A rule-based optimization method for production scheduling of roll-to-roll packaging machines

Abstract

The present invention discloses a method for optimizing production scheduling of a roll-to-wrap machine group based on a rule, characterized in that it includes the following steps: constructing a production scheduling model for the roll-to-wrap stage based on a genetic algorithm; adding an optimization module to the production scheduling model for the roll-to-wrap stage, finding the optimization object and optimizing the allocation of each unit of the optimization object according to the same stop of the machines; the optimization method includes: for each generation of high-quality individuals in the genetic algorithm, all optimization objects are found according to the principle that the units with the same production grade are a group of optimization objects; for each optimization object, the unit with the earliest end time and the latest end time is found, and the machine with unfinished remaining tasks is found; according to the unfinished amount of the remaining tasks, the optimization amount is determined; according to the optimization amount, the allocation of each unit in the optimization object is adjusted. The present invention realizes the same stop of the machines through the optimization design of the production scheduling method of the docking package machine group, and meets the needs of energy supply and dust removal and cleaning in production.

Description

Technical Field

[0001] The invention relates to the field of cigarette production scheduling, and in particular to a rule-based production scheduling optimization method for a cigarette rolling and packing machine group. Background Art

[0002] With the introduction of the smart manufacturing strategy, cigarette processing companies are considering the automated scheduling of production plans when carrying out smart manufacturing construction, including the cigarette production and manufacturing links of rolling, packing, and mouth rods. Rolling and packing are the most direct link in measuring the company's output in production and processing, and are particularly important in the entire production process.

[0003] Patent document CN112633576A discloses a two-stage scheduling optimization method and system for production scheduling in cigarette factories, including initializing the parameters of rolling and packing and silk making, and inferring the silk making plan from the rolling and packing plan. This method can complete the formulation of the enterprise workshop plan according to the superior production plan. However, this method does not take into account the actual management constraints of the rolling and packing production, especially the actual needs of energy supply, cleaning and dust removal, and cannot meet the energy supply, cleaning and dust removal. Summary of the invention

[0004] The purpose of the present invention is to provide a rule-based method for optimizing the production scheduling of a rolling and packaging machine group in response to the above-mentioned problems. By optimizing the production scheduling method of the rolling and packaging machine group, when the genetic algorithm schedules the production of the rolling and packaging machine, the individuals generated ensure that the machines producing the same brand can finish production at the same time, and meet the management requirements of energy supply, dust removal, etc. in the rolling and packaging production process. The method is suitable for the production scheduling of rolling and packaging in any cigarette processing enterprise.

[0005] The technical solution adopted by the present invention is as follows:

[0006] The present invention discloses a rule-based production scheduling optimization method for a roll-to-wrap machine group, comprising the following steps: constructing a production scheduling model for the roll-to-wrap stage based on a genetic algorithm; adding an optimization module to the production scheduling model for the roll-to-wrap stage, wherein the optimization module finds the optimization object according to the simultaneous stop of the machines and adjusts and optimizes the allocation amount of each unit of the optimization object.

[0007] Preferably, the optimization method of the optimization module includes:

[0008] Step 1: For each generation of high-quality individuals in the genetic algorithm, find all optimization objects according to the principle of taking the units with the same production brand as a group of optimization objects;

[0009] Step 2: For each optimization object, find out the Jmin / Jmax of the unit with the earliest end time min and the latest end time max, and find out the units with unfinished remaining tasks; determine the optimization amount according to the amount of unfinished remaining tasks;

[0010] Step 3: According to the optimization amount, adjust the allocation amount of each unit in the optimization object.

[0011] Preferably, in step 1, the principle of optimization objects also includes that machines in the same area are a group of optimization objects or the same equipment group is a group of optimization objects.

[0012] Preferably, the optimization method specifically includes:

[0013] S1: Find the machines that produce the same brand, query the end time of each machine's production, and record the earliest end time and the latest end time of the machine;

[0014] S2: Query the number of brands that have not been produced by the machine that produces the brand; and determine whether there is a number R of brands that have not been produced;

[0015] S3: If there is an unproduced grade quantity R, the unproduced grade quantity R is optimized;

[0016] S4: If there is no unproduced grade quantity R, the time difference between the earliest end time and the latest end time is optimized;

[0017] S5: Calculate the optimization amount m, and according to the optimization amount m, correct the production allocation amount one by one according to the relationship between each machine and the brand.

[0018] Preferably, in S4, the median of the end time of each machine may be optimized.

[0019] Preferably, the method for calculating the optimization amount m of S5 is: if there is an unproduced brand amount R, the optimization amount m is: m=R; if there is no unproduced brand amount R, the optimization amount m is: m=(ba)α / (1 / A+1 / B), wherein a is the earliest time point for finishing the machine, A is the adaptability of a to the brand; b is the latest time point for finishing the machine, B is the adaptability of b to the brand, and α is the adjustment coefficient.

[0020] Preferably, the method for constructing a production scheduling model for the winding and packaging stage based on a genetic algorithm includes: taking the relationship constraints between the winding and packaging machine and the wire feeding machine, the relationship constraints between the winding and packaging machine and the order brand, and the constraints of related materials on the brand order as winding and packaging constraint rules; and constructing the initial individual according to the winding and packaging constraint rules.

[0021] As a preferred method, after constructing the initial individuals, the individuals undergo individual crossover, individual variation, individual evaluation, individual optimization and individual inheritance, and finally converge to obtain the production scheduling result.

[0022] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0023] 1. The present invention solves the problem that the existing production scheduling cannot meet the management requirements of energy supply, dust removal, etc. in the roll-to-roll packaging production process. Through the optimized design of simultaneous machine stop, energy supply and dust removal are ensured while facilitating production.

[0024] 2. When the genetic algorithm of the present invention schedules the production of the roll-to-roll packaging machine, the individuals generated ensure that the machines producing the same brand, the same area or the same equipment group can complete production at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention will now be described by way of example with reference to the accompanying drawings, in which:

[0026] Figure 1 It is a flow chart of roll-to-roll packaging production scheduling based on genetic algorithm in the embodiment.

[0027] Figure 2 It is a production scheduling optimization method for a roll-to-roll packaging machine group based on the same stop rule in the embodiment. DETAILED DESCRIPTION

[0028] All features disclosed in this specification, or steps in all methods or processes disclosed, except mutually exclusive features and / or steps, can be combined in any manner.

[0029] Any feature disclosed in this specification (including any additional claims and abstract), unless otherwise stated, may be replaced by other equivalent or alternative features having similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

[0030] The present invention discloses a rule-based production scheduling optimization method for a roll-to-wrap machine group, comprising the following steps: constructing a production scheduling model for the roll-to-wrap stage based on a genetic algorithm; adding an optimization module to the production scheduling model for the roll-to-wrap stage, wherein the optimization module finds the optimization object according to the simultaneous stop of the machines and adjusts and optimizes the allocation amount of each unit of the optimization object.

[0031] like Figure 1 As shown, in the production scheduling of rolling and packing based on genetic algorithm, a production scheduling model of the rolling and packing stage of the actual production system scheduling problem of the cigarette factory is established; the relationship constraints between the rolling and packing machine and the thread feeding machine, the relationship constraints between the rolling and packing machine and the order brand, and the constraints of related materials on the brand order are taken as rolling and packing constraint rules; the initial individual is constructed according to the rolling and packing constraint rules; among them, the constraint relationship between the rolling and packing machine and the order brand is taken as a settable parameter as the intermediate calculation basis of the scheduling model: Pij, expressed as the adaptability of brand i to the rolling and packing machine j, Wi = Pi1 + Pi2 + ... + Pij, expressed as the total adaptability of the unit to brand i; the constraint relationship between the related materials and the brand order is taken as a constraint condition: Ti is expressed as the start time constraint of brand i.

[0032] After constructing the initial individuals, the individuals go through individual crossover, individual variation, individual evaluation, individual optimization and individual inheritance, and finally converge to obtain the production scheduling result. Among the generated individuals, for the selected individual objects, the constraints of the machine stop rule are taken into account, and the individuals that have an impact on the offspring are optimized.

[0033] In one embodiment, the optimization method includes:

[0034] Step 1: For each generation of high-quality individuals in the genetic algorithm, find all optimization objects according to the principle of taking the units with the same production brand as a group of optimization objects;

[0035] Step 2: For each optimization object, find out the Jmin / Jmax of the unit with the earliest end time min and the latest end time max, and find out the units with unfinished remaining tasks; determine the optimization amount according to the amount of unfinished remaining tasks;

[0036] Step 3: According to the optimization amount, adjust the allocation amount of each unit in the optimization object.

[0037] In another embodiment, the principle of optimizing objects in step 1 further includes that machines in the same area are a group of optimization objects or the same equipment group is a group of optimization objects.

[0038] like Figure 2 As shown, in the embodiments, a specific optimization method is disclosed:

[0039] S1: Select a certain winder, find the winder with the same ending brand as the winder, and go to S2; and find the last ending brand of the winder, and go to S4;

[0040] S2: Find the earliest and latest finishing machines;

[0041] S3: Find the coiling machine that has not completed the task;

[0042] S4: Determine whether there are any remaining tail brands; if not, proceed to S5; if there are any remaining, take the remaining amount as the optimization amount and proceed to S6;

[0043] S5: The difference between the earliest and latest finishing machines is used as the optimization amount;

[0044] S6: Optimize the machines with the same finishing grade with the optimization amount;

[0045] S7: Start optimization.

[0046] The calculation method of the optimization amount m of S4 and S5 is as follows: if there is a remaining brand amount R, the optimization amount m is: m=R; if there is no remaining brand amount R, the optimization amount m is: m=(ba)α / (1 / A+1 / B), wherein a is the earliest time point to terminate the machine, A is the adaptability of a to the brand; b is the latest time point to terminate the machine, B is the adaptability of b to the brand, and α is the adjustment coefficient.

[0047] Compared with the previous embodiment, in S5 of this embodiment, the median of the end time of each machine can also be used as the optimization amount.

[0048] The present invention is not limited to the above-mentioned specific embodiments, but extends to any new features or any new combination disclosed in this specification, as well as any new method or process steps or any new combination disclosed.

Claims

1. A rule-based method for optimizing production scheduling of a roll-to-roll package machine, characterized in that: The following steps are involved: Construct a production scheduling model for the roll-to-roll packaging stage based on genetic algorithms; add an optimization module to the production scheduling model for the roll-to-roll packaging stage. The optimization module finds the optimization object based on the simultaneous stop of the machines and adjusts and optimizes the allocation of each unit of the optimization object; The optimization method of the optimization module includes: Step 1: For each generation of high-quality individuals in the genetic algorithm, find all optimization objects according to the principle of taking the units with the same production brand as a group of optimization objects; Step 2: For each optimization object, find out the unit with the earliest and latest end time, and find out the units with unfinished remaining tasks; determine the optimization amount based on the amount of unfinished remaining tasks; Step 3: According to the optimization amount, adjust the allocation amount of each unit in the optimization object; The optimization method specifically includes: S1: Find the machines that produce the same brand, query the end time of each machine's production, and record the earliest end time and the latest end time of the machine; S2: Query the number of brands that have not been produced by the machine that produces the brand; and determine whether there is a number R of brands that have not been produced; S3: If there is an unproduced grade quantity R, the unproduced grade quantity R is optimized; S4: If there is no unproduced grade quantity R, the time difference between the earliest end time and the latest end time is optimized; S5: Calculate the optimization amount m, and according to the optimization amount m, correct the production allocation amount one by one according to the relationship between each machine and the brand; The method for calculating the optimization amount m of S5 is as follows: if there is an unproduced brand amount R, the optimization amount m is: m=R; if there is no unproduced brand amount R, the optimization amount m is: m=(ba)α / (1 / A+1 / B), wherein a is the earliest time point to terminate the machine, A is the adaptability of a to the brand; b is the latest time point to terminate the machine, B is the adaptability of b to the brand, and α is the adjustment coefficient.

2. The rule-based production scheduling optimization method for a roll-to-roll package machine group according to claim 1 is characterized in that: In the step 1, the principle of optimizing objects also includes that machines in the same area are a group of optimization objects or the same equipment group is a group of optimization objects.

3. The rule-based production scheduling optimization method for a roll-to-roll package machine group according to claim 1, characterized in that: In S4, the median of the end time of each machine may also be optimized.

4. The rule-based production scheduling optimization method for a roll-to-roll package machine group according to claim 1, characterized in that: The method for constructing a production scheduling model for the coiling and packaging stage based on genetic algorithm includes: taking the relationship constraints between the coiling and packaging machine and the wire feeding machine, the relationship constraints between the coiling and packaging machine and the order brand, and the constraints of related materials on the brand order as coiling and packaging constraint rules; constructing the initial individual according to the coiling and packaging constraint rules.

5. The rule-based production scheduling optimization method for a roll-to-roll package machine group according to claim 4 is characterized in that: After constructing the initial individuals, the individuals go through individual crossover, individual mutation, individual evaluation, individual optimization and individual inheritance, and finally converge to obtain the production scheduling results.

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