x i ≤t i

[0088] Among them, S i is the time point when the production of the i-th customer parts begins, TS i is the time required to produce a single component, t i is the agreed completion time of the demand of the i-th customer;

[0089] The production rate and resource constraints include normal production rate constraints, overtime production rate constraints, and resource constraints. The normal production rate constraint is:

[0090] x i ≤r i ×L i ×M i

[0091] Among them, r i is the normal production speed when producing the i-th customer parts, L i is the number of man-hours available to produce the i-th customer component, M i is the number of available production lines when producing the i-th customer component;

[0092] The overtime production rate constraint is:

[0093] γ i ≤ρ i ×O i ×M i

[0094] Among them, γ i represents the overtime production volume when producing the i-th customer parts, ρ i is the overtime production speed when producing parts for the i-th customer, and ρ i <r i , O i is the number of overtime hours in producing parts for the i-th customer;

[0095] The resource constraints are:

[0096] Maximum use of labor and production lines: L i +O i ≤L max ,M i ≤M max , where L max Indicates the maximum man-hours, M max Indicates the maximum number of production lines.

[0097] It should be noted that the production schedule strategy for the first workshop is a strategy combining the above-mentioned first objective function and the first preset constraint condition.

[0098] It should be noted that if the current production status has reached the maximum man-hours and the maximum number of production lines, no orders will be accepted or the agreed completion time of the orders is within an acceptable range. For example, when the maximum man-hours and the maximum number of production lines in the workshop are put into use, the time to complete the current order is 2024.05.20. Then, new acceptable orders need to ensure that the demand requirements are met after 2024.05.20 when the maximum man-hours and the maximum number of production lines in the workshop are put into use.

[0099] It should be noted that by obtaining and analyzing the demand data of the first target automotive parts and the corresponding constraint time, the company can clearly understand the current production demand and production pressure. This helps the company make more informed decisions, such as whether to accept new orders and how to arrange the production sequence of existing orders.

[0100] Secondly, by establishing the first objective function and the first preset constraint, the enterprise can formulate a workshop production schedule strategy that meets both customer needs and its own production capacity. This strategy not only takes into account factors such as production cost and production time, but also fully considers the limitations of production resources, such as man-hours and the number of production lines. This helps enterprises maximize production benefits while ensuring production quality.

[0101] In addition, by setting different constraints and objective functions, enterprises can flexibly adjust production strategies to adapt to market changes and changes in customer demand. For example, when market demand increases, enterprises can meet demand by increasing overtime production or expanding production line scale; when market demand decreases, enterprises can improve efficiency by optimizing production processes or reducing production costs.

[0102] S103, judging whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy, and if so, using the first workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule;

[0103] Wherein, judging whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy includes: reorganizing the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data with the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data;

[0104] In this embodiment, data reorganization can be performed according to the order sequence of customer orders. For example, if the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data are X = {x1, x2, x3, x4, x5, x6}, T = {t1, t2, t3, t4, t5, t6}, and t1, t2, t3, t4, t5, t6 are the customer's order sequence, the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data are X2 = {x1, x2, x3, x4, x5, x6}, T = {t1, t2, t3, t4, t5, t6}, and t1, t2, t3, t4, t5, t6 are the customer's order sequence, 2，1 ,x 2，2 ,x 2，3 ,x 2，4 ,x 2，5 ,x 2，6}、T2={t 2，1 ,t 2，2 ,t 2，3 ,t 2，4 ,t 2，5 ,t 2，6}, and t 2，1 ,t 2，2 ,t 2，3 ,t 2，4 ,t 2，5 ,t 2，6 is the order sequence of the customers, then the result of data reorganization according to the order sequence of the customers is:

[0105] X c ={x1,x2,x3,x4,x5,x6,x 2，1 ,x 2，2 ,x 2，3 ,x 2，4 ,x 2，5 ,x 2，6}

[0106] T={t1,t2,t3,t4,t5,t6,t 2，1 ,t 2，2 ,t 2，3 ,t 2，4 ,t 2，5 ,t 2，6},

[0107] The data can also be sorted from large to small according to the corresponding constraint time or reorganized from small to large. The data can also be reorganized according to the size of customer demand or the importance of the customer. There is no restriction here.

[0108] Furthermore, it is determined whether the reorganized data meets the production schedule strategy of the first workshop. If so, the production schedule strategy of the first workshop determines whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule;

[0109] In this embodiment, judging whether the reorganized data meets the production schedule strategy of the first workshop includes the following contents:

[0110] First, determine whether the second constraint time corresponding to the second target automobile parts demand data in the reorganized data is before the second constraint time corresponding to the first target automobile parts demand data;

[0111] If not, the reorganized data does not meet the production schedule strategy of the first workshop;

[0112] If so, determine whether the production volume generated during the time period from when the workshop has invested the maximum man-hours and the maximum number of production lines to the second constraint time of the first target automobile parts demand data or the second constraint time of the second target automobile parts demand data meets the sum of the first target automobile parts demand and the second target automobile parts demand;

[0113] If so, the reorganized data meets the production schedule strategy of the first workshop;

[0114] If not, the reorganized data does not meet the production schedule strategy of the first workshop. If not, the production schedule strategy of the second workshop and the preset first resource allocation logic are used to control the production schedule of the workshop.

[0115] In this embodiment, the first resource allocation logic includes dynamic personnel replacement logic and work reallocation and task priority adjustment logic, wherein the dynamic personnel replacement logic includes:

[0116] For each worker w i Define a skill vector JS i , where each element Js ij Represents worker w i The proficiency of mastering skill j (e.g., 1 for proficiency and 0 for unfamiliarity). The skill requirement matrix of the task set RT is composed of each task Rt j The corresponding skill demand vector R j Composition, where element r jk =1 indicates task Rt j Requires skill k, otherwise r jk =0;

[0117] When workers k When unable to work, calculate all other workers w l (l≠k) and task t jThe matching degree M lj , defined as:

[0118]

[0119] Select M lj The highest worker w l As w k while ensuring that the worker is not overloaded, i.e. its current task burden does not exceed its work capacity limit.

[0120] The work reallocation and task priority adjustment logic includes:

[0121] For each task Rt j Assign a priority level P j , high priority tasks are assigned first. It can be determined based on factors such as delivery time urgency and customer importance.

[0122] Establish the initial task allocation matrix RX, where RX ij =1 means worker w i Responsible for the task j , otherwise 0.

[0123] When workers k When asking for leave, first remove w in RX k All assigned tasks are then sorted according to the priority P j and workers l (l≠k)'s current task burden is redistributed.

[0124] For each task to be assigned Rt j , check all workers w that are not fully loaded l , choose such that:

[0125]

[0126] That is, minimize the sum of weighted priorities of worker tasks after assignment, giving priority to the assignment of high-priority tasks.

[0127] In this embodiment, the second workshop production schedule strategy includes: establishing the second workshop production schedule strategy according to the first target automobile parts demand data, the first constraint time corresponding to the first target automobile parts demand data, the second target automobile parts demand data, and the second constraint time corresponding to the second target automobile parts demand data, in combination with the second planning and scheduling algorithm;

[0128] It should be noted that the second target automobile parts demand data is the new automobile parts demand.

[0129] In this embodiment, the second planning and scheduling algorithm includes: obtaining the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data includes: recording X y ={x y1 ,x y2 ,...,x yj ,...,x ym} is the second target automobile parts demand data set, where x yj represents the demand of the jth customer among the customers corresponding to the second target automobile parts demand data, and m represents the total number of customers corresponding to the determined automobile parts demand in the second target automobile parts demand data;

[0130] Remember T y ={t y1 ,t y2 ,...,t yj ,...,t ym} is the second constraint time data set corresponding to the second target automobile parts demand data, where t yj Indicates the agreed completion time of the demand of the j-th customer among the customers corresponding to the second target automobile parts demand data.

[0131] The second planning and scheduling algorithm includes establishing a second objective function and a second preset constraint condition. The second objective function is expressed as follows:

[0132]

[0133] Among them, c i is the basic cost of producing the i-th customer parts in the first target automobile parts demand data, x i It is represented as the demand of the i-th customer in the first target automobile parts demand data, o i is the overtime production cost coefficient of the i-th customer parts in the first target automobile parts demand data, y i Indicates whether the i-th customer parts in the first target automobile parts demand data are produced overtime, and the value is 0 or 1. If it is overtime production, the value is 1, otherwise it is 0. i is the penalty cost for delayed delivery in the first target automotive parts demand data, z i Indicates whether the delivery of the i-th customer parts in the first target automobile parts demand data is delayed, and the value is 0 or 1. If it is delayed, the value is 1, otherwise it is 0;

[0134] c j is the basic cost of producing the jth customer parts in the second target automobile parts demand data, x yjIt is represented as the demand of the jth customer in the second target automobile parts demand data, o j is the overtime production cost coefficient of the jth customer parts in the second target automobile parts demand data, y j Indicates whether the j-th customer parts in the second target automobile parts demand data are produced overtime, and the value is 0 or 1. If it is overtime production, the value is 1, otherwise it is 0. j is the penalty cost for delayed delivery in the second target automobile parts demand data, z j Indicates whether the j-th customer parts in the second target automobile parts demand data are delayed in delivery, and the value is 0 or 1. If the delivery is delayed, the value is 1, otherwise it is 0;

[0135] Furthermore, the second preset constraint condition includes demand satisfaction constraint, time window constraint, and production rate and resource constraint, which are expressed as follows:

[0136] The requirement satisfaction constraint is expressed as:

[0137]

[0138] Among them, C L It represents the production volume calculation formula;

[0139] It should be noted that the time window constraint and the production rate and resource constraint are the same as the first preset constraint condition.

[0140] It should be noted that by defining the matching degree between workers and tasks, it is possible to more accurately assess the adaptability and ability of workers to tasks, thereby ensuring more reasonable and efficient task allocation. This helps reduce production delays and quality issues caused by worker skill mismatch or task overload, and improves overall work efficiency.

[0141] It should be noted that the use of task priority adjustment logic can ensure that high-priority tasks are handled first. This helps to meet key factors such as urgent delivery time and important customer needs, thereby maintaining the stability and flexibility of the production schedule. At the same time, by minimizing the sum of the weighted priorities of the assigned worker tasks, task allocation can be further optimized to reduce resource waste and cost expenditure.

[0142] It should be noted that the establishment of the second workshop production schedule strategy, combined with the second planning and scheduling algorithm, can better cope with the changes in the demand for new auto parts. This helps to ensure the flexibility and adaptability of the production plan to meet the rapid changes in market demand. At the same time, by optimizing the objective function and constraints, it is possible to reduce production costs and shorten delivery time.

[0143] S104: If not satisfied, the second workshop production schedule strategy and the preset first resource allocation logic are used to control the workshop production schedule.

[0144] It should be noted that when the second target automobile parts demand is greater than the first preset demand within the first preset time control threshold, it is determined whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy, and this step is recorded as the first judgment;

[0145] When the second target automobile parts demand quantity is not greater than the first preset demand quantity within the first preset time control threshold, and the second constraint time corresponding to the second target automobile parts demand quantity data is greater than the first preset time control threshold, judging whether the second target automobile parts demand quantity data and the second constraint time corresponding to the second target automobile parts demand quantity data meet the workshop production schedule according to the first workshop production schedule strategy, and this step is recorded as the second judgment;

[0146] When the customer corresponding to the second target demand for automobile parts is a customer in the first preset customer list, judging whether the second target demand for automobile parts and the second constraint time corresponding to the second target demand for automobile parts meet the workshop production schedule according to the first workshop production schedule strategy, this step is recorded as the third judgment;

[0147] In this embodiment, the first preset customer list is a list artificially divided according to the importance of users.

[0148] In an optional embodiment, it is determined whether the Nth target automobile parts demand data and the Nth constraint time corresponding to the Nth target automobile parts demand data meet the workshop production schedule according to the N-1th workshop production schedule strategy. If so, the workshop production schedule control is performed using the N-1th workshop production schedule strategy and the preset first resource allocation logic;

[0149] If not, the production schedule strategy of the Nth workshop and the preset first resource allocation logic are used to control the production schedule of the workshop.

[0150] In an optional embodiment, when it is determined that the next step of workshop production schedule strategy judgment needs to be performed based on the first judgment, the second judgment and the third judgment, the third target automobile parts demand data and the third constraint time corresponding to the third target automobile parts demand data meet the workshop production schedule are judged according to the second workshop production schedule strategy. Similarly, according to this logic, the N-1 workshop production schedule strategy is used to judge whether the N-th target automobile parts demand data and the N-th constraint time corresponding to the N-th target automobile parts demand data meet the workshop production schedule. If they meet, the N-1 workshop production schedule strategy and the preset first resource allocation logic are used to control the workshop production schedule; if not, the N-1 workshop production schedule strategy and the preset first resource allocation logic are used to control the workshop production schedule.

[0151] In an optional embodiment, a production schedule strategy for the Nth workshop is established based on the N-1th target automobile parts demand data, the N-1th constraint time corresponding to the N-1th target automobile parts demand data, the Nth target automobile parts demand data, and the Nth constraint time corresponding to the Nth target automobile parts demand data, combined with the Nth planning and scheduling algorithm.

[0152] In an optional embodiment, the Nth planning and scheduling algorithm includes establishing an Nth objective function and an Nth preset constraint condition, and the second objective function is expressed as follows: N ={x N1 ,x N2 ,...,x Nh ,...,x NH} is the second target automobile parts demand data set, where x Nh represents the demand of the hth customer among the customers corresponding to the second target automobile parts demand data, and H represents the total number of customers corresponding to the determined automobile parts demand in the second target automobile parts demand data set;

[0153] Remember T N ={t N1 ,t N2 ,...,t Nh ,...,t NH} is the second constraint time data set corresponding to the second target automobile parts demand data, where t Nh Indicates the agreed completion time of the demand of the hth customer among the customers corresponding to the second target automobile parts demand data.

[0154]

[0155] Among them, c i is the basic cost of producing the i-th customer parts in the first target automobile parts demand data, xi It is represented as the demand of the i-th customer in the first target automobile parts demand data, o i is the overtime production cost coefficient of the i-th customer parts in the first target automobile parts demand data, y i Indicates whether the i-th customer parts in the first target automobile parts demand data are produced overtime, and the value is 0 or 1. If it is overtime production, the value is 1, otherwise it is 0. i is the penalty cost for delayed delivery in the first target automotive parts demand data, z i Indicates whether the delivery of the i-th customer parts in the first target automobile parts demand data is delayed, and the value is 0 or 1. If it is delayed, the value is 1, otherwise it is 0;

[0156] c j is the basic cost of producing the jth customer parts in the second target automobile parts demand data, x yj It is represented as the demand of the jth customer in the second target automobile parts demand data, o j is the overtime production cost coefficient of the jth customer parts in the second target automobile parts demand data, y j Indicates whether the j-th customer parts in the second target automobile parts demand data are produced overtime, and the value is 0 or 1. If it is overtime production, the value is 1, otherwise it is 0. j is the penalty cost for delayed delivery in the second target automobile parts demand data, z j Indicates whether the j-th customer parts in the second target automobile parts demand data are delayed in delivery, and the value is 0 or 1. If the delivery is delayed, the value is 1, otherwise it is 0;

[0157] c h is the basic cost of producing the hth customer parts in the Nth target automobile parts demand data, x Nh It is represented by the demand of the hth customer in the demand data of the Nth target automobile parts, o h is the overtime production cost coefficient of the hth customer parts in the Nth target automobile parts demand data, y h Indicates whether the h-th customer parts in the N-th target automobile parts demand data have overtime production, and the value is 0 or 1. If it is overtime production, the value is 1, otherwise it is 0. h is the penalty cost for delayed delivery in the demand data of the Nth target automobile parts, z h Indicates whether the h-th customer's parts in the N-th target automobile parts demand data are delayed in delivery, and the value is 0 or 1. If the delivery is delayed, the value is 1, otherwise it is 0;

[0158] Furthermore, the Nth preset constraint condition includes demand satisfaction constraint, time window constraint, and production rate and resource constraint, which are expressed as follows:

[0159] The requirement satisfaction constraint is expressed as:

[0160]

[0161] Among them, C L It represents the production volume calculation formula;

[0162] It should be noted that the time window constraint and the production rate and resource constraint are the same as the first preset constraint condition.

[0163] In summary, the present invention proposes a workshop production schedule control method, obtains the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data, the first target automobile parts demand data is the currently determined automobile parts demand data; according to the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data, combined with the first planning and scheduling algorithm, establishes the first workshop production schedule strategy; according to the first workshop production schedule strategy, judges whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule, if satisfied, uses the first workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule; if not satisfied, uses the second workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule. The present invention can flexibly adjust the workshop production schedule according to the changing needs of customers, ensure production efficiency and product quality, solve the problems existing in the prior art, and has broad application prospects.

[0164] This embodiment also provides a workshop production progress control system, including:

[0165] A data acquisition module, used to acquire first target automobile parts demand data and a first constraint time corresponding to the first target automobile parts demand data, wherein the first target automobile parts demand data is currently determined automobile parts demand data;

[0166] A schedule strategy acquisition module, used to establish a first workshop production schedule strategy based on the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data in combination with the first planning and scheduling algorithm;

[0167] A judgment module, used for judging whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy, and if so, using the first workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule;

[0168] The control module is used to use the second workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule if the conditions are not met.

[0169] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0170] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a workshop production schedule control method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0171] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0172] Acquire first target automobile parts demand quantity data and a first constraint time corresponding to the first target automobile parts demand quantity data, wherein the first target automobile parts demand quantity data is currently determined automobile parts demand quantity data;

[0173] Establishing a first workshop production schedule strategy based on the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data in combination with the first planning and scheduling algorithm;

[0174] Determine whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy, and if so, use the first workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule;

[0175] If not, the second workshop production schedule strategy and the preset first resource allocation logic are used to control the workshop production schedule.

[0176] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0177] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0178] 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.

[0179] 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.

[0180] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0181] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0182] 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 workshop production progress control method, characterized in that: include: Acquire first target automobile parts demand quantity data and a first constraint time corresponding to the first target automobile parts demand quantity data, wherein the first target automobile parts demand quantity data is currently determined automobile parts demand quantity data; Establishing a first workshop production schedule strategy based on the first target automobile parts demand data and a first constraint time corresponding to the first target automobile parts demand data in combination with a first planning and scheduling algorithm; The step of obtaining the first target automobile parts demand quantity data and the first constraint time corresponding to the first target automobile parts demand quantity data comprises: remember is the first target automobile parts demand data set, where x i represents the demand of the i-th customer, and n represents the total number of customers corresponding to the currently determined demand for auto parts; remember is the first constraint time data set corresponding to the first target automobile parts demand data, where t i represents the agreed completion time of the demand of the i-th customer; The first planning and scheduling algorithm includes: The first planning and scheduling algorithm includes establishing a first objective function and a first preset constraint condition, wherein the first objective function is expressed as follows: ; Among them, c i is the basic cost of producing the i-th customer parts in the first target automobile parts demand data, x i It is represented as the demand of the i-th customer in the first target automobile parts demand data, o i is the overtime production cost coefficient of the i-th customer parts in the first target automobile parts demand data, y i Indicates whether the i-th customer parts in the first target automobile parts demand data are produced overtime, and the value is 0 or 1. If it is overtime production, the value is 1, otherwise it is 0. i is the penalty cost for delayed delivery in the first target automotive parts demand data, z i Indicates whether the delivery of the i-th customer parts in the first target automobile parts demand data is delayed, and the value is 0 or 1. If it is delayed, the value is 1, otherwise it is 0; The first preset constraint condition includes demand satisfaction constraint, time window constraint, and production rate and resource constraint, which are expressed as follows: The requirement satisfaction constraint is expressed as: ; Among them, S1 represents the time point when the production of the first customer parts in the first target automobile parts demand data begins. It indicates the production volume from the time when the first target automobile parts demand data is started to the agreed completion time of the first customer's demand. The production volume calculation formula is: Indicates the maximum agreed completion time of the customer's demand; The time window constraint is expressed as: ; Among them, S i is the time point when the production of the i-th customer parts begins, TS i is the time required to produce a single component, t i is the agreed completion time of the demand of the i-th customer; The production rate and resource constraints include normal production rate constraints, overtime production rate constraints and resource constraints, wherein the normal production rate constraints are: ; Among them, r i is the normal production speed when producing the i-th customer parts, L i is the number of man-hours available to produce the i-th customer component, M i is the number of available production lines when producing the i-th customer component; The overtime production rate constraint is: ; in, represents the overtime production volume when producing the i-th customer parts, is the overtime production speed when producing the i-th customer component, and , is the number of overtime hours in producing parts for the i-th customer; The resource constraints are: Maximum use of manpower and production lines: ,in, Indicates the maximum man-hours. Indicates the maximum number of production lines; Determine whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy, and if so, use the first workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule; If not, the second workshop production schedule strategy and the preset first resource allocation logic are used to control the workshop production schedule.

2. The workshop production progress control method according to claim 1, characterized in that: The production schedule strategy of the second workshop includes: Establishing a second workshop production schedule strategy based on the first target automobile parts demand data, the first constraint time corresponding to the first target automobile parts demand data, the second target automobile parts demand data, and the second constraint time corresponding to the second target automobile parts demand data in combination with a second planning and scheduling algorithm; The second target automobile parts demand data is the new automobile parts demand.

3. The workshop production progress control method according to claim 2, characterized in that: The step of judging whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy includes: Recombining the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data with the first target automobile parts demand data and the first constraint time corresponding to the first target automobile parts demand data; Determine whether the reorganized data meets the production schedule strategy of the first workshop. If so, the production schedule strategy of the first workshop determines whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule. If not satisfied, the second workshop production schedule strategy and the preset first resource allocation logic are used to control the workshop production schedule.

4. The workshop production progress control method according to claim 3, characterized in that: Also includes: When the second target automobile parts demand is greater than the first preset demand within the first preset time control threshold, judging whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy; When the second target automobile parts demand quantity is not greater than the first preset demand quantity within the first preset time control threshold, and the second constraint time corresponding to the second target automobile parts demand quantity data is greater than the first preset time control threshold, judging whether the second target automobile parts demand quantity data and the second constraint time corresponding to the second target automobile parts demand quantity data meet the workshop production schedule according to the first workshop production schedule strategy; When the customer corresponding to the second target automobile parts demand is a customer in the first preset customer list, the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy.

5. The workshop production progress control method according to claim 4, characterized in that: Also includes: According to the N-1th workshop production schedule strategy, determine whether the Nth target automobile parts demand data and the Nth constraint time corresponding to the Nth target automobile parts demand data meet the workshop production schedule; if so, use the N-1th workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule; If not, the production schedule strategy of the Nth workshop and the preset first resource allocation logic are used to control the production schedule of the workshop.

6. A system using the workshop production progress control method according to claim 1, characterized in that: include: A data acquisition module, used to acquire first target automobile parts demand data and a first constraint time corresponding to the first target automobile parts demand data, wherein the first target automobile parts demand data is currently determined automobile parts demand data; A schedule strategy acquisition module, configured to establish a first workshop production schedule strategy based on the first target automobile parts demand data and a first constraint time corresponding to the first target automobile parts demand data in combination with a first planning and scheduling algorithm; a judgment module, used for judging whether the second target automobile parts demand data and the second constraint time corresponding to the second target automobile parts demand data meet the workshop production schedule according to the first workshop production schedule strategy, and if so, using the first workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule; The control module is used to use the second workshop production schedule strategy and the preset first resource allocation logic to control the workshop production schedule if the conditions are not met.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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