A method and system for optimizing flue distribution based on cigarette orders

Abstract

The present invention provides a method and system for optimizing cigarette duct allocation based on cigarette orders. By constructing a cigarette duct allocation optimization model, the cigarette duct optimization problem is introduced into the sorting time model, and the objective function is changed to solve the waiting time. Based on the relationship between the similarity of the specifications and the sorting quantity, the sum-difference relationship between the sorting quantities of the items is introduced, the solution of the specification similarity coefficient is improved, and the positive correlation between the two is proved. Finally, the objective function is determined as the minimum sum of the specification similarity coefficients of each sorting area. The actual data is simulated and verified by combining the specification optimization algorithm-the improved K-means dynamic clustering algorithm, so as to reduce the total operation time of the sorting system, thereby improving the work efficiency of the cigarette distribution center.

Description

Technical Field

[0001] The present invention relates to the field of automatic sorting technology, and in particular to a method and system for optimizing cigarette duct distribution based on cigarette orders. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] In the allocation of automatic cigarette sorting equipment in cigarette logistics distribution centers, the existing staff generally allocates according to the EIQ-ABC analysis method combined with daily work experience. Usually, the cigarette specifications are classified according to the sorting volume of cigarette specifications. Small specifications and large quantities are Class A cigarette specifications, large specifications and small batches are Class C cigarette specifications, and the rest are Class B cigarette specifications. For Class A cigarette specifications with huge order sorting volume, channel sorting machines are generally selected for sorting to ensure their sorting efficiency. For Class B and C cigarette specifications, the sorting volume is relatively small, and horizontal sorting machines are generally selected for sorting, which to a certain extent meets the daily sorting work of the distribution center. However, the automatic cigarette sorting line is mainly composed of multiple horizontal sorting machines, channel sorting machines, conveyor belts, coding equipment, packaging machines, etc. The efficiency of the entire line is mainly determined by horizontal sorting machines, channel sorting machines and conveyor belts. Channel sorting machine: a channel sorting machine can only sort one specification of cigarettes, and can produce any number of cigarettes from 1 to 5 at a time. The mode of discharging cigarettes by the horizontal sorting machine (hereinafter referred to as the sorting area) is to spit cigarettes onto the conveyor belt according to the order. A horizontal sorting machine can sort up to 10 specifications of cigarettes, and can only produce one cigarette at a time. In the automatic cigarette sorting line, each cigarette sorting machine performs sorting operations in parallel as an independent partition, and then merges serially after all the cigarettes required in an order are sorted. Due to parallel sorting, the total time required to sort a cigarette order depends on the sorting completion time of the sorting area with the longest sorting time.

[0004] However, the investigation found that after the initial classification, the different specifications of cigarettes were placed in the corresponding sorting equipment, and there was a certain degree of untimeliness in the adjustment of the flue of the cigarette specifications. For daily sorting operations, within a relatively stable cycle, the order quantity of cigarette specifications will not fluctuate greatly. However, since the order quantity of cigarettes is often affected by other factors such as holidays, the order quantity will change to a certain extent. For the above-mentioned sorting method, there is a certain degree of untimeliness. The sorting efficiency will change with the change of the cigarette order structure, which will increase the sorting time. There will also be an imbalance in the distribution of cigarette specifications, which will slow down the completion time of the sorting area as a whole and reduce operating efficiency. Summary of the invention

[0005] In order to solve the above problems, the present disclosure proposes a method and system for optimizing duct allocation based on cigarette orders, which optimizes and adjusts the cigarette specifications in each sorting area to ensure the balance of cigarette sorting tasks between different sorting areas, thereby reducing the completion time of the longest sorting area as a whole and improving the operating efficiency of the entire automatic cigarette sorting line.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions:

[0007] A method for optimizing cigarette duct allocation based on cigarette orders, comprising:

[0008] Obtaining cigarette order information for the current day, and matching the order information with stored cigarette specifications;

[0009] To minimize the total time of cigarette order sorting, a product specification flue allocation optimization model is constructed to divide the cigarette specifications of each flue in the sorting area;

[0010] The cigarette order is divided into several cigarette sub-orders. Each sorting area performs parallel sorting according to the sorting order instructions issued, and then enters the buffer area to wait for merging;

[0011] When the parallel sorting of cigarette orders is completed in all buffer areas, the cigarettes are serially merged and plastic-sealed to complete the automatic sorting of cigarettes.

[0012] According to other embodiments, the present disclosure also adopts the following technical solutions:

[0013] A cigarette order-based smoke channel allocation optimization system, comprising:

[0014] A data collection module is used to obtain the cigarette order information of the current day and match the order information with the stored cigarette specifications;

[0015] The model building module is used to build a product specification flue gas distribution optimization model to divide the cigarette specifications of each flue gas in the sorting area with the goal of achieving the shortest total time for cigarette order sorting;

[0016] The host computer is used to divide the cigarette order into several cigarette sub-orders and issue sorting order instructions;

[0017] The automatic sorting module includes a horizontal sorter and a channel sorter, which are used to perform parallel sorting according to the sorting order instructions issued and then enter the buffer area to wait for merging. When the parallel sorting of cigarette orders is completed in all buffer areas, the cigarettes are merged in series and plastic-sealed to complete the automatic sorting of cigarettes.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The present invention establishes a sorting time model with the goal of minimizing the total sorting time by analyzing the unique parallel picking and serial merging sorting method of the automatic cigarette sorting line. After comparing and analyzing various factors affecting the total sorting time, it is determined that the waiting time is the decisive factor affecting the total time, and a further analysis of the waiting time is conducted.

[0020] The present invention introduces the optimization problem of cigarette specifications into the sorting time model, and changes the objective function to solving the waiting time. Based on the relationship between specification similarity and sorting volume, the sum-difference relationship between item sorting volumes is introduced, the solution of specification similarity coefficients is improved, and the positive correlation between the two is proved. Finally, the objective function is determined as the minimum sum of specification similarity coefficients of each sorting area; by combining the specification optimization algorithm - the improved K-means dynamic clustering algorithm, the actual data is simulated and verified, the total operation time of the sorting system is reduced, and the work efficiency of the cigarette distribution center is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0022] Figure 1 A schematic diagram showing a process flow of implementing the disclosed method;

[0023] Figure 2 A schematic diagram showing the sorting operation flow of the automatic cigarette sorting line disclosed in the present invention;

[0024] Figure 3 A schematic diagram showing the merging process of cigarette orders disclosed in the present invention; DETAILED DESCRIPTION

[0025] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0026] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0027] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0028] The automatic cigarette sorting line is mainly composed of multiple horizontal sorting machines, channel sorting machines, conveyor belts, coding equipment, packaging machines, etc. The efficiency of the entire line is mainly determined by the horizontal sorting machines, channel sorting machines and conveyor belts. Channel sorting machines, a channel sorting machine can only sort one specification of cigarettes, and one smoke dispensing action can produce any number of cigarettes from 1 to 5. The mode of the horizontal sorting machine (hereinafter referred to as the sorting area) to spit out cigarettes onto the conveyor belt according to the order. A horizontal sorting machine can sort up to 10 specifications of cigarettes, and one smoke dispensing action can only produce 1 cigarette. In the automatic cigarette sorting line, each cigarette sorting machine performs sorting operations in parallel as an independent partition, and then merges in series after all the cigarettes required in an order are sorted. The specification flue distribution of the automatic cigarette sorting line refers to the reasonable distribution of specification cigarettes to the sorting area. On the automatic cigarette sorting line, due to parallel sorting, the total time required to sort a cigarette order depends on the sorting completion time of the sorting area with the longest sorting time. By optimizing and adjusting the cigarette specifications in each sorting area, the cigarette sorting tasks between different sorting areas are balanced, thereby reducing the completion time of the longest sorting area as a whole and improving the operating efficiency of the entire automatic cigarette sorting line.

[0029] Example 1

[0030] The present disclosure provides a method for optimizing cigarette duct allocation based on cigarette orders in one embodiment, such as Figure 1 As shown, including:

[0031] Step 1: Obtain the cigarette order information for the current day, and match the order information with the stored cigarette specifications;

[0032] Step 2: To minimize the total time required for sorting cigarette orders, a product specification flue gas distribution optimization model is constructed to divide the cigarette specifications of each flue gas in the sorting area;

[0033] Step 3: Divide the cigarette order into several cigarette sub-orders. Each sorting area performs parallel sorting according to the sorting order instructions issued, and then enters the buffer area to wait for merging;

[0034] Step 4: When the parallel sorting of cigarette orders is completed in all buffer areas, the cigarettes are serially merged and plastic-sealed to complete the automatic sorting of cigarettes.

[0035] During the sorting operation of the automatic cigarette sorting line, first, the control system matches the daily cigarette order information to the cigarette specifications stored in the flue of each sorting area, and divides a cigarette order into several sub-orders suitable for the sorting mode of the automatic cigarette sorting line. Each sorting area performs parallel sorting and cache waiting for merging according to the sub-orders issued by the control system. When an order sorting operation is completed in all cache areas, the buffer areas open the baffles in turn and the goods merge serially onto the main conveyor belt, and are transported to the plastic packaging machine through the main conveyor belt for plastic sealing.

[0036] During the sorting process, the flues in the cigarette sorting area start sorting and enter the buffer area to wait for merging, which should meet two conditions:

[0037] (1) The previous cigarette order has completed the merging operation, and there are no cigarettes left in the buffer area;

[0038] (2) The cigarette specifications that need to be sorted for the current cigarette order exist in this sorting area.

[0039] The confluence of each cache area also needs to meet two conditions:

[0040] (1) The sorting tasks of the current order in all sorting areas are completed and all cigarettes fall into the buffer area;

[0041] (2) The last cigarette of the previous order passes over the baffle of this buffer area on the main conveyor belt, which ensures that there will be no stacking during the merging process of cigarettes.

[0042] For the constructed optimization model of flue gas distribution for different specifications, the following conditions are set for the construction of the model:

[0043] (1) The appearance and shape of the cigarettes of various specifications sorted in the sorting area are uniform, and the volume of the cigarettes of various specifications is consistent.

[0044] (2) Each sorting area sorts the orders according to the order instructions. After all sorting areas have completed the sorting and merging of the current order, they can start the sorting task of the next order;

[0045] (3) The configuration specifications of each sorting area are unified. The time interval between sorting two adjacent cigarettes from the same flue of the horizontal machine is the same as the time between sorting two adjacent cigarettes from different flues in the same order.

[0046] (4) Any cigarette specification can be assigned to any channel, and the number of cigarette specifications sorted must correspond to the number of flues used;

[0047] (5) The buffer area can hold the number of cigarettes in the sorting area for the current order at one time;

[0048] (6) The time it takes for cigarettes to be sorted out from the flue and fall through the conveyor belt to the buffer area is not calculated;

[0049] (7) The time it takes for cigarettes to fall from the buffer area to the main conveyor belt is negligible;

[0050] (8) The situation of waiting for replenishment during the sorting process of cigarette equipment is not considered;

[0051] (9) The cigarettes on the main conveyor belt are arranged neatly without any stacking;

[0052] (10) The running speed of the main conveyor belt is considered as a constant;

[0053] (11) The distance between orders on the main conveyor belt is equal to the distance between two cigarettes;

[0054] (12) The distance between sorting areas is fixed;

[0055] Specifically, the optimization model of product and specification flue gas distribution is constructed. The product and specification flue gas distribution strategy is to reasonably distribute all cigarette specifications that need to be sorted to the sorting channels of each sorting area to achieve the goal of minimizing the total time for sorting cigarette orders. The total order sorting processing time is composed of the confluence time and waiting time of the cigarette sorting line. The length of the confluence time is determined by the total amount of cigarette orders to be sorted. The variable that affects the total time for sorting cigarette orders is the waiting time.

[0056] The waiting time depends on the sorting completion time and the confluence start time. When the sorting completion time is shorter than or equal to the confluence start time, the baffle of the sorting area can be opened in time, and the cigarettes can be smoothly confluenced without waiting time. When the sorting completion time is longer than the confluence start time, the waiting time is equal to the difference between the two; when the waiting time of all orders is minimized, the total processing time of the order is the shortest; otherwise, the total processing time of the order is longer; the goal of the flue optimization model of cigarette specifications is converted to the minimum waiting time for all cigarette orders.

[0057] The goal of the smoke duct optimization model for cigarette specifications is converted to the minimum waiting time for all cigarette orders:

[0058]

[0059] i represents the order number, i=1...n; j represents the sorting area number, j=1...m; T ij 2 It represents the waiting time for cigarettes of order i to merge in sorting area j.

[0060] The calculation of the waiting time for a single order depends on the previous order and the cigarette confluence time and waiting time of each sorting area involved. It is a very complex cumulative recursive process.

[0061] The relationship between the flue distribution and waiting time of cigarette specifications is expressed by the cigarette specification similarity coefficient. It is proved that the waiting time is positively correlated with the sum of the specification similarity coefficients in each cigarette sorting area, that is, the sorting area with the largest sum of cigarette specification similarity coefficients has the longest waiting time; conversely, the sorting area with the smallest sum of cigarette specification similarity coefficients has the shortest waiting time.

[0062] Cigarette specifications with high similarity should be placed in different sorting areas for sorting. By reasonably allocating cigarette specifications in the flue of the sorting area, the cigarette similarity coefficient and the cigarette sorting area with the longest waiting time are used to adjust and allocate the internal cigarette specifications to reduce the waiting time, so that the sum of the cigarette similarity coefficients in each sorting area becomes balanced, thereby reducing the waiting time of the entire cigarette automatic sorting line. Based on this, a corresponding improved dynamic clustering algorithm is designed to solve the model, reducing the computational complexity of the model and the difficulty of optimization.

[0063] In the automatic cigarette sorting line, the cigarette sorting area sorts cigarettes piece by piece through the command issued by the host computer, and the order sorting time is proportional to the number of cigarette specifications. Therefore, when the number of times two cigarette specifications appear in the same order at the same time represents the similarity coefficient between the two, the two specifications with larger similarity coefficients should appear in different sorting areas, so that the two specifications can be sorted in parallel and the sorting completion time can be reduced. Conversely, cigarette specifications with small similarity coefficients can be assigned to the same sorting area.

[0064] At the same time, due to the limitation of sorting mode, the sorting volume of cigarette specifications also affects the total time of order sorting. When the sum of the sorting volumes of two cigarette specifications is larger, they need to be allocated to different sorting areas, so as to ensure the balance of sorting tasks in each sorting area and reduce the waiting time. On the contrary, the larger the difference between the two, the more they can be placed in the same sorting area of ​​the automatic cigarette sorting line.

[0065]

[0066] in,

[0067] S ab represents the similarity coefficient between item a and item b; Q ia Indicates the sorting quantity of specification a in order i;

[0068] According to formula (2), when the sum of the sorting quantities of two cigarette specifications in the same order is larger and the difference in the sorting quantities of the two cigarette specifications is smaller, the sum of the similarity coefficients of the two cigarette specifications is larger, which means that the two cigarette specifications should be allocated to different sorting areas; otherwise, they can be allocated to the same sorting area.

[0069] In the automatic cigarette sorting line, assume that there are two sorting areas j-1 and j in all cigarette orders, and the sum of the similarity coefficients of cigarette specifications in these two sorting areas is equal, that is, SUM j-1 =SUM j Then the sum of the similarity coefficients of cigarette specifications of the i-th order in sorting area j-1 and sorting area j will have the following three situations.

[0070] (1)SUM ij-1 >SUM ij

[0071] When the sum of the similarity coefficients of cigarette specifications of the i-th order in sorting area j-1 is greater than that in sorting area j, it means that the sorting quantity or the number of cigarette specifications to be sorted of the i-th order in sorting area j-1 is greater than the sorting quantity or the number of cigarette specifications to be sorted in sorting area j, so the waiting time of order i in sorting area j-1 is longer than the waiting time in sorting area j.

[0072] (2)SUM ij-1 <SUM ij

[0073] When the sum of the similarity coefficients of cigarette specifications of the i-th order in sorting area j-1 is less than that in sorting area j, it means that the sorting quantity or the number of cigarette specifications to be sorted of the i-th order in sorting area j-1 is less than the sorting quantity or the number of cigarette specifications to be sorted in sorting area j, so the waiting time of order i in sorting area j-1 is less than the waiting time in sorting area j.

[0074] (3)SUM ij-1 =SUM ij

[0075] When the sum of the similarity coefficients of cigarette specifications of the i-th order in sorting area j-1 is equal to that of sorting area j, it means that the sorting quantity or the number of cigarette specifications to be sorted of the i-th order in sorting area j-1 is equal to the sorting quantity or the number of cigarette specifications to be sorted of sorting area j, so the waiting time of order i in sorting area j-1 is equal to the waiting time in sorting area j.

[0076] Based on this, the waiting time of cigarette orders in each sorting area is positively correlated with the sum of the similarity coefficients of cigarette specifications in each partition. Because the total processing time of an order depends on the length of the waiting time, it can be inferred that the total processing time of an order is also positively correlated with the sum of the similarity coefficients of cigarette specifications in each partition. Therefore, in the automatic cigarette sorting line, the smaller the sum of the similarity coefficients of cigarette specifications in all sorting areas, the shorter the total processing time of the order. Therefore, the optimization goal of the automatic cigarette sorting line can be converted to reducing the sum of the similarity coefficients of cigarette specifications in all sorting areas, as described in the following formula:

[0077]

[0078] In formula (3):

[0079]

[0080]

[0081] The constraints of the objective function are:

[0082]

[0083] The constraint of formula (4) indicates that product specification a is allocated to and only to one sorting area.

[0084]

[0085] The constraint of formula (5) indicates that product specification b is allocated to and only to one sorting zone.

[0086] m>1, m is an integer (6)

[0087] The constraint in equation (6) indicates that the number of sorting zones is greater than one.

[0088] k=l·m (7)

[0089] The constraint of formula (7) indicates that k is the total number of cigarette sorting channels in all sorting areas of the automatic cigarette sorting line, which is also equal to the total number of cigarette specifications.

[0090] Through this model, the sum of the similarity coefficients of product specifications in all sorting areas can be calculated. Through the combination of different product specifications, the similarity coefficients of corresponding product specifications will also change. At this time, the corresponding total sorting time is also the smallest. Then this product specification is the optimal solution. After continuous searching and comparison, the smallest product specification combination is found.

[0091] The similarity coefficient expression is modified according to formula (2). On this basis, the K-means clustering algorithm is introduced, and the cigarette specification allocation problem is solved by combining the improved K-means dynamic clustering algorithm according to the characteristics of the model.

[0092] Dynamic Clustering Algorithm

[0093] Step 1: Calculate the similarity coefficients between all cigarette specifications, where the similarity coefficient S between specification a and specification b is ab =S ba , so there are w = k (k-1) / 2 S ab . All S ab Sort in ascending order and store in list s, that is:

[0094]

[0095] Step 2: Allocation of the two cigarette specification numbers corresponding to each similarity coefficient in the list s to each picking area in turn. Note that the cigarette specification numbers cannot be repeated during allocation. If repeated, the two specification numbers corresponding to the next similarity coefficient will be postponed. The sorting stops when all sorting areas have obtained two initially allocated specification numbers.

[0096] Step 3: Find the first Satisfy a c or b c At least one cigarette specification is not assigned to any sorting area. for a c The sum of similarity coefficients of the cigarette specification numbers assigned in the h-th sorting area. The sum of similarity coefficients of the cigarette specification numbers assigned in the h-th sorting area is expressed as:

[0097]

[0098] In formula (9): p h The number of cigarette specifications already in picking area H.

[0099] The number of cigarette specifications already in sorting area h must be less than the limit on the number of sorting channels in the sorting area, otherwise it will not be considered.

[0100] If a c and b c If there is only one unassigned picking zone, all picking zones are calculated. or Select the sorting area number h corresponding to the minimum value, and set a c or b c Assigned to this sorting area.

[0101] Step 4: Repeat step 3 until all cigarette specifications are assigned. Record the item assignment results and L value.

[0102]

[0103] From the solution steps of the algorithm, it can be seen that the algorithm can find a better solution quickly, but it limits the search range of the solution by finding the smallest or clustering standard, and it is easy to fall into the local optimal solution.

[0104] The core of the K-means clustering algorithm is to determine the distance between each specification and each sorting area. If the distance between a specification and other sorting areas is smaller, move the specification to the corresponding sorting area and recalculate the sum of items in the area. Repeat this process until no specification can be moved. According to the clustering goal, the evaluation function is used to replace the sum of specifications in the sorting area, as shown in the following formula (11):

[0105]

[0106] Formula (11) indicates that if the product specification a c Transferred from sorting area h to sorting area g, item specification a c The difference between the similarity coefficients of the cigarette specifications in the two sorting areas and the sum of the similarity coefficients of the cigarette specifications in the two sorting areas. If the difference is greater than 0, the objective function decreases, and the specification a c It should be transferred to the sorting area g, otherwise it will not be transferred. Based on this, the steps of the improved K-means dynamic clustering algorithm are as follows:

[0107] Step 1 uses the solution of the dynamic clustering algorithm as the initial item allocation.

[0108] Step 2: Select the first sorting area, start from the first specification in the sorting area, and calculate the evaluation functions of this specification and other sorting areas in turn. If the maximum value is greater than 0, move the item number to the corresponding picking area; otherwise, select the next specification and repeat the previous calculation and comparison work.

[0109] Step 3: If the evaluation functions of all product specifications in the first sorting area and other sorting areas are less than 0, directly select the next sorting area and repeat the calculation and comparison work in step 2 until there are items that can be moved or the product specifications of all sorting areas are traversed.

[0110] Step 4 Repeat steps 2 and 3 until there are no more gauges to move.

[0111] Example 2

[0112] In one embodiment of the present disclosure, a smoke duct allocation optimization system based on cigarette orders is provided, comprising: a data acquisition module for acquiring cigarette order information of a current day and matching the order information with stored cigarette specifications;

[0113] The model building module is used to build a product specification flue gas distribution optimization model to divide the cigarette specifications of each flue gas in the sorting area with the goal of achieving the shortest total time for cigarette order sorting;

[0114] The host computer is used to divide the cigarette order into several cigarette sub-orders and issue sorting order instructions;

[0115] The automatic sorting module includes a horizontal sorter and a channel sorter, which are used to perform parallel sorting according to the sorting order instructions issued and then enter the buffer area to wait for merging. When the parallel sorting of cigarette orders is completed in all buffer areas, the cigarettes are merged in series and plastic-sealed to complete the automatic sorting of cigarettes.

[0116] Based on the above system, the automatic cigarette sorting method based on the improved dynamic clustering algorithm is implemented. During the operation of the automatic cigarette sorting line, there are two main operation processes: smoke output and confluence. First, each sorting area sorts the specifications of cigarettes for each order in the sorting area into the buffer area according to the current order requirements. When all specifications of cigarettes for the order are sorted, the buffer area opens the buffer area baffles in turn to merge the cigarettes in each buffer area onto the main conveyor belt. At this point, the entire sorting operation of the current order is completed, and then the above steps are repeated to start the sorting operation of the next order.

[0117] (1) Prerequisites for the automatic cigarette sorting line to merge:

[0118] When order i enters the buffer area for merging after completing the sorting task in sorting area j, the following two prerequisites must be met:

[0119] ① The cigarettes of the specifications that need to be sorted for order i in sorting area j have all been sorted to the buffer area. The time it takes for all the cigarettes of this order to be sorted from the sorting area to the buffer area is called the sorting completion time of the cigarettes.

[0120] ② All cigarettes in the buffer area of ​​the previous sorting area have completed confluence, and the last cigarette on the main conveyor belt has passed over the baffle of the buffer area of ​​the current sorting area. The time when the last cigarette in the previous sorting area has completed confluence and passed over the baffle of the buffer area of ​​the sorting area is called the confluence start time of the cigarettes.

[0121] (2) Situations where waiting time occurs:

[0122] During the merging process of cigarettes in the orders of the automatic cigarette sorting line, two situations may occur in the sorting area j, which will cause waiting time.

[0123] ① When all the cigarettes in the previous sorting area (j-1) have been merged, and the last cigarette arranged on the main conveyor belt has just passed the baffle of the buffer area of ​​sorting area j, if the cigarette sorting task in sorting area j has not been completed, and some cigarettes have not entered the buffer area, the cigarette sorting completion time is longer than the cigarette merging start time. This means that sorting area j needs to wait for all cigarettes in this partition to be sorted and enter the buffer area before it can open the baffle to merge the cigarettes in this partition. In this case, the time generated due to the inability to merge in time is called the merging waiting time. The generation of merging waiting time occurs in the gaps formed on the main conveyor belt on the automatic cigarette sorting line. As a result, the total processing time of the order increases and the operating efficiency of the automatic cigarette sorting line decreases.

[0124] ② When sorting area j has sorted all the cigarettes in the current order and entered the buffer area to wait for merging, at this time, the last cigarette merged in sorting area (j-1) on the main conveyor belt has not yet crossed the buffer area baffle of sorting area j. At this time, the cigarette sorting completion time is less than the cigarette merging start time. This means that sorting area j needs to wait for the last cigarette merged in the previous sorting area to cross the buffer area baffle of sorting area j before it can merge the cigarettes that have been sorted and waiting in sorting area j. At this time, the time caused by the inability to merge in time is called the sorting waiting time of order i in sorting area j.

[0125] In the sorting process of the automatic cigarette sorting line, the most ideal sorting state is that the cigarette sorting completion time of each sorting area is less than the cigarette confluence start time. In this case, it can be guaranteed that the cigarettes in each sorting area can be confluenced in time, and the cigarettes are arranged densely on the main conveyor belt, which can maximize the efficiency of the sorting system. At this time, there is no sorting waiting time, and the total sorting time of the order is only affected by the confluence waiting time. Next, the waiting time is further analyzed.

[0126] In the automatic cigarette sorting line, the process of cigarette order merging is as follows: Figure 3 As shown. Figure 3 As shown in the figure, the merging process of the i-th order and the (i+1)-th order is described. In the i-th order, the merging time of cigarettes in the buffer area of ​​sorting area j is , the cigarette sorting completion time in the buffer area of ​​sorting area j is When all the cigarettes of the i-th order have been merged in sorting area j, the time when the last cigarette in sorting area j passes through sorting area (j+1) from the main conveyor belt is t2. Therefore, the start time of the merging of cigarettes in the buffer area of ​​sorting area (j+1) is equal The sum of the two times t1 and t2. Figure 2 As shown, in the i-th order, the cigarette sorting completion time in the buffer area of ​​sorting area (j+1) is longer than the merging start time, so waiting time is generated Similarly, the (i+1)th order also has a waiting time in the (j+1)th sorting area.

[0127] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0128] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0131] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

[0132] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for optimizing smoke duct allocation based on cigarette orders, characterized in that: include Obtaining cigarette order information for the current day, and matching the order information with stored cigarette specifications; To minimize the total time of cigarette order sorting, a product specification flue allocation optimization model is constructed to divide the cigarette specifications of each flue in the sorting area; The cigarette order is divided into several cigarette sub-orders. Each sorting area performs parallel sorting according to the sorting order instructions issued, and then enters the buffer area to wait for merging; When the parallel sorting of cigarette orders is completed in all buffer areas, the cigarettes are serially merged and plastic-sealed to complete the automatic sorting of cigarettes; According to the characteristics of the model and the improved K-means dynamic clustering algorithm, the cigarette specification allocation problem is solved; The steps of the improved K-means dynamic clustering algorithm are as follows: Step 1: Use the solution of the dynamic clustering algorithm as the initial product specification allocation; Step 2: Select the first sorting area, start from the first product specification in the sorting area, and calculate the evaluation functions of this product specification and other sorting areas in turn. If the maximum value is greater than 0, move the product specification to the corresponding picking area; otherwise, select the next product specification and repeat the previous calculation and comparison work; Step 3: If the evaluation functions of all specifications of the first sorting area and other sorting areas are less than 0, directly select the next sorting area and repeat the calculation and comparison work in step 2 until there is a specification that can be moved or the specifications of all sorting areas are traversed; Step 4: Repeat steps 2 and 3 until there are no more items to move; According to the clustering goal, the evaluation function is used to replace the specifications and the sum of the sorting areas: , indicating that if the product specification By sorting area Transfer to sorting area , Specifications The difference between the sum of similarity coefficients of the cigarette specifications in the two sorting areas; The relationship between the smoke channel distribution and waiting time of cigarette specifications is expressed by the cigarette specification similarity coefficient; The similarity coefficient between specification a and specification b is: in, ; Indicates the sorting quantity of specification a in order i.

2. A method for optimizing smoke duct allocation based on cigarette orders as claimed in claim 1, characterized in that: Each sorting area flue starts sorting and enters the buffer area to wait for confluence when the following two conditions are met: (1) The previous cigarette order has been merged and there are no cigarettes left in the buffer area; (2) The cigarette specifications that need to be sorted for the current cigarette order exist in this sorting area.

3. The method for optimizing smoke duct allocation based on cigarette orders according to claim 1, characterized in that: The following two conditions must be met for each cache area to merge: (1) The sorting tasks of the current order in all sorting areas are completed and all cigarettes fall into the buffer area; (2) The last cigarette of the previous order has passed this buffer area.

4. The method for optimizing smoke duct allocation based on cigarette orders according to claim 1, characterized in that: The appearance and shape of the cigarettes of various specifications sorted in each sorting area are uniform, and the volume of the cigarettes of various specifications is consistent.

5. The method for optimizing smoke duct allocation based on cigarette orders according to claim 1, characterized in that: Any cigarette specification can be randomly assigned to a channel, and the number of cigarette specifications sorted must correspond to the number of flues used.

6. The method for optimizing smoke duct allocation based on cigarette orders according to claim 1, characterized in that: The total processing time of order sorting is composed of the confluence time and waiting time of the cigarette sorting line. The length of the confluence time is determined by the total number of cigarette orders to be sorted. The variable that affects the total sorting time of cigarette orders is the waiting time.

7. A method for optimizing smoke duct allocation based on cigarette orders as claimed in claim 6, characterized in that: The length of the waiting time depends on the sorting completion time and the merging start time. When the sorting completion time is shorter than or equal to the merging start time, the baffle of the sorting area can be opened in time, the cigarettes can merge smoothly, and there is no waiting time.

8. A method for optimizing smoke duct allocation based on cigarette orders as claimed in claim 7, characterized in that: When the sorting completion time is longer than the merging start time, the waiting time is equal to the difference between the two; when the waiting time of all orders is minimized, the total order processing time is the shortest; conversely, the total order processing time is longer; the goal of the flue optimization model for cigarette specifications is converted to finding the minimum waiting time for all cigarette orders.

9. A method for optimizing smoke duct allocation based on cigarette orders as claimed in claim 8, characterized in that: The goal of the smoke duct optimization model for cigarette specifications is converted to the minimum waiting time for all cigarette orders: i Indicates the order number. i =1...n; j Indicates the sorting area number. j =1...m; T ij 2 Indicates order i In the sorting area j The confluence waiting time of medium cigarettes.

10. A smoke duct allocation optimization system based on cigarette orders, characterized in that: include: A data collection module is used to obtain the cigarette order information of the current day and match the order information with the stored cigarette specifications; A model building module is used to build a product specification flue gas distribution optimization model to divide the cigarette specifications of each flue gas in the sorting area with the goal of minimizing the total time of cigarette order sorting; According to the characteristics of the model and the improved K-means dynamic clustering algorithm, the cigarette specification allocation problem is solved; The steps of the improved K-means dynamic clustering algorithm are as follows: Step 1: Use the solution of the dynamic clustering algorithm as the initial product specification allocation; Step 2: Select the first sorting area, start from the first product specification in the sorting area, and calculate the evaluation functions of this product specification and other sorting areas in turn. If the maximum value is greater than 0, move the product specification to the corresponding picking area; otherwise, select the next product specification and repeat the previous calculation and comparison work; Step 3: If the evaluation functions of all specifications of the first sorting area and other sorting areas are less than 0, directly select the next sorting area and repeat the calculation and comparison work in step 2 until there is a specification that can be moved or the specifications of all sorting areas are traversed; Step 4: Repeat steps 2 and 3 until there are no more items to move; According to the clustering goal, the evaluation function is used to replace the specifications and the sum of the sorting areas: , indicating that if the product specification By sorting area Transfer to sorting area , Specifications The difference between the sum of similarity coefficients of the cigarette specifications in the two sorting areas; The relationship between the smoke channel distribution and waiting time of cigarette specifications is expressed by the cigarette specification similarity coefficient; The similarity coefficient between specification a and specification b is: in, ; Indicates the sorting quantity of specification a in order i; The upper computer is used to divide the cigarette order into several cigarette sub-orders and issue sorting order instructions; The automatic sorting module includes a horizontal sorter and a channel sorter, which are used to perform parallel sorting according to the sorting order instructions issued and then enter the buffer area to wait for merging. When the parallel sorting of cigarette orders is completed in all buffer areas, the cigarettes are merged in series and plastic-sealed to complete the automatic sorting of cigarettes.

Images