A method and device for processing production scheduling data for the refrigerator industry

Abstract

The present invention provides a refrigerator industry production scheduling data processing method and device. First, a parameter calculation formula is used to calculate the specific values corresponding to the decision variables and each target constant based on the target constant and the decision variable. Then, based on the preset constraint conditions, each optimization target model generates a corresponding calculation result based on the specific values corresponding to the decision variables and each target constant. The calculation results of the optimization target model obtained under different combinations of decision variable values are analyzed, and the calculation results of the optimization target model that meets the preset conditions are used as the target calculation results. Then, the values of the decision variables corresponding to the target calculation results are extracted. At this time, the value of the decision variable is the target production plan, and the production line can be scheduled according to the target production plan.

Description

Technical Field

[0001] The present invention relates to the technical field of production management, and in particular to a method and device for processing production scheduling data for the refrigerator industry. Background Art

[0002] Production scheduling in the refrigerator industry involves determining the production quantities of various product models on each production line within a specific timeframe. Mixed-flow production is characterized by the simultaneous production of multiple product models on multiple lines. This requires considering factors such as production orders, factory line capacity, equipment and mold availability, and key material constraints. A scheduling method must be designed to maximize both order delivery rates and factory capacity utilization. Summary of the Invention

[0003] In view of this, an embodiment of the present invention provides a method and apparatus for processing production scheduling data for the refrigerator industry, so as to provide a scheduling method for improving factory capacity utilization.

[0004] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0005] A method for processing production scheduling data for the refrigerator industry, comprising:

[0006] Obtain target constants and decision variables for refrigerator production scheduling, where the target constants are constant data for the production thread and the decision variables are variable data in the production process that matches the order data.

[0007] Determining specific values of the decision variables under various target constants;

[0008] Obtaining an optimization target model created based on a mapping relationship between the decision variables and the total number of order delays and the number of mold changes;

[0009] Substituting the specific values of the decision variables under each target constant into an optimization target model, the optimization target model including: a first model for calculating the total number of order delays based on the specific values of the decision variables under each target constant, and a second model for calculating the number of mold changes in a refrigerator foaming process based on the specific values of the decision variables under each target constant;

[0010] Solving the optimization target model based on the specific values of the decision variables under each target constant and the preset constraint conditions to obtain calculation results of the target optimization model under various specific value combinations of the decision variables under each target constant;

[0011] A calculation result that meets preset conditions is obtained as a target calculation result, and specific values of the mold hanging state and the fixture mold changing order state in the production thread in the decision variables corresponding to the target calculation result are output as target variables.

[0012] Optionally, in the above refrigerator industry production scheduling data processing method, the decision variables include:

[0013] A combination of one or more of the following: model production, mold production, order production, order extension quantity, order extension status, daily cumulative order extension quantity, mold production time, mold hanging status, and fixture change sequence status.

[0014] Optionally, the optimization target model also includes:

[0015] The third model is used to calculate the daily delay penalty for orders based on the specific values of the decision variables under various target constants.

[0016] Optionally, in the above refrigerator industry production scheduling data processing method, the first model is specifically used to calculate the total number of delayed orders based on the postponement status of each order;

[0017] The second model is specifically used to calculate the total number of mold changes based on the absolute value of the difference between the idle states of the mold on two consecutive days;

[0018] The third model is specifically used to calculate the daily delay penalty for the total orders based on the daily order delay penalty coefficient corresponding to each order and the daily cumulative delay amount of the orders.

[0019] Optionally, in the refrigerator industry production scheduling data processing method, the obtaining of a calculation result that satisfies a preset condition as a target calculation result includes:

[0020] The calculation results of the first model, the second model and the third model are multiplied by their corresponding weight coefficients, and the sum of the calculation results of the first model, the second model and the third model after adjustment of the weight coefficients is calculated as the candidate result, and the candidate result with the minimum value is used as the target calculation result.

[0021] Optionally, in the above refrigerator industry production scheduling data processing method, the constraint condition includes one or more of the following combinations:

[0022] The required quantity of an order is equal to the actual quantity of the order plus the delay of the order:

[0023] The sum of the cumulative planned quantity and cumulative delay of orders as of a certain day is not less than the cumulative demand for the orders;

[0024] The order quantity is equal to the model quantity;

[0025] Model production is equal to mold production;

[0026] The mold production output is equal to the mold production time multiplied by the cycle time;

[0027] The daily output of the line body cannot exceed the upper limit of the line body;

[0028] Mold output cannot exceed the theoretical output limit;

[0029] The sum of the actual production time of the mold on a single fixture in a single day and the mold change loss time cannot exceed the upper limit of the production time for that day;

[0030] The order of hanging molds on a single fixture on a single day is unique;

[0031] The relationship between the mold hanging order of a single fixture on a single day and the mold hanging status in the order:

[0032] Only one mold can be mounted on a single fixture in a single day and in a single order;

[0033] The mold hanging status must be consistent between two consecutive days;

[0034] The total usage time of the mold cannot exceed the theoretical upper limit.

[0035] A refrigerator industry production scheduling data processing device, comprising:

[0036] A parameter acquisition unit is used to acquire target constants and decision variables for refrigerator production scheduling, wherein the target constants are constant data of the production thread, and the decision variables are variable data in the production process that matches the order data;

[0037] A parameter calculation unit, used to determine the specific value of the decision variable under each target constant;

[0038] a model calculation unit, configured to obtain an optimization target model created based on a mapping relationship between the decision variables and the total number of order delays and the number of mold changes, and substitute specific values of the decision variables under each target constant into the optimization target model, the optimization target model comprising: a first model for calculating the total number of order delays based on the specific values of the decision variables under each target constant, and a second model for calculating the number of mold changes in a refrigerator foaming process based on the specific values of the decision variables under each target constant;

[0039] A decision variable selection unit is used to solve the optimization target model based on the specific values of the decision variables under various target constants and preset constraints, and obtain the calculation results of the target optimization model under various combinations of specific values of the decision variables under various target constants; obtain the calculation results that meet the preset conditions as the target calculation results, obtain the calculation results that meet the preset conditions as the target calculation results, and output the specific values of the mold hanging state and the fixture mold changing order state in the production thread in the decision variables corresponding to the target calculation results as target variables.

[0040] Optionally, in the above-mentioned refrigerator industry production scheduling data processing device, the decision variables include:

[0041] A combination of one or more of the following: model production volume, mold production volume, order production volume, total order delays, order delay status, cumulative daily order delays, mold production duration, mold hanging status, and fixture change sequence status. Optionally, in the refrigerator industry production scheduling data processing device, the first model is specifically configured to calculate the total number of delayed orders based on the delay status of each order.

[0042] The optimization target model also includes:

[0043] The third model is used to calculate the daily delay penalty for orders based on the specific values of the decision variables under various target constants.

[0044] The first model is specifically used to calculate the total number of delayed orders based on the postponement status of each order;

[0045] The second model is specifically used to calculate the total number of mold changes based on the absolute value of the difference between the idle states of the mold on two consecutive days;

[0046] The second model is specifically used to calculate the daily delay penalty for the total orders based on the daily order delay penalty coefficient corresponding to each order and the daily cumulative delay amount of the orders.

[0047] Optionally, in the above-mentioned refrigerator industry production scheduling data processing device, the decision variable selection unit is specifically used to:

[0048] The calculation results of the first model, the second model and the third model are multiplied by their corresponding weight coefficients, and the sum of the calculation results of the first model, the second model and the third model after adjustment of the weight coefficients is calculated as the candidate result, and the minimum candidate result is used as the target calculation result.

[0049] Optionally, in the above-mentioned refrigerator industry production scheduling data processing device, the constraint condition includes one or more of the following combinations:

[0050] The required quantity of an order is equal to the actual quantity of the order plus the delay of the order:

[0051] The sum of the cumulative planned quantity and cumulative delay of orders as of a certain day is not less than the cumulative demand for the orders;

[0052] The order quantity is equal to the model quantity;

[0053] Model production is equal to mold production;

[0054] The mold production output is equal to the mold production time multiplied by the cycle time;

[0055] The daily output of the line body cannot exceed the upper limit of the line body;

[0056] Mold output cannot exceed the theoretical output limit;

[0057] The sum of the actual production time of the mold on a single fixture in a single day and the mold change loss time cannot exceed the upper limit of the production time for that day;

[0058] The order of hanging molds on a single fixture on a single day is unique;

[0059] The relationship between the mold hanging order of a single fixture on a single day and the mold hanging status in the order:

[0060] Only one mold can be mounted on a single fixture in a single day and in a single order;

[0061] The mold hanging status must be consistent between two consecutive days;

[0062] The total usage time of the mold cannot exceed the theoretical upper limit.

[0063] Based on the above technical solution, in the above solution provided by the embodiment of the present invention, the parameter calculation formula will first be used to calculate the specific values corresponding to the decision variables and each target constant based on the target constant and the decision variable. The specific values corresponding to the decision variables and each target constant are then based on the preset constraints. Each of the optimization target models will generate corresponding calculation results based on the specific values corresponding to the decision variables and each target constant. By analyzing the calculation results of the optimization target models obtained under different combinations of decision variables, the calculation results of the optimization target models that meet the preset conditions are used as the target calculation results, and then the values of each decision variable corresponding to the target calculation results are extracted. At this time, the value of the decision variable is the target production plan, and the production line can be scheduled according to the target production plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0065] Figure 1 The main flow chart for refrigerator production;

[0066] Figure 2 This is the box foaming structure diagram;

[0067] Figure 3This is a flow chart of the refrigerator industry production scheduling data processing method disclosed in an embodiment of the present application;

[0068] Figure 4 This is a structural diagram of the refrigerator industry production scheduling data processing device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0070] The main processes of refrigerator production are as follows: Figure 1 As shown. The general process of refrigerator production is divided into 4 parts: pre-process process 1, cabinet foaming process 2, door foaming process 3 and final assembly process 4. The process 1 mainly involves the processing and modular assembly of the main key parts of the refrigerator door. The process 2 includes placing the assembled cabinet into the corresponding mold, and then heating, injecting materials, and foaming the foaming mold. Its characteristics are slow production rhythm and long foaming mold replacement time. The process 3 is the foaming process of the door. The process 4 is the assembly of the cabinet door after foaming. Comparing these four processes, process 2 is a bottleneck process.

[0071] The core of refrigerator industry production scheduling is to develop a production time sequence plan for bottleneck process 2, thereby increasing the production capacity of finished product assembly by maximizing the capacity of the bottleneck process. The cabinet mold foaming process includes several foaming lines, coded in the format of L1, L2, ...; each foaming line contains a certain number of fixtures, coded in the format of P1, P2, ...; cabinet foaming requires related foaming molds / equipment, coded in the format of M1, M2, .... Figure 2 As shown, 2-1 represents a foaming line, 2-2 represents a fixture on the foaming line, and 2-3 represents a mold mounted in the fixture. Both fixtures and molds are scarce resources. Without considering fixture failure, the fixtures can be put into production at the same time, but a fixture can only mount one mold at the same time. There is no special difference between the fixtures on the same line. The molds put into production at the same time can only produce products of the model corresponding to their respective molds. However, a single mold can only produce one box at the same time. At the same time, it should be taken into account that switching the mold on the fixture takes time, that is, the fixture and related molds cannot produce products during this time period. Therefore, the core problem to be solved by the patent of this invention is to take into account the order delivery time and the minimum mold switching loss cost while considering the precise time loss of mold switching, that is, to take into account the order delivery time and the highest production line utilization rate while considering the precise time loss of mold switching.

[0072] In order to better describe how this patent calculates the precise loss time of mold switching on the foaming line, a timing scheduling status table describing the mold is first designed, as shown in Table 1. Table 1 is a line-fixture-date-mold relationship table.

[0073]

[0074] Table 1

[0075] Assume that each fixture has a maximum of n-1 (n >= 2) mold hanging sequences per day, meaning at least one sequence. This can be manually set during scheduling based on actual conditions. As previously mentioned, each fixture can only have one mold in production per sequence. For example, for fixture P1 on day 1 in the table, production of mold M1 is scheduled first (this can also be understood as the initial mold loading state), followed by production of mold M3 in sequences 2 and 3. It's important to note that the mold in the last sequence of the day remains the same as the mold in the first sequence of the second day.

[0076] Table 2 shows the time loss associated with switching between molds, known as the mold change loss time matrix. For example, switching from mold M1 to mold M2 takes 5 minutes, while switching from mold M2 to mold M1 takes 10 minutes. This is because mold changes involve two steps: unloading and reloading, and the time required for each step can vary for different molds. For the example shown in Table 1, the mold change loss for fixture P1 on the first day is: 10 * S (M1->M3) + 15 * S (M3->M4) = 25. Each fixture has a unique mold production sequence on each day, and therefore, the mold change loss time is unique.

[0077] M1 M2 M3 M4 M5 M6 M1 0 5 10 5 10 15 M2 10 0 5 10 15 10 M3 7 6 0 15 10 10 M4 10 12 15 0 15 10 M5 15 11 13 10 0 15 M6 8 15 15 15 20 0

[0078] Table 2

[0079] Based on the above background, this application discloses a method and device for processing production scheduling data for the refrigerator industry, see Figure 3 , the method comprising:

[0080] Step S101: Obtain target constants and decision variables for refrigerator production scheduling;

[0081] The target constant is constant data of the production thread, and the decision variable is variable data in the production process that matches the order data. The specific value of the decision variable varies with the target constant.

[0082] In this solution, the specific selection of the target constant can be set based on user needs. For example, in the technical solution disclosed in the embodiment of this application, the target constant includes a combination of one or more of the following parameters:

[0083] Foaming line code, scheduling calendar code, line production time, line production capacity, fixture code, maximum number of mold changes on the fixture, product model code, foaming mold code, number of molds, mold cycle, mold model corresponding production status, order code, order quantity, order delivery status, order delay penalty coefficient, mold change sequence set, and mold change loss time;

[0084] Specifically:

[0085] Foaming line body code L l , where l = 1, 2, 3...

[0086] Scheduling calendar code D d , where d = 1, 2, 3, etc. For example, D2 represents the second day in the production scheduling period;

[0087] Line production time T l,d , indicating the foaming line body L l In the scheduling calendar D d Production time per day;

[0088] Line production capacity L_capacity l , indicating the linear foaming line L l Maximum daily output;

[0089] Fixture code P l,p , p=1,2,3……,P 2,3 Indicates the third clamp on line L2;

[0090] The maximum number of mold changes on the fixture is N ≥ 2;

[0091] Product Model Code X x , x=1,2,3……;

[0092] Foaming mold code M m , m=1,2,3……;

[0093] The number of molds M_num m , indicating mold M m the quantity available;

[0094] M_beat m , indicating mold M m Hourly output, unit: units / hour;

[0095] Mold model corresponds to production status M2X m,x ∈[0,1], such as M2X m,x If it is 1, it means mold M m Can produce model X x, if M2X m,x If it is 0, it means M m Model X cannot be produced x ;

[0096] Order code O x,j , j=1,2,3……such as O 2，3 Indicates the third order corresponding to model X2;

[0097] Order quantity O_q x,j Indicates order O x,j The corresponding demand quantity;

[0098] Order delivery status O_day x,j,d , for example O x，j In the D d 1 if delivery is within 2 days, 0 otherwise;

[0099] Order delay penalty coefficient O_pemalty x,j,d , indicating order O i，j In D k The penalty coefficient should reflect the severity of the penalty for order type and time delay. For example, if an order can be fully produced before the delivery date, the penalty coefficient is 0. For example, if there is a two-day delay, the penalty coefficient is equal to the production volume of the first day multiplied by 2 plus the production volume of the second day multiplied by 4, where 2 and 4 are the penalty coefficients. For orders of different priorities, slight adjustments can be made based on the time penalty coefficient.

[0100] The mold change sequence set M_sequence is deterministic because the number of mold changes n per fixture per day is fixed for a fixed algorithm. For example, if there are a maximum of three mold change sequences per fixture per day and there are six types of molds, then the number of elements in the set is 6*6*6=216, meaning that the mold production sequence on a selected fixture is one of 216 possible scenarios.

[0101] Mold change loss time M_change_cost e , e∈M_sequgnce represents the die change time loss corresponding to an element in M_sequence.

[0102] Of course, the target constant may also include other parameters, and the above example is only a specific example provided in this application.

[0103] The parameters specifically included in the decision variables may also be set based on user needs. For example, the decision variables may include a combination of one or more of the following parameters:

[0104] Model production schedule, mold production schedule, order production schedule, total order delay, total order delay status, daily cumulative order delay, mold production time, mold hanging status, and fixture change sequence status;

[0105] Specifically:

[0106] Model production volume X_Q x，l，d , where x = 1, 2, 3, ...; l = 1, 2, 3, ...; d = 1, 2, 3, ..., represents model X x Online L l D d daily output;

[0107] Mold production M_Q m，l，d , where m=1, 2, 3, ...; l=1, 2, 3, ...; d=1, 2, 3, ..., represents the mold M m Online L l D d daily output;

[0108] Order quantity O_Q x，j，d , where x=1,2,3,...; j=1,2,3,...; d=1,2,3,..., represents order O x，j In D d daily output;

[0109] Total order delay amount O_Delay x，j , where x=1, 2, 3, ...; j=1, 2, 3, ..., represents order O x，j The total delay in the entire production scheduling cycle;

[0110] Total order delay status O_Delay_Status x，j , where x=1, 2, 3, ...; j=1, 2, 3, ..., are binary [0, 1] variables. If O_Delay x，j If it is greater than 0, it is 1, otherwise it is 0;

[0111] The cumulative delay of orders per day O_Day_Delay x，j，d , where x=1,2,3,...; j=1,2,3,...; d=1,2,3,..., represents order O x，j In D d The amount of extension in days;

[0112] Mold production time M_T m，l，p，d , where m=1, 2, 3, ...; l=1, 2, 3, ...; p=1, 2, 3, ...; d=1, 2, 3, ..., represents the mold M mOnline L l Fixture P on p Output on the previous day Dd;

[0113] Mold hanging status M_S m，l，p，d，i , where m=1,2,...; l=1,2,...; p=1,2,...; d=1,2,..., i=1,2,..., binary [0,1] variables, representing mold M m Online L l On D d Is the i-th order of the day hung on the fixture P p superior;

[0114] Fixture (mold position) mold change sequence status P_S l，p，d，e , where l = 1, 2, ...; p = 1, 2, ...; d = 1, 2, ...; e∈M_sequence represents the possible die hanging sequence enumeration values of the current die position, and each enumeration value corresponds to a unique die change loss time.

[0115] The decision variable is a variable parameter, and the specific value of the decision variable changes with the value of the target constant in the decision variable. Different values of the target constant have different specific values of the decision variable.

[0116] During the specific design, the specific parameters included in the target constant and decision variables can be set based on user needs.

[0117] Step S102: determining the specific values of the decision variables under each target constant;

[0118] In this solution, in this solution, the value of the identifier of the target constant in the decision variable is different, and the value of the decision variable is also different. The values of these decision variables can be directly obtained from the production database. For example, different foaming line body codes, scheduling calendar codes, line body production time, line body production capacity, fixture codes and other target constants have different values, and the values of decision variables such as model scheduling output, mold scheduling output, order scheduling output, total order delay amount, etc. are also different. This application can substitute different target constants for the decision variables, and then determine the specific values corresponding to the decision variables and each target constant from the database.

[0119] Step S103: Solving the optimization target model based on the specific values of the decision variables under each target constant and the preset constraint conditions to obtain calculation results of the target optimization model under various combinations of specific values corresponding to the decision variables and each target constant;

[0120] The optimization objective model includes: a first model for calculating the total number of order delays based on the specific values corresponding to the decision variables and each target constant; a second model for calculating the number of mold changes based on the specific values corresponding to the decision variables and each target constant; and a third model for calculating the daily order delay penalty based on the specific values corresponding to the decision variables and each target constant. Specifically, the first model is specifically used to calculate the total number of refrigerator production order delays based on the specific values of the decision variables under each target constant; the second model is specifically used to calculate the number of mold changes in the refrigerator foaming process based on the specific values of the decision variables under each target constant; and the third model is specifically used to calculate the daily order delay penalty based on the specific values of the decision variables under each target constant.

[0121] The variable parameters in the optimization target model are decision variables. Under different specific values of the decision variables, the optimization target model will obtain different calculation results. The optimization target model is used to calculate the specific values corresponding to the decision variables and each target constant based on preset constraints, and obtain the calculation results of the optimization target model under various combinations of specific values corresponding to the decision variables and each target constant.

[0122] Here, the preset constraint condition is a constraint condition that constrains the decision variable.

[0123] Step S104: Obtain the calculation result of the target optimization model that meets the preset conditions as the target calculation result, and output the specific values of the mold hanging state and the fixture mold changing order state in the production thread in the decision variables corresponding to the target calculation result as target variables.

[0124] In this solution, based on the preset constraints, each of the optimization target models will generate corresponding calculation results based on the specific values corresponding to the decision variables and each target constant. The calculation results of the optimization target model obtained under different combinations of decision variable values are analyzed, and the calculation results of the optimization target model that meets the preset conditions are used as the target calculation results. The values of the decision variables corresponding to the target calculation results are then extracted. At this time, the value of the decision variable is the target production plan, and the production line can be scheduled according to the target production plan.

[0125] In the technical solution disclosed in the embodiments of the present application, the specific constraint objects in the constraint conditions can be selected based on user needs. For example, the constraint objects in the constraint conditions may include:

[0126] O_Delay_Status x，j , where x represents the order model, j represents the order code, O_Delay_Statusx，j Indicates the total deferred status of orders with model x and code j;

[0127] O_penalty x，j，d , d represents the Dth d O_penalty x，j，d Indicates order O with model x and code j x，j In D d Day penalty coefficient;

[0128] O_Day_Delay x，j，d , O_Day_Delay x，j，d Indicates order O with model x and code j x，j In D d The amount of extension in days;

[0129] M_S m，l，p，d，i , the m represents the foaming mold code, l represents the foaming line code, i represents the order, M_S m，l，p，d，i Indicates that the code is M m Foaming mold online body L l On D d Is the i-th order of the day hung on the fixture P p When hung on the fixture P p When going up, M_S m，l，p，d，i The value of is 1, otherwise, the value is 0;

[0130] The constraints include one or more of the following:

[0131] The required quantity of an order is equal to the actual quantity of the order plus the delay of the order:

[0132]

[0133] Among them, the O_Q x，j，d For order O x，j In the D d The actual output of the day, the O_Delay x，j For order O x，j The delay amount, the O_q x，j For order O x，j The required production volume;

[0134] The sum of the cumulative planned quantity and cumulative delay of orders as of a certain day is not less than the cumulative demand for the orders;

[0135]

[0136] O_Q x，j，d1 For order O x，j In the D d1The cumulative planned order quantity for the day, the O_Day_Delay x，j，d For order O x，j In the D d Cumulative delay of days For order O x，j The cumulative demand for

[0137] The order quantity is equal to the model quantity;

[0138]

[0139] Among them, X_Q x，l，d Indicates model X x Order online l D d Days production, the OQ x，j，d Indicates order O x，j In the D d daily output;

[0140] Model production is equal to mold production;

[0141]

[0142] Among them, MQ m，l，d Indicates mold M m Online L l D d days of output, the M2X m，x Indicates the mold model corresponding to the production status, XQ x，l，d Indicates model X x Online L l D d daily output;

[0143] The mold production output is equal to the mold production time multiplied by the cycle time;

[0144]

[0145] The daily output of the line body cannot exceed the upper limit of the line body;

[0146]

[0147] Mold output cannot exceed the theoretical output limit;

[0148] M_Q m，l，d ≤M_num m *M_beat m *T l，d ;

[0149] The sum of the actual production time of the mold on a single fixture in a single day and the mold change time loss cannot exceed the upper limit of the production time on that day;

[0150]

[0151] The order of hanging molds on a single fixture on a single day is unique;

[0152]

[0153] The relationship between the mold hanging order of a single fixture on a single day and the mold hanging status in the order:

[0154]

[0155] Only one mold can be mounted on a single fixture in a single day and in a single order;

[0156]

[0157] The mold hanging status must be consistent between two consecutive days;

[0158] M_S m，l，p，d-1，N =M_S m，l，p，d，0 , d≥2;

[0159] The total usage time of the mold cannot exceed the theoretical upper limit;

[0160]

[0161] In the technical solution disclosed in the embodiment of the present application, corresponding to the above-mentioned constraint objects, a specific calculation formula for the optimization target model is also disclosed. Specifically,

[0162] The first model is

[0163] The second model is,

[0164] The third model is,

[0165] In this solution, corresponding to the above-mentioned optimization target model, in the technical solution disclosed in the embodiment of this application, a calculation result that meets the preset conditions is obtained as the target calculation result, wherein the type of the preset conditions can be set according to user needs. In the technical solution disclosed in this embodiment, the calculation result that meets the preset conditions is obtained as the target calculation result. Specifically, it can include: multiplying the calculation results of the first model and the second model by the corresponding weight coefficients, and calculating the sum of the calculation results of the first model and the second model after the weight coefficients are adjusted as the candidate result, and taking the minimum candidate result as the target calculation result. Or, multiplying the calculation results of the first model, the second model and the third model by the corresponding weight coefficients, and calculating the sum of the calculation results of the first model, the second model and the third model after the weight coefficients are adjusted as the candidate result, and taking the minimum candidate result as the target calculation result.

[0166] In the technical solution disclosed in another embodiment of the present application, in order to facilitate machine processing, the present application discloses a specific calculation formula of a preset condition. Specifically, obtaining a calculation result that meets the preset condition as a target calculation result specifically includes:

[0167] Substitute the calculation results of the optimization target model corresponding to each decision variable into the formula

[0168] obj_total=c1*obj_order_delay+c2*obj_order_day_delay+c3*obj_mould_exchange_count

[0169] , the calculation result of the corresponding optimization target model when the value of obj_total is the smallest is used as the target calculation result, wherein c1, c2, and c3 are preset weight coefficients. The values of c1, c2, and c3 can be set according to specific needs.

[0170] In this embodiment, corresponding to the above method, a refrigerator industry production scheduling data processing device is also disclosed. For the specific working content of each unit in the device, please refer to the content of the above method embodiment. The refrigerator industry production scheduling data processing device provided by the embodiment of the present invention is described below. The refrigerator industry production scheduling data processing device described below and the refrigerator industry production scheduling data processing method described above can be referenced to each other.

[0171] See also Figure 4 The refrigerator industry production scheduling data processing device disclosed in the embodiment of the present application may include:

[0172] Parameter acquisition unit 100, which corresponds to step S101 in the above method, is used to obtain target constants and decision variables for refrigerator production scheduling, wherein the target constants are constant data of the production thread, and the decision variables are variable data in the production process that matches the order data;

[0173] The parameter calculation unit 200 corresponds to step S102 in the above method and is used to determine the specific value of the decision variable under each target constant;

[0174] The model calculation unit 300 corresponds to step S103 in the above method, and is used to obtain an optimization target model created based on the mapping relationship between the decision variable and the total number of order delays and the number of mold changes, and substitute the specific values of the decision variables under each target constant into the optimization target model. The optimization target model includes: a first model for calculating the total number of order delays based on the specific values of the decision variables under each target constant, and a second model for calculating the number of mold changes in the refrigerator foaming process based on the specific values of the decision variables under each target constant.

[0175] The decision variable selection unit 400 corresponds to step S104 in the above method, and is used to solve the optimization target model based on the specific values of the decision variables under each target constant and the preset constraints, and obtain the calculation results of the target optimization model under various combinations of specific values of the decision variables under each target constant; obtain the calculation results that meet the preset conditions as the target calculation results, obtain the calculation results that meet the preset conditions as the target calculation results, and output the specific values of the mold hanging status and the fixture mold changing order status in the production thread in the decision variables corresponding to the target calculation results as target variables.

[0176] The optimization target model also includes:

[0177] The third model is used to calculate the daily delay penalty for orders based on the specific values of the decision variables under various target constants.

[0178] In the above-mentioned device, the specific value of the decision variable corresponding to the target calculation result is output as the target variable. It should be noted that the scheme disclosed in the above-mentioned embodiment of the present application can be applied not only to the scheduling planning of refrigerator production, but also to the scheduling planning of other products.

[0179] For the specific working contents of each unit in the refrigerator industry production scheduling data processing device, please refer to the above method introduction, which will not be repeated here one by one.

[0180] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0181] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0182] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0183] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0184] It should also be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0185] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A refrigerator industry production scheduling data processing method, characterized in that: include: Obtain target constants and decision variables for refrigerator production scheduling, where the target constants are constant data for the production thread and the decision variables are variable data in the production process that matches the order data. Determining specific values of the decision variables under various target constants; Obtaining an optimization target model created based on a mapping relationship between the decision variables and the total number of order delays and the number of mold changes; Substituting specific values of the decision variables under various target constants into an optimization target model, wherein the target constants include mold change loss time, the optimization target model including: a first model for calculating the total number of delays in refrigerator production orders based on the specific values of the decision variables under various target constants, and a second model for calculating the number of mold changes in a refrigerator foaming process based on the specific values of the decision variables under various target constants; The optimization target model is solved based on the specific values of the decision variables under each target constant and preset constraints to obtain calculation results of the optimization target model under various combinations of specific values of the decision variables under each target constant, wherein the constraints include that the sum of the actual production time of the mold on a single fixture in a single day and the mold change loss time cannot exceed the upper limit of the production time on that day; A calculation result that meets preset conditions is obtained as a target calculation result, and specific values of the mold hanging state and the fixture mold changing order state in the production thread in the decision variables corresponding to the target calculation result are output as target variables.

2. The refrigerator industry production scheduling data processing method according to claim 1, characterized in that: The optimization target model also includes: The third model is used to calculate the daily delay penalty for orders based on the specific values of the decision variables under various target constants.

3. The refrigerator industry production scheduling data processing method according to claim 2, characterized in that: The first model is specifically used to calculate the total number of delayed orders based on the postponement status of each order; The second model is specifically used to calculate the total number of mold changes based on the absolute value of the difference between the idle states of the mold on two consecutive days; The third model is specifically used to calculate the daily delay penalty for the total orders based on the daily order delay penalty coefficient corresponding to each order and the daily cumulative delay amount of the orders.

4. The refrigerator industry production scheduling data processing method according to claim 3, characterized in that: The obtaining of a calculation result that meets a preset condition as a target calculation result includes: The calculation results of the first model, the second model and the third model are multiplied by their corresponding weight coefficients, and the sum of the calculation results of the first model, the second model and the third model after adjustment of the weight coefficients is calculated as the candidate result, and the candidate result with the minimum value is used as the target calculation result.

5. The refrigerator industry production scheduling data processing method according to claim 1, characterized in that: The constraints may also include one or more of the following combinations: The required quantity of an order is equal to the actual quantity of the order plus the delay of the order: The sum of the cumulative planned quantity and cumulative delay of orders as of a certain day is not less than the cumulative demand for the orders; The order quantity is equal to the model quantity; Model production is equal to mold production; The mold production output is equal to the mold production time multiplied by the cycle time; The daily output of the line body cannot exceed the upper limit of the line body; Mold output cannot exceed the theoretical output limit; The order of hanging molds on a single fixture on a single day is unique; The relationship between the mold hanging order of a single fixture on a single day and the mold hanging status in the order: Only one mold can be mounted on a single fixture in a single day and in a single order; The mold hanging status must be consistent between two consecutive days; The total usage time of the mold cannot exceed the theoretical upper limit.

6. A refrigerator industry production scheduling data processing device, characterized in that: include: A parameter acquisition unit, configured to acquire target constants and decision variables for refrigerator production scheduling, wherein the target constants are constant data of the production thread, and the decision variables are variable data in the production process that matches the order data; A parameter calculation unit, used to determine the specific value of the decision variable under each target constant; a model calculation unit, configured to obtain an optimization target model created based on a mapping relationship between the decision variables and the total number of order delays and the number of mold changes, and substitute specific values of the decision variables under various target constants into the optimization target model, wherein the target constants include mold change loss time, the optimization target model comprising: a first model for calculating the total number of order delays based on the specific values of the decision variables under various target constants, and a second model for calculating the number of mold changes in a refrigerator foaming process based on the specific values of the decision variables under various target constants; A decision variable selection unit is used to solve the optimization target model based on the specific values of the decision variables under various target constants and preset constraints, and obtain the calculation results of the optimization target model under various combinations of specific values of the decision variables under various target constants; obtain the calculation results that meet the preset conditions as the target calculation results, and the constraints include that the sum of the actual production time of the mold on a single fixture in a single day and the mold change loss time cannot exceed the upper limit of the production time of the day, obtain the calculation results that meet the preset conditions as the target calculation results, and output the specific values of the mold hanging status and the fixture mold change order status in the production thread in the decision variables corresponding to the target calculation results as target variables.

7. The refrigerator industry production scheduling data processing device according to claim 6, characterized in that: The optimization target model also includes: The third model is used to calculate the daily delay penalty for orders based on the specific values of the decision variables under various target constants.

8. The refrigerator industry production scheduling data processing device according to claim 7, characterized in that: The first model is specifically used to calculate the total number of delayed orders based on the postponement status of each order; The second model is specifically used to calculate the total number of mold changes based on the absolute value of the difference between the idle states of the mold on two consecutive days; The second model is specifically used to calculate the daily delay penalty for the total orders based on the daily order delay penalty coefficient corresponding to each order and the daily cumulative delay amount of the orders.

9. The refrigerator industry production scheduling data processing device according to claim 8, characterized in that: The decision variable selection unit is specifically used for: The calculation results of the first model, the second model and the third model are multiplied by their corresponding weight coefficients, and the sum of the calculation results of the first model, the second model and the third model after adjustment of the weight coefficients is calculated as the candidate result, and the minimum candidate result is used as the target calculation result.

10. The refrigerator industry production scheduling data processing device according to claim 6, characterized in that: The constraints may also include one or more of the following combinations: The required quantity of an order is equal to the actual quantity of the order plus the delay of the order: The sum of the cumulative planned quantity and cumulative delay of orders as of a certain day is not less than the cumulative demand for the orders; The order quantity is equal to the model quantity; Model production is equal to mold production; The mold production output is equal to the mold production time multiplied by the cycle time; The daily output of the line body cannot exceed the upper limit of the line body; Mold output cannot exceed the theoretical output limit; The order of hanging molds on a single fixture on a single day is unique; The relationship between the mold hanging order of a single fixture on a single day and the mold hanging status in the order: Only one mold can be mounted on a single fixture in a single day and in a single order; The mold hanging status must be consistent between two consecutive days; The total usage time of the mold cannot exceed the theoretical upper limit.

Images