Rolling and packaging workshop production control daily production plan calculation method considering uncertain factors

Abstract

The invention discloses a rolling and packing workshop production control daily plan calculation method considering uncertain factors, and the method comprises the steps: taking a linear programming mathematical model as a core, and taking each cigarette production mark as a category; the method comprises the following steps: respectively acquiring production condition data of cigarettes with production marks including uncertain factors, expressing the data as constraint inequalities of corresponding marks, and establishing a joint production planning mathematical model of a plurality of operation areas; according to the characteristic that the actual production control process is divided into full-equipment production and single-machine ending, a multi-dimensional planning model is converted into two-time two-dimensional model solving to achieve rapid compiling and updating of a production control daily plan of a corresponding scene when different uncertain factors are updated, and the solving result is specifically shown as full-equipment production time and single-machine ending operation time. According to the method, the uncertain factors influencing production are included, the influence conditions of the uncertain factors are represented, the influence degree of the uncertain factors on the production time can be obtained by updating the calculation result, and production control management can conveniently respond to the generated uncertain factors in time and change a scheduling scheme.

Description

Technical Field

[0001] The invention relates to a production plan compilation method, and in particular to a method for calculating a daily production plan for production control in a packaging workshop taking uncertain factors into account. Background Art

[0002] The essence of production scheduling is the execution of production plans, so the preparation of reasonable production plans determines the efficiency and rationality of production scheduling. The tobacco production department implements a planned production system, so the rolling and packaging workshop needs to complete the planned production and control and reduce the unplanned production at the end of the month. The actual daily production plan has high requirements, but many uncertain factors in production will affect the execution of the production plan during production control, and the original plan must be re-arranged and adjusted according to the actual situation.

[0003] Roll-to-roll production has the characteristics of large-scale production, large number of equipment, re-entrant processing flow, and many uncertain factors, which brings great difficulties to the accurate formulation of daily production plan under actual conditions, and often results in excessive over-plan production. The research on production planning is mainly based on deterministic production environment. In recent years, production planning in uncertain production environment has gradually attracted attention. The uncertain factors studied are mainly concentrated on demand fluctuations, production capacity changes, and inaccurate production time. At present, the research on production planning under uncertain re-entrant conditions mainly focuses on: (1) production planning model based on capacity constraints under the condition of low repeatability of production system and uncertainty of product qualification rate; (2) optimal planning production decision model based on meeting product demand and delivery date under the condition of random qualified product rate and rework success rate.

[0004] In terms of production planning calculation models and solutions, there are many methods for production planning, including mathematical model methods such as queuing networks and mathematical programming, and artificial intelligence algorithms such as genetic algorithms, ant colony algorithms, and annealing algorithms. A common way to solve production planning problems is to use mathematical expressions of equations or inequalities to transform the problem into an optimization problem of a multi-objective function, and on this basis, to describe, analyze, and solve the problem. The above methods and algorithms are mainly analyzed from the perspective of theoretical content related to production operation planning, or mainly from the perspective of the application of production operation planning methods and related management technologies.

[0005] In short, no research has been found on the problem of rescheduling the daily production plan to meet the needs of production control in tobacco production, that is, the rescheduling of the required real-time production plan in combination with the actual situation of tobacco production has not been studied yet. Considering the tobacco packaging workshop in the production control mode, the rapid re-compiling and updating of the production plan for the corresponding scenario under the influence of existing uncertain factors is a practical problem that needs to be solved in production. Summary of the invention

[0006] In view of the shortcomings of the above-mentioned prior art, the present invention will study how to quickly recompile and update the production daily plan for the corresponding scenario when the rolling and packaging workshop is in production control, taking into account various uncertain factors affecting production, and control the error between the production output of finished cigarettes in the workshop and the planned output within a reasonably set range. The specific design scheme is as follows:

[0007] 1. Establish a calculation model. With the linear programming mathematical model as the core and each cigarette production brand as the category, the production data of the cigarette brands with uncertain factors are collected and expressed as the constraint inequality of the corresponding brand to establish a mathematical model for joint production planning of multiple operation areas.

[0008] 2. Model solution. According to the characteristics of the actual production control process, which is divided into full equipment production and single machine finishing, the multi-dimensional planning model is converted into two two-dimensional model solutions to achieve rapid preparation and update of the production control daily plan corresponding to the scenarios when different uncertain factors are present. The solution results are specifically manifested as the full equipment production time and the single machine finishing operation time.

[0009] 3. Visualization of planning results.

[0010] Specifically, a method for calculating a daily production plan for a packaging workshop with uncertainties taken into account in the present invention includes:

[0011] Based on the existing technology, the standard mathematical model of linear programming problem can be expressed as formula (1-1):

[0012]

[0013] Combined with the actual production situation of the packaging workshop, the factors affecting the production control and final production output at the end of the month include cigarette brand, average operating speed of production equipment, production equipment model, linkage and coordination between operation areas, and uncertain downtime of production equipment due to auxiliary material supply and equipment failure. A total of 4 types of cigarettes, a, b, c, and d, are selected for joint production in operation area A (hereinafter referred to as area A) and operation area B (hereinafter referred to as area B). A mathematical model for joint production planning of the two operation areas is constructed, and the multi-dimensional planning model is converted into a two-dimensional model to solve and calculate the precise large production time and the estimated operating time of a single final equipment, so as to achieve the estimated calculation of the output of the precise control department at the end of the month. The main modeling and calculation process is as follows:

[0014] 1. Data collection. According to the actual production arrangement on the day of final production control and the remaining production of each brand of cigarettes on that day, the cigarette production brand, equipment model, cigarette machine production speed (average), the number of storage layers of YF17 cigarette storage barrels and the single-layer storage production, the cigarette machine operation time after the tobacco supply is stopped, and the packaging machine emptying production (the total emptying production from the cigarette machine smoke outlet to the packaging machine) of each production equipment in each operation area are counted respectively. The impact of uncertain factors on production is reflected in the production speed of production equipment.

[0015] 2. Construction and solution of computational model. Take cigarette a as an example to model, and the modeling method of other brands is the same. Set the following variables: the remaining output of cigarette a on that day is p0 pieces, the final output of all equipment is p1 pieces, and the production speed of cigarette a in area A is v A , the maximum production time is t A , the production speed of cigarette a in area B is v B , the maximum production time is t B , the production speed of the finishing machine is v c , production time is t c , the internal output of cigarette making machine and packaging machine is ε, and the control calculation accuracy is Δ. According to the above settings, the linear calculation model of formula (2-1) can be obtained:

[0016]

[0017] Since the production time of area A and area B is roughly the same, formula (2-1) is transformed into formula (2-2):

[0018]

[0019] The feasible solution domain of formula (2-2) is a plane, and similar solution results are as follows Figure 2 As shown. In the process of production control, the most ideal state is to complete the production task of the month within the large production period, that is, the solution of formula (2-2) degenerates into a binary solution, that is, formula (2-3). The solution of formula (2-3) is as follows Figure 3 shown.

[0020]

[0021] The result calculated under ideal conditions is the time required for large-scale production. After taking into account the uncertain influencing factors in the production process (such as equipment failure repair, equipment maintenance, packaging machine production loss, auxiliary material supply, etc.), it is inevitable that the current shift output will be lower than the output to be controlled. The missing output will be made up by the production of a single tailing equipment. At this time, the calculation model is transformed into formula (2-4), where p2 is the sum of the cigarette a output of the two large-scale production operation areas A and B:

[0022]

[0023] Solving equation (2-4) again can get the production time for large-scale production and single-machine closing. According to the production brand, the models shown in equations (2-2), (2-3), and (2-4) are established one by one for calculation, and the daily plan for closing production control can be compiled.

[0024] 3. Visualization of calculation results. Choose Excel as the implementation platform to complete the calculation and visualization of the daily schedule.

[0025] The beneficial effects of the present invention include:

[0026] 1. The present invention standardizes and programs the daily plan calculation during the final production of the packaging workshop. The model selection and calculation process are in line with the actual production, meeting the information and results required for production control management in the operation area, and facilitating production scheduling among multiple departments.

[0027] 2. The present invention takes into account the uncertain factors that affect production and characterizes their impact. By updating the calculation results, the degree of their impact on production time can be known, which facilitates production control management to respond to the uncertain factors in a timely manner and change the scheduling plan;

[0028] 3. The present invention simplifies the multi-dimensional solution problem into two-dimensional solutions in combination with the actual production situation of the wrapping workshop, which greatly speeds up the solution speed of the model and is more suitable for the application of the operating area in actual production;

[0029] 4. The calculation results of the present invention are visualized based on Excel, and the calculation time is fast to meet the needs of timely updates, avoiding the development of application programs, saving resources and better intuitively presenting the meaning of the calculation results; BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of the method of the present invention.

[0031] Figure 2 Schematic diagram of the class solution domain of formula (2-2) of the present invention

[0032] Figure 3 This is a schematic diagram of the class solution of formula (2-3) of the present invention. DETAILED DESCRIPTION

[0033] like Figure 1 As shown, a method for calculating a daily production plan for a packaging workshop with production control taking into account uncertain factors of the present invention comprises:

[0034] The standard mathematical model of linear programming problem can be expressed as formula (1-1):

[0035]

[0036] Combined with the actual production situation of the packaging workshop, the factors affecting the production control and final production output at the end of the month include cigarette brand, average operating speed of production equipment, production equipment model, linkage and coordination between operation areas, and uncertain downtime of production equipment due to auxiliary material supply and equipment failure. A total of 4 types of cigarettes, a, b, c, and d, are selected for joint production in operation area A (hereinafter referred to as area A) and operation area B (hereinafter referred to as area B). A mathematical model for joint production planning of the two operation areas is constructed, and the multi-dimensional planning model is converted into a two-dimensional model to solve and calculate the precise large production time and the estimated operating time of a single final equipment, so as to achieve the estimated calculation of the output of the precise control department at the end of the month. The main modeling and calculation process is as follows:

[0037] 1. Data collection. According to the actual production arrangement on the day of final production control and the remaining production of each brand of cigarettes on that day, the cigarette production brand, equipment model, cigarette machine production speed (average), the number of storage layers of YF17 cigarette storage barrels and the single-layer storage production, the cigarette machine operation time after the tobacco supply is stopped, and the packaging machine emptying production (the total emptying production from the cigarette machine smoke outlet to the packaging machine) of each production equipment in each operation area are counted respectively. The impact of uncertain factors on production is reflected in the production speed of production equipment.

[0038] 2. Construction and solution of computational model. Take cigarette a as an example to model, and the modeling method of other brands is the same. Set the following variables: the remaining output of cigarette a on that day is p0 pieces, the final output of all equipment is p1 pieces, and the production speed of cigarette a in area A is v A , the maximum production time is t A , the production speed of cigarette a in area B is v B , the maximum production time is t B , the production speed of the finishing machine is v c , production time is t c , the internal output of cigarette making machine and packaging machine is ε, and the control calculation accuracy is Δ. According to the above settings, the linear calculation model of formula (2-1) can be obtained:

[0039]

[0040] Since the production time of area A and area B is roughly the same, formula (2-1) is transformed into formula (2-2):

[0041]

[0042] The feasible solution domain of formula (2-2) is a plane, and similar solution results are as follows Figure 2 As shown. In the process of production control, the most ideal state is to complete the production task of the month within the large production period, that is, the solution of formula (2-2) degenerates into a binary solution, that is, formula (2-3). The solution of formula (2-3) is as follows Figure 3 shown.

[0043]

[0044] The result calculated under ideal conditions is the time required for large-scale production. After taking into account uncertain factors such as equipment failure repair, equipment maintenance, packaging machine production loss, and auxiliary material supply, it is inevitable that the current shift output will be lower than the controlled output. The missing output will be made up by a single tailing equipment. The calculation model at this time is transformed into formula (2-4), where p2 is the sum of the cigarette a output of the two large-scale production operation areas A and B:

[0045]

[0046] Solving equation (2-4) again can get the production time for large-scale production and single-machine closing. According to the production brand, the models shown in equations (2-2), (2-3), and (2-4) are established one by one for calculation, and the daily plan for closing production control can be compiled.

[0047] 3. Visualization of calculation results. Excel is selected as the implementation platform to complete the calculation and visualization of the daily schedule. The overall implementation is as follows, which will be divided into four sections including data collection statistics, internal equipment output measurement, large-scale production calculation, and single-machine end-of-life calculation. The visualization example will be taken as the end of cigarette A.

[0048] (1) Data collection and statistics: fill in the information in sequence according to the equipment status on the day, as shown in Table 1, Table 2 and Table 3.

[0049] Table 1A Area Data Collection

[0050]

[0051] Table 2B Area Data Collection

[0052]

[0053] Table 3 Uncertain factors affecting conversion

[0054]

[0055] (2) Actual measurement of internal equipment output

[0056] When the supply of tobacco is stopped, the timer counts the continuous operation time of the cigarette making machine, the carton output corresponding to the single-layer YF17 cigarettes, and the carton output in the corresponding period after the packaging machine is emptied. The specific situation is shown in Table 4, where the continuous operation time of the cigarette making machine after the tobacco is cut off is the average time of multiple measurements.

[0057] Table 4 Actual measurement of internal production of equipment

[0058]

[0059] (3) Calculation of production time

[0060] According to the production scheduling department's information on the output to be controlled, the time required for the large production period is calculated using the model of formula (2-3), as shown in Table 5.

[0061] Table 5 Calculation of production time

[0062]

[0063] (4) Calculation of the end time of a single machine

[0064] In the calculation of the end time of a single machine, a solution model is established according to formula (2-4), and the required single machine smoke output and the end time are calculated by collecting and testing data, as shown in Table 6.

[0065] Table 6 Calculation of single machine closing time

[0066]

[0067] The above process demonstrates the solution and visualization of a planning result using Yunyan (Purple) as an example. If different uncertain influencing factors appear, the planning result can be updated by changing the required statistical data.

Claims

1. A method for calculating the daily production plan of a packaging workshop with uncertain factors, characterized in that: Combined with the factors affecting the production control and final production of the packing workshop at the end of the month, a total of 4 types of cigarettes, a, b, c, and d, are selected for joint production in operation area A and operation area B. A mathematical model for joint production planning of the two operation areas is constructed. The multidimensional planning model is converted into a two-dimensional model to solve and calculate the precise large production time and the estimated operating time of a single final equipment, so as to achieve the estimated calculation of the production of the precise final production control department at the end of the month. The method includes: (1) Data Collection According to the actual production arrangement on the day of final production control and the remaining production of each brand of cigarettes on that day, the cigarette production brand, equipment model, average production speed of cigarette making machines, number of storage layers of cigarette barrels and single-layer storage production, operation time of cigarette making machines after tobacco supply stops, and emptying production of packaging machines of each production equipment in each operation area are counted respectively. The influence of uncertain factors on production is reflected in the average production speed of production equipment; the emptying production of packaging machines refers to the total emptying production from the cigarette machine outlet to the packaging machine; (2) Computational model construction and solution Set up the following variables: the remaining production of cigarette a on that day is p0 pieces, the final production of all equipment is p1 pieces, and the production speed of cigarette a in area A is v A , the maximum production time is t A , the production speed of cigarette a in area B is v B , the maximum production time is t B , the production speed of the finishing machine is v c , production time is t c , the internal output of cigarette making machine and packaging machine is ε, and the accuracy of production control calculation is Δ; According to the above settings, the linear calculation model of formula (2-1) can be obtained: The large production time of zone A is the same as that of zone B, and formula (2-1) is converted into formula (2-2): The feasible solution domain of formula (2-2) is a plane; In the process of production control, the ideal state is to complete the production task of the month within the large production period. The solution of formula (2-2) degenerates into a binary solution to obtain formula (2-3): The result calculated under ideal conditions is the time required for large-scale production. After taking into account the uncertain influencing factors in the production process, it is inevitable that the current shift output will be lower than the output to be controlled. The missing output will be made up by a single tailing equipment. The calculation model is transformed into formula (2-4), where p2 is the sum of the cigarette a output of the two large-scale production operation areas A and B: Solving equation (2-4) again will give the production time for large-scale production and single-machine end-of-life scenarios; According to the production brands, the models shown in formulas (2-2), (2-3) and (2-4) are established one by one for calculation, and a daily plan for final production control is prepared.

2. The method for calculating the daily production plan of a packaging workshop with uncertainties taken into account according to claim 1 is characterized in that: Also includes: (3) Visualization of calculation results Choose Excel as the implementation platform to complete the calculation and visualization of the daily schedule.

3. The method for calculating the daily production plan of a packaging workshop with uncertainties in mind according to claim 1 is characterized by: The uncertain influencing factors in the production process include equipment failure repair, equipment maintenance, packaging machine production loss and auxiliary material supply.

4. The method for calculating the daily production plan for production control in a packaging workshop taking into account uncertain factors according to any one of claims 1 to 3, characterized in that: The factors affecting the month-end production control and final output control of the packaging workshop include cigarette brand, average operating speed of production equipment, production equipment model, linkage and coordination between work areas, and production equipment shutdown due to auxiliary material supply and equipment failure.

5. 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 a method for calculating a daily production plan for production control in a packaging workshop taking into account uncertain factors as described in any one of claims 1 to 4 are implemented.

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