Method and system for production scheduling optimization in a packaging printing plant

Abstract

The present disclosure relates to a production scheduling optimization method and system in a packaging printing factory, the method comprising: obtaining time constraints of each task in a plurality of orders to be scheduled, resource consumption of the each task; based on resource inventory and the resource consumption, adjusting a default schedule to obtain a first schedule; based on the resource consumption, the first schedule and the time constraints, using an order scheduling model to determine a target scheduling algorithm, and based on the target scheduling algorithm, sorting the plurality of orders to obtain a scheduling result. In the present disclosure, in the process of scheduling, considering resource inventory, customer demand (order) and production capacity, the relationship between production, supply and demand can be coped with, and the time constraints of each task in the order are also considered, so that the probability of obtaining an optimal order scheduling which reasonably utilizes resources, saves time and meets customer demand is improved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a production scheduling optimization method and system in a packaging and printing factory. Background Technology

[0002] In manufacturing, it is crucial to schedule orders effectively and produce according to the schedule. Effective scheduling can help companies improve production efficiency, reduce production costs, promote collaboration among multiple departments, and enhance customer satisfaction.

[0003] Order scheduling involves many aspects, including the effective allocation of resources, the rational formulation of production plans, and the timely fulfillment of customer needs, making it a crucial link in the manufacturing industry. However, the scheduling process involves many factors; the relationship between production, supply, and demand is flexible and ever-changing, making it difficult to obtain reasonable and accurate scheduling results in a timely manner. Summary of the Invention

[0004] In view of this, this disclosure proposes a packaging and printing order scheduling scheme.

[0005] According to one aspect of this disclosure, a method for scheduling packaging and printing orders is provided, comprising: obtaining the time constraints of each task in a plurality of orders to be scheduled and the resource consumption of each task; adjusting the default schedule based on resource inventory and the resource consumption to obtain a first schedule; determining a target scheduling algorithm using an order scheduling model based on the resource consumption, the first schedule and the time constraints, and sorting the plurality of orders based on the target scheduling algorithm to obtain a scheduling result.

[0006] In one possible implementation, obtaining the time constraints of each task in the multiple orders to be scheduled includes: obtaining the personnel time, material time, and equipment time occupied by each task; for a single task, determining the maximum value between the material time and the equipment time; using the sum of the maximum value and the personnel time of the single task as the execution time of the single task, to obtain the execution time of each task; obtaining the time difference between the start time of each task and the start time of its predecessor, and using the time difference as the lag time of each task.

[0007] In one possible implementation, adjusting the default schedule based on resource inventory and resource consumption to obtain a first schedule includes: traversing the resource consumption of the multiple orders according to the default schedule, accumulating the resource consumption of each order and determining the remaining resource inventory; if the remaining resource inventory is insufficient, obtaining a new remaining resource inventory in response to a received inventory replenishment, and continuing the traversal until the resource consumption of all orders has been traversed; if the remaining resource inventory is insufficient, filtering out the current order in response to a received skip order operation, and continuing the traversal until the resource consumption of all orders has been traversed; if only an inventory replenishment is received during the traversal, using the default schedule as the first schedule; if at least one skip order operation is received during the traversal, removing the filtered-out orders from the default schedule to obtain the first schedule.

[0008] In one possible implementation, the training process of the order scheduling model includes: obtaining an initial model, wherein the initial model pre-sets multiple scheduling algorithms; using the initial model, determining a preferred algorithm based on a single training sample; using the preferred algorithm to schedule orders in the single training sample to obtain a first scheduling result; determining the similarity between the first scheduling result and the ground truth of the single training sample; determining a reward value based on the similarity; adjusting the probability values ​​corresponding to the multiple scheduling algorithms based on the reward value until the similarity is not less than a similarity threshold or all of the multiple scheduling algorithms are determined to be preferred algorithms at least once; using the initial model to process other training samples; until the model convergence condition is met to obtain the order scheduling model.

[0009] In one possible implementation, the method further includes: determining the sample resource consumption of each sample task based on the sample personnel time, sample material time, and sample equipment time occupied by each sample task in multiple sample orders, as well as the personnel consumption per unit time, material consumption per unit time, and equipment consumption per unit time of each sample task; determining the sample execution time of each sample task based on the sample personnel time, sample material time, and sample equipment time; using the sample time difference between the start time of the sample task and the start time of the previous sample task as the sample lag time of the sample task, and using the sample execution time and the sample lag time as the sample time constraint of the sample task; using the order acceptance order of each sample task as the sample schedule, and using the sample resource consumption, sample time constraint, and sample schedule corresponding to the multiple sample orders as a single training sample.

[0010] In one possible implementation, the method further includes: reading multiple initial orders; detecting the initial orders and removing empty orders to obtain the multiple orders to be scheduled.

[0011] In one possible implementation, the method further includes: sorting the first orders executed in parallel in the scheduling result according to the order of receipt, and updating the scheduling result to obtain the updated scheduling result.

[0012] According to another aspect of this disclosure, a packaging printing order scheduling system is provided, comprising:

[0013] A time constraint and resource consumption determination unit is used to obtain the time constraint of each task in multiple orders to be scheduled and the resource consumption of each task.

[0014] The first scheduling determination unit is used to adjust the default schedule based on the resource inventory and the resource consumption to obtain the first schedule.

[0015] The scheduling result determination unit is used to determine a target scheduling algorithm based on the resource consumption, the first schedule, and the time constraint using an order scheduling model, and to sort the multiple orders based on the target scheduling algorithm to obtain a scheduling result.

[0016] In one possible implementation, the time constraint and resource consumption determination unit is further configured to:

[0017] Obtain the personnel time, material time, and equipment time consumed by each of the tasks;

[0018] For a single task, determine the maximum value between material processing time and equipment processing time;

[0019] The sum of the maximum value and the personnel time of the individual task is used as the execution time of the individual task, so as to obtain the execution time of each task.

[0020] Obtain the time difference between the start time of each task and the start time of its predecessor, and use the time difference as the lag time of each task.

[0021] In one possible implementation, the first scheduling determination unit is further configured to:

[0022] According to the default schedule, iterate through the resource consumption of the multiple orders, accumulate the resource consumption of each order, and determine the remaining resource inventory.

[0023] If the resource inventory is insufficient, in response to the received inventory replenishment, a new resource inventory is obtained, and the traversal continues until the resource consumption of all orders is traversed.

[0024] If the remaining resource inventory is insufficient, in response to the received skip order operation, the current order is filtered out and the traversal continues until the resource consumption of all orders has been traversed.

[0025] In the traversal, if only the inventory replenishment quantity is received, the default schedule is used as the first schedule;

[0026] During the traversal, if at least one skip order operation is received, the orders that have been filtered out are removed from the default schedule to obtain the first schedule.

[0027] In one possible implementation, the training process of the order scheduling model includes:

[0028] Obtain an initial model, in which multiple scheduling algorithms are preset;

[0029] Using the initial model, an optimal algorithm is determined based on a single training sample;

[0030] The orders in the single training sample are scheduled using an optimization algorithm to obtain the first scheduling result;

[0031] Determine the similarity between the first scheduling result and the ground truth of the individual training sample;

[0032] The reward value is determined based on the similarity.

[0033] Based on the reward value, adjust the probability values ​​corresponding to the various scheduling algorithms until the similarity is not less than the similarity threshold or all of the various scheduling algorithms are determined to be the preferred algorithm at least once. Use the initial model to process other training samples until the model convergence condition is met to obtain the order scheduling model.

[0034] In one possible implementation, the training process of the order scheduling model further includes:

[0035] Based on the sample personnel time, sample material time, and sample equipment time occupied by each sample task in multiple sample orders, as well as the personnel consumption per unit time, material consumption per unit time, and equipment consumption per unit time of each sample task, the sample resource consumption of each sample task is determined.

[0036] Based on the time allotted for each sample person, the time allotted for each sample material, and the time allotted for each sample device, the sample execution time for each of the aforementioned sample tasks is determined.

[0037] The difference between the start time of the sample task and the start time of the previous sample task is taken as the sample lag time of the sample task, and the sample execution time and the sample lag time are taken as the sample time constraint of the sample task.

[0038] The order of each sample task is used as the sample schedule, and the sample resource consumption, sample time constraint, and sample schedule corresponding to the multiple sample orders are used as a single training sample.

[0039] In one possible implementation, the system further includes:

[0040] The initial order reading unit is used to read multiple initial orders;

[0041] The pending order determination unit is used to detect the initial order and remove empty orders to obtain multiple pending orders.

[0042] In one possible implementation, the system further includes:

[0043] The scheduling result update unit is used to sort the first orders that are executed in parallel in the scheduling result according to the order of receiving the orders, and update the scheduling result to obtain the updated scheduling result.

[0044] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-described method when executing instructions stored in the memory.

[0045] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is provided that stores computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the above-described method.

[0046] According to another aspect of this disclosure, a computer program product is provided, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0047] In this embodiment, during the scheduling of orders to be scheduled, resource inventory (personnel, materials, and equipment), customer demand (orders), and production capacity (equipment and personnel) are considered. This approach can address the changing relationships between production, supply, and demand. Furthermore, the time constraints of each task within the order are taken into account, thus increasing the probability of obtaining an optimal order schedule that rationally utilizes resources, saves time, and meets customer needs. In addition, this method uses an order scheduling model to directly process the resource consumption, the first schedule, and the time constraints, which can reduce manual intervention and improve efficiency.

[0048] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0049] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0050] Figure 1 This is a flowchart illustrating the packaging and printing order scheduling method provided in an embodiment of the present disclosure.

[0051] Figure 2 This is a flowchart illustrating another packaging and printing order scheduling method provided in an embodiment of this disclosure.

[0052] Figure 3 A schematic diagram of the structure of the packaging and printing order scheduling system provided in the embodiments of this disclosure.

[0053] Figure 4 A schematic diagram of the structure of an electronic device for scheduling packaging and printing orders provided in an embodiment of this disclosure. Detailed Implementation

[0054] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0055] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0056] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0057] In manufacturing, contract management, supply chain, and production departments are interconnected yet independent. This means that obtaining and fulfilling orders requires collaboration among these three departments, and sometimes more. Furthermore, customer requirements vary, and the resources used for different orders may differ in some areas while sharing others. Therefore, order scheduling is necessary to ensure that multiple orders can be executed sequentially, achieving the most efficient use of time and resources.

[0058] Compared to other manufacturing industries, the packaging and printing industry often features orders that are diverse and produced in small batches. This characteristic necessitates highly flexible and responsive order scheduling. Due to intense market competition, customers have increasingly stringent delivery time requirements, often expecting on-demand manufacturing and just-in-time delivery. This demands that packaging and printing companies' order scheduling be able to respond quickly to market changes and ensure timely order fulfillment.

[0059] Given the characteristics of the packaging and printing industry, higher requirements are placed on order scheduling in terms of accuracy, efficiency, flexibility, and rationality.

[0060] Figure 1 This is a flowchart illustrating the packaging and printing order scheduling method provided in an embodiment of this disclosure. Figure 1 As shown, the method includes:

[0061] S11, obtain the time constraints of each task in the multiple orders to be scheduled and the resource consumption of each task.

[0062] In this embodiment of the disclosure, a task can be a project or a step in the order execution process. For example, packaging printing may include tasks such as inkjet printing, sheet cutting, and hot stamping. Time constraints can be limitations on the task in the time dimension. For example, time constraints can be limitations on the start time, the end time, or the duration required to complete the task. Another example is a time constraint on the execution order of two tasks. Resources can include personnel, equipment, and materials. In this embodiment of the disclosure, one or more personnel can use one or more devices and consume one or more materials to perform a single task.

[0063] In this embodiment of the disclosure, resource consumption can be represented using a resource demand matrix. Columns of the resource demand matrix represent individual resources, and rows represent individual tasks. The element in the i-th row and j-th column of the resource demand matrix represents the consumption of the j-th resource when executing the i-th task.

[0064] The resource demand matrix can be obtained directly. Alternatively, the time information matrix and the unit resource demand matrix can be obtained first. Then, the elements with the same rows and columns in the time information matrix and the unit resource demand matrix are multiplied together to obtain the resource demand matrix.

[0065] In the time information matrix, columns represent individual resources and rows represent individual tasks. The element in the s-th row and t-th column of the time matrix represents the duration of resource t occupied by task s. Similarly, in the unit resource demand matrix, columns represent individual resources and rows represent individual tasks. The element in the s-th row and t-th column of the unit resource demand matrix represents the amount of resource t consumed by task s per unit time.

[0066] S12, based on resource inventory and resource consumption, adjust the default schedule to obtain the first schedule.

[0067] The default schedule can be a predefined order of order execution, but it may not be optimal in terms of material consumption or time consumption. For example, the default schedule can be the order in which orders are received.

[0068] Resource inventory can be an estimated amount of personnel, equipment, and materials remaining when pending orders begin execution; that is, resource surplus. Resource inventory can be obtained by subtracting the amount of personnel, equipment, and materials currently in use when pending orders begin execution (for non-pending orders) from the total amount of personnel, equipment, and materials respectively.

[0069] In this embodiment of the disclosure, resource consumption can be used to update resource inventory, and the updated resource inventory can be used to adjust the default schedule. For example, orders with lower resource consumption can be executed first, moving their execution order forward. This satisfies the delivery time requirements of orders with lower resource consumption while also allowing time to replenish resource inventory.

[0070] The default schedule can be adjusted to obtain the first schedule. The first schedule and the orders in the default schedule, as well as their order, can be the same or different. Resource inventory can be replenished during the process of obtaining the first schedule. After obtaining the first schedule, the resource inventory can support the execution of the orders in the first schedule.

[0071] S13, based on the resource consumption, the first schedule, and the time constraint, a target scheduling algorithm is determined using an order scheduling model, and based on the target scheduling algorithm, the multiple orders are sorted to obtain the scheduling result.

[0072] The order scheduling model can pre-store multiple scheduling algorithms. In this embodiment, a target scheduling algorithm can be determined based on resource consumption and time constraints. Using the scheduling method of the target scheduling algorithm, the orders to be scheduled are scheduled, and the resulting scheduling result can achieve the optimal scheduling result in terms of resource consumption, time consumption, and customer demand.

[0073] The order scheduling model can use a target algorithm to update the first schedule based on resource consumption, the initial schedule, and time constraints, thus obtaining the scheduling result. The scheduling result can include the execution order of the orders to be scheduled, and can also include a Gantt chart.

[0074] In this embodiment, during the scheduling of orders to be scheduled, resource inventory (personnel, materials, and equipment), customer demand (orders), and production capacity (equipment and personnel) are considered. This approach can address the changing relationships between production, supply, and demand. Furthermore, the time constraints of each task within the order are taken into account, thus increasing the probability of obtaining an optimal order schedule that rationally utilizes resources, saves time, and meets customer needs. In addition, this method uses an order scheduling model to directly process the resource consumption, the first schedule, and the time constraints, which can reduce manual intervention and improve efficiency.

[0075] In one possible implementation, obtaining the time constraints of each task in the multiple orders to be scheduled includes: obtaining the personnel time, material time, and equipment time occupied by each task; for a single task, determining the maximum value between the material time and the equipment time; using the sum of the maximum value and the personnel time of the single task as the execution time of the single task, to obtain the execution time of each task; obtaining the time difference between the start time of each task and the start time of its predecessor, and using the time difference as the lag time of each task.

[0076] In this embodiment of the disclosure, the time constraints include: the execution time of each task in the order to be scheduled, and the lag time of each task relative to the start time of its predecessor.

[0077] For example, a task may include the following stages: personnel operating equipment for pre-preparation, personnel operating equipment for material pre-processing, personnel operating equipment for material processing, and materials being placed naturally and waiting for completion. The above are merely examples, and this disclosure does not limit the specific stages of task execution.

[0078] In most cases, personnel operate equipment to process equipment and materials. This indicates that, in most situations, personnel and equipment work in parallel, so personnel time and equipment time are mostly concurrent, with a low probability of personnel or equipment operating independently. Materials, however, not only work in parallel with personnel and equipment but also have their own dedicated time. Therefore, when determining the execution duration of a task, personnel and equipment can be considered as participating in the task in parallel, determining the maximum value of personnel and equipment time. Material time can be considered as a separate, dedicated time. The sum of the maximum value and the material time is taken as the execution duration of a single task.

[0079] The time difference can be manually defined; alternatively, it can be the warm-up time of different devices in two upstream and downstream tasks. Furthermore, when the current task depends on the output of the previous task, the current task can only begin after the previous task has produced one unit of output. Therefore, the time difference can be the duration for the previous task to produce one unit of output.

[0080] In this embodiment, the maximum value of personnel time and material time can be used to calculate the execution time of a task, reducing the probability of repeated calculations and improving the accuracy of the execution time. Furthermore, adding the material time as a separately occupied time to the aforementioned maximum value reduces the probability of insufficient execution time estimation, further improving the accuracy of the execution time. In addition, the time constraint includes the execution time of each task and the lag time relative to the previous task, ensuring that the scheduling operation meets the actual needs of the order in terms of time, improving the rationality and accuracy of the scheduling results.

[0081] In one possible implementation, adjusting the default schedule based on resource inventory and resource consumption to obtain a first schedule includes: traversing the resource consumption of the multiple orders according to the default schedule, accumulating the resource consumption of each order and determining the remaining resource inventory; if the remaining resource inventory is insufficient, obtaining a new remaining resource inventory in response to a received inventory replenishment, and continuing the traversal until the resource consumption of all orders has been traversed; if the remaining resource inventory is insufficient, filtering out the current order in response to a received skip order operation, and continuing the traversal until the resource consumption of all orders has been traversed; if only an inventory replenishment is received during the traversal, using the default schedule as the first schedule; if at least one skip order operation is received during the traversal, removing the filtered-out orders from the default schedule to obtain the first schedule.

[0082] In this embodiment of the disclosure, the resource consumption of each order can be obtained sequentially according to the default schedule. After obtaining the resource consumption of each order, the remaining resource inventory is obtained by subtracting the resource consumption of that order from the remaining resource inventory. If the remaining resource inventory contains at least one resource with a balance less than zero, the user can choose an action. Optional actions include: replenishing the inventory, and skipping orders that result in insufficient remaining resource inventory.

[0083] If the user chooses to replenish inventory, the remaining inventory level is no longer less than zero, and the process can continue iterating through the remaining orders until completion. If the user chooses to skip orders that would cause insufficient inventory, the current order is directly removed, and the process continues iterating through the remaining orders until completion. If insufficient inventory occurs again, the process continues to wait for the user to make the aforementioned choice to complete the iteration.

[0084] When users choose to replenish inventory, the default schedule remains unchanged, so the default schedule can be used as the first schedule. When users choose to skip orders that result in insufficient resource inventory, some orders have already been filtered out. Therefore, these filtered-out orders should be removed from the default schedule to obtain the first schedule.

[0085] In this embodiment of the disclosure, the resource consumption of each order is iterated based on the remaining inventory, ensuring that the resource inventory meets the resource requirements of the orders to be scheduled. Updating the default schedule to obtain the first schedule makes subsequent scheduling processes more effective, reduces the probability of invalid scheduling for orders that cannot participate in the scheduling process, and improves the accuracy and availability of the scheduling results.

[0086] In one possible implementation, the training process of the order scheduling model includes: obtaining an initial model, wherein the initial model pre-sets multiple scheduling algorithms; using the initial model, determining a preferred algorithm based on a single training sample; using the preferred algorithm to schedule orders in the single training sample to obtain a first scheduling result; determining the similarity between the first scheduling result and the ground truth of the single training sample; determining a reward value based on the similarity; adjusting the probability values ​​corresponding to the multiple scheduling algorithms based on the reward value until the similarity is not less than a similarity threshold or all of the multiple scheduling algorithms are determined to be preferred algorithms at least once; using the initial model to process other training samples; until the model convergence condition is met to obtain the order scheduling model.

[0087] In one example, the preferred algorithm can be selected from a set of algorithms pre-defined in the initial model, following a pre-set selection order. In another example, for a single training sample, multiple algorithms pre-defined in the initial model can be scheduled. Specifically, during the first scheduling, an algorithm can be randomly selected. In subsequent schedulings of the same training sample, algorithms already selected can be excluded, and algorithms can be randomly selected from the remaining algorithms, with each selected algorithm being designated as the preferred algorithm.

[0088] Each time the optimization algorithm is used to schedule orders in the training samples, a first scheduling result can be obtained. The first scheduling result can be represented by a sequence matrix. In the sequence matrix, the rows represent orders and the columns represent the execution order. The element in the p-th row and q-th column of the sequence matrix indicates that the p-th order is scheduled to be executed in the q-th order.

[0089] A single training sample can include multiple sample orders and a ground truth value. The most suitable scheduling algorithm is determined manually or by software based on the order details, considering time, resource utilization, and customer demand dimensions. The scheduling result determined using this algorithm is then used as the ground truth value for the training sample. Here, the scheduling algorithm determined manually or by software can be one of several pre-set scheduling algorithms in the initial model. The ground truth value can have the same representation as the first scheduling result. The first scheduling result can be the result of updating the initial model based on the training samples.

[0090] Each time a first-order result is determined, it can be compared with the true value to determine the similarity. For example, the Euclidean distance or Manhattan distance between the first-order result and the true value can be determined. When both the first-order result and the true value are represented in matrix form, the rank of each can be determined, and the similarity between the two ranks can be determined.

[0091] A reward greater than zero is given when the similarity is not less than the similarity threshold. A reward less than zero is given when the similarity is less than the similarity threshold. The similarity threshold can be between 0.91 and 0.99. In another example, a mapping relationship between similarity value ranges and reward values ​​can be pre-defined. The range in which the similarity falls is determined, and the reward is determined based on the mapping relationship. Similarity and reward can be positively correlated.

[0092] The reward value is used to adjust the probability of each scheduling algorithm being used to schedule sample orders. The probability value can be positively correlated with the reward value. If all the preset scheduling algorithms are determined as the preferred algorithms at least once and participate in scheduling, or the similarity is not less than the similarity threshold (i.e., the scheduling algorithm used to determine the true value has been found), then training with the current training sample can be stopped. The next training sample is input into the initial model for training until the model convergence condition is met, and the trained model is used as the order scheduling model. The model convergence condition can be preset. For example, the model convergence condition can be that training is completed using c training samples, where c is a positive integer. Another example is that the model convergence condition can be that the probability of obtaining the same scheduling result as the true value using the first preferred algorithm is greater than the probability threshold.

[0093] In this embodiment, multiple scheduling algorithms pre-set in the initial model can be used to schedule sample orders. Each scheduling yields a first scheduling result. The similarity between the first scheduling result and the ground truth is used to guide the adjustment of the probability values ​​corresponding to each of the multiple scheduling algorithms, thereby increasing the probability of selecting an appropriate scheduling algorithm based on the resource consumption and time constraints of the orders to be scheduled. The training method of this disclosure allows the trained model to directly obtain the scheduling result based on the resource consumption, time constraints, and the first scheduling algorithm of the orders to be scheduled, or to update the first scheduling algorithm to obtain the scheduling result, thus improving order scheduling efficiency.

[0094] In one possible implementation, the method further includes: determining the sample resource consumption of each sample task based on the sample personnel time, sample material time, and sample equipment time occupied by each sample task in multiple sample orders, as well as the personnel consumption per unit time, material consumption per unit time, and equipment consumption per unit time of each sample task; determining the sample execution time of each sample task based on the sample personnel time, sample material time, and sample equipment time; using the sample time difference between the start time of the sample task and the start time of the previous sample task as the sample lag time of the sample task, and using the sample execution time and the sample lag time as the sample time constraint of the sample task; using the order acceptance order of each sample task as the sample schedule, and using the sample resource consumption, sample time constraint, and sample schedule corresponding to the multiple sample orders as a single training sample.

[0095] A single training sample can include the sample resource consumption, sample time constraints, and sample scheduling of multiple sample orders.

[0096] In this embodiment of the disclosure, for a single training sample, the duration of resource consumption for each sample task can be obtained separately. Then, the resource consumption per unit time for each sample task is obtained. As mentioned earlier, resources may include personnel, materials, and equipment. For ease of description, the duration of personnel consumption for a sample task is named "sample personnel duration," which can include the duration of each person required for a single sample task; the duration of materials consumption for a sample task is named "sample material duration," which can include the duration of various materials required for a single sample task; the duration of equipment consumption for a sample task is named "sample equipment duration," which can include the duration of each piece of equipment required for a single sample task. Additionally, the amount of personnel consumed by a sample task per unit time can be named "personnel consumption per unit time," which can include the unit consumption of each person required for the sample task; the amount of materials consumed by a sample task per unit time is named "material consumption per unit time," which can include the unit consumption of each material required for the sample task; and the amount of equipment consumed by a sample task per unit time is named "equipment consumption per unit time," which can include the unit consumption of each piece of equipment required for the sample task.

[0097] In this embodiment, the duration of a sample task's use of a single resource (sample personnel time, sample material time, sample equipment time) can be multiplied by the sample task's consumption of that single resource per unit time (personnel consumption per unit time, material consumption per unit time, equipment consumption per unit time) to determine the sample task's consumption of that single resource. Here, a single resource can be a single person, a single piece of equipment, or a single type of material. In this way, the sample resource consumption of each sample task in each sample order can be obtained.

[0098] In this embodiment of the disclosure, a first maximum value of the sample personnel duration and the sample equipment duration corresponding to the sample task can be determined. The sum of the first maximum value and the sample material duration is taken as the sample execution duration of the sample task. Furthermore, the difference between the start time of the sample task and the start time of the previous sample task is taken as the sample lag duration of the sample task. In addition, the sample execution duration of the same sample task and the duration after the sample are taken as the sample time constraint of the sample task.

[0099] In this embodiment, the sample schedule for sample tasks can be manually or randomly specified. This allows the resource consumption, time constraints, and schedule of each sample task within each sample order to be used as a single training sample. Furthermore, the optimal scheduling result across the time, resource consumption, and customer demand dimensions is taken as the ground truth of the training sample.

[0100] Furthermore, if a new scheduling algorithm needs to be added to the order scheduling model, the method of constructing training samples in this disclosure can be used to train the order scheduling model and obtain an updated order scheduling model.

[0101] Using the methods in this disclosure, suitable training samples can be constructed for training or updating order scheduling models, thereby improving training efficiency and effectiveness.

[0102] In one possible implementation, the method further includes: reading multiple initial orders; detecting the initial orders and removing empty orders to obtain the multiple orders to be scheduled.

[0103] In this embodiment, it is possible to detect whether the initial order is an empty order. If it is not an empty order, the initial order is identified as an order to be scheduled, which can reduce the probability of invalid scheduling. Furthermore, when the shortest duration algorithm is used as the target algorithm, empty orders will be displayed as the first order to be executed in the scheduling results, which disrupts the scheduling results and reduces their accuracy. Therefore, the detection and removal of empty orders in this embodiment can improve the accuracy of the scheduling results.

[0104] In one possible implementation, the method further includes: sorting the first orders executed in parallel in the scheduling result according to the order of receipt, and updating the scheduling result to obtain the updated scheduling result.

[0105] In this embodiment, if the scheduling result contains at least two first orders with the same execution order (same start time and same end time) (i.e., first orders executed in parallel), the first orders can be reordered according to the order of receipt to update the scheduling result. In this way, when personnel and equipment cannot support the parallel execution of multiple orders throughout the entire process, the updated scheduling result can resolve production conflicts and meet user requirements for order deadlines, improving the alignment between the scheduling result and the actual production and order situation.

[0106] Figure 2 This is a flowchart illustrating another packaging and printing order scheduling method provided in an embodiment of this disclosure.

[0107] In this embodiment of the disclosure, empty orders can be filtered out from multiple initial orders to obtain multiple orders to be scheduled. Then, the execution time and lag time of each task in these multiple orders are determined, that is, the time constraints of each task are determined. Furthermore, the resource consumption of each task is determined.

[0108] The system can iterate through the resource consumption of multiple orders according to the default schedule, accumulate the resource consumption of each order, and determine the remaining resource inventory. If the remaining resource inventory is insufficient, the system determines whether the user should replenish inventory. If no inventory is replenished (i.e., the user skipped an order at least once during the iteration), the skipped order is removed from the default schedule, resulting in the first schedule. If the user replenishes inventory every time during the iteration, the default schedule is used as the first schedule.

[0109] Furthermore, training samples can be created and used to train the initial model to obtain the order scheduling model. The methods for creating training samples and training the initial model have been introduced earlier and will not be repeated here. The time constraints, resource consumption, and first schedule of each task in the multiple orders to be scheduled are input into the order scheduling model to obtain the scheduling results.

[0110] If there are first orders with the same execution order in the scheduling results, the default scheduling order can be referenced, and the first orders with the same order can be sorted according to the default scheduling order to obtain the updated scheduling results.

[0111] Figure 3 A schematic diagram of a packaging and printing order scheduling system provided in an embodiment of this disclosure. The system 20 includes:

[0112] The time constraint and resource consumption determination unit 21 is used to obtain the time constraint of each task in the multiple orders to be scheduled and the resource consumption of each task.

[0113] The first scheduling determination unit 22 is used to adjust the default schedule based on the resource inventory and the resource consumption to obtain the first schedule.

[0114] The scheduling result determination unit 23 is used to determine the target scheduling algorithm based on the resource consumption, the first schedule and the time constraint using the order scheduling model, and to sort the multiple orders based on the target scheduling algorithm to obtain the scheduling result.

[0115] In one possible implementation, the time constraint and resource consumption determination unit 21 is further configured to:

[0116] Obtain the personnel time, material time, and equipment time consumed by each of the tasks;

[0117] For a single task, determine the maximum value between material processing time and equipment processing time;

[0118] The sum of the maximum value and the personnel time of the individual task is used as the execution time of the individual task, so as to obtain the execution time of each task.

[0119] Obtain the time difference between the start time of each task and the start time of its predecessor, and use the time difference as the lag time of each task.

[0120] In one possible implementation, the first scheduling determination unit 22 is further configured to:

[0121] According to the default schedule, iterate through the resource consumption of the multiple orders, accumulate the resource consumption of each order, and determine the remaining resource inventory.

[0122] If the resource inventory is insufficient, in response to the received inventory replenishment, a new resource inventory is obtained, and the traversal continues until the resource consumption of all orders is traversed.

[0123] If the remaining resource inventory is insufficient, in response to the received skip order operation, the current order is filtered out and the traversal continues until the resource consumption of all orders has been traversed.

[0124] In the traversal, if only the inventory replenishment quantity is received, the default schedule is used as the first schedule;

[0125] During the traversal, if at least one skip order operation is received, the orders that have been filtered out are removed from the default schedule to obtain the first schedule.

[0126] In one possible implementation, the training process of the order scheduling model includes:

[0127] Obtain an initial model, in which multiple scheduling algorithms are preset;

[0128] Using the initial model, an optimal algorithm is determined based on a single training sample;

[0129] The orders in the single training sample are scheduled using an optimization algorithm to obtain the first scheduling result;

[0130] Determine the similarity between the first scheduling result and the ground truth of the individual training sample;

[0131] The reward value is determined based on the similarity.

[0132] Based on the reward value, adjust the probability values ​​corresponding to the various scheduling algorithms until the similarity is not less than the similarity threshold or all of the various scheduling algorithms are determined to be the preferred algorithm at least once. Use the initial model to process other training samples until the model convergence condition is met to obtain the order scheduling model.

[0133] In one possible implementation, the training process of the order scheduling model further includes:

[0134] Based on the sample personnel time, sample material time, and sample equipment time occupied by each sample task in multiple sample orders, as well as the personnel consumption per unit time, material consumption per unit time, and equipment consumption per unit time of each sample task, the sample resource consumption of each sample task is determined.

[0135] Based on the time allotted for each sample person, the time allotted for each sample material, and the time allotted for each sample device, the sample execution time for each of the aforementioned sample tasks is determined.

[0136] The difference between the start time of the sample task and the start time of the previous sample task is taken as the sample lag time of the sample task, and the sample execution time and the sample lag time are taken as the sample time constraint of the sample task.

[0137] The order of each sample task is used as the sample schedule, and the sample resource consumption, sample time constraint, and sample schedule corresponding to the multiple sample orders are used as a single training sample.

[0138] In one possible implementation, the system 20 further includes:

[0139] The initial order reading unit is used to read multiple initial orders;

[0140] The pending order determination unit is used to detect the initial order and remove empty orders to obtain multiple pending orders.

[0141] In one possible implementation, the system 20 further includes:

[0142] The scheduling result update unit is used to sort the first orders that are executed in parallel in the scheduling result according to the order of receiving the orders, and update the scheduling result to obtain the updated scheduling result.

[0143] In some embodiments, the system provided in this disclosure may have functions or include modules that can be used to execute the methods described in the above method embodiments. The specific implementation of these methods can be referred to the description in the above method embodiments, and for the sake of brevity, they will not be repeated here.

[0144] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium can be volatile or non-volatile.

[0145] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0146] This disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0147] Figure 4 This is a schematic diagram of the structure of an electronic device for scheduling packaging and printing orders, provided as an embodiment of the present disclosure. For example, the electronic device 1900 can be provided as a server or terminal device. (Refer to...) Figure 4 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0148] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). Electronic device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM Mac OS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0149] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0150] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0151] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0152] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0153] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0154] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0155] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0156] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0157] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0158] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for scheduling packaging and printing orders, characterized in that, include: Obtain the time constraints of each task in multiple orders to be scheduled, and the resource consumption of each task, wherein the resources include: personnel, equipment, and materials; Based on resource inventory and resource consumption, the default schedule is adjusted to obtain the first schedule. Based on the resource consumption, the first schedule, and the time constraints, a target scheduling algorithm is determined using an order scheduling model, and the multiple orders are sorted based on the target scheduling algorithm to obtain the scheduling result. The process of obtaining the time constraints for each task in the multiple orders to be scheduled includes: Obtain the personnel time, material time, and equipment time consumed by each of the tasks; For a single task, determine the maximum value between material processing time and equipment processing time; The sum of the maximum value and the personnel time of the individual task is used as the execution time of the individual task, so as to obtain the execution time of each task. Obtain the time difference between the start time of each task and the start time of its predecessor, and use the time difference as the lag time of each task. The step of adjusting the default schedule based on resource inventory and resource consumption to obtain the first schedule includes: According to the default schedule, iterate through the resource consumption of the multiple orders, accumulate the resource consumption of each order, and determine the remaining resource inventory. If the resource inventory is insufficient, in response to the received inventory replenishment, a new resource inventory is obtained, and the traversal continues until the resource consumption of all orders is traversed. If the remaining resource inventory is insufficient, in response to the received skip order operation, the current order is filtered out and the traversal continues until the resource consumption of all orders has been traversed. In the traversal, if only the inventory replenishment quantity is received, the default schedule is used as the first schedule; During the traversal, if at least one skip order operation is received, the orders that have been filtered out are removed from the default schedule to obtain the first schedule.

2. The method according to claim 1, characterized in that, The training process of the order scheduling model includes: Obtain an initial model, in which multiple scheduling algorithms are preset; Using the initial model, an optimal algorithm is determined based on a single training sample; The orders in the single training sample are scheduled using an optimization algorithm to obtain the first scheduling result; Determine the similarity between the first scheduling result and the ground truth of the individual training sample; The reward value is determined based on the similarity. Based on the reward value, adjust the probability values ​​corresponding to the various scheduling algorithms until the similarity is not less than the similarity threshold or all of the various scheduling algorithms are determined to be the preferred algorithm at least once. Use the initial model to process other training samples until the model convergence condition is met to obtain the order scheduling model.

3. The method according to claim 2, characterized in that, Also includes: Based on the sample personnel time, sample material time, and sample equipment time occupied by each sample task in multiple sample orders, as well as the personnel consumption per unit time, material consumption per unit time, and equipment consumption per unit time of each sample task, the sample resource consumption of each sample task is determined. Based on the time allotted for each sample person, the time allotted for each sample material, and the time allotted for each sample device, the sample execution time for each of the aforementioned sample tasks is determined. The difference between the start time of the sample task and the start time of the previous sample task is taken as the sample lag time of the sample task, and the sample execution time and the sample lag time are taken as the sample time constraint of the sample task. The order of each sample task is used as the sample schedule, and the sample resource consumption, sample time constraint, and sample schedule corresponding to the multiple sample orders are used as a single training sample.

4. The method according to claim 1, characterized in that, The method further includes: Read multiple initial orders; The initial order is checked, and empty orders are removed to obtain multiple orders to be scheduled.

5. The method according to claim 1, characterized in that, The method further includes: The first order executed in parallel in the scheduling result is sorted according to the order of receipt, and the scheduling result is updated to obtain the updated scheduling result.

6. A packaging and printing order scheduling system, characterized in that, include: A time constraint and resource consumption determination unit is used to obtain the time constraints of each task in multiple orders to be scheduled and the resource consumption of each task, wherein the resources include: personnel, equipment and materials; The first scheduling determination unit is used to adjust the default schedule based on the resource inventory and the resource consumption to obtain the first schedule. The scheduling result determination unit is used to determine a target scheduling algorithm based on the resource consumption, the first schedule, and the time constraint using an order scheduling model, and to sort the multiple orders based on the target scheduling algorithm to obtain a scheduling result; The time constraint and resource consumption determination unit is further used for: Obtain the personnel time, material time, and equipment time consumed by each of the tasks; For a single task, determine the maximum value between material processing time and equipment processing time; The sum of the maximum value and the personnel time of the individual task is used as the execution time of the individual task, so as to obtain the execution time of each task. Obtain the time difference between the start time of each task and the start time of its predecessor, and use the time difference as the lag time of each task. The first scheduling determination unit is further configured to: According to the default schedule, iterate through the resource consumption of the multiple orders, accumulate the resource consumption of each order, and determine the remaining resource inventory. If the resource inventory is insufficient, in response to the received inventory replenishment, a new resource inventory is obtained, and the traversal continues until the resource consumption of all orders is traversed. If the remaining resource inventory is insufficient, in response to the received skip order operation, the current order is filtered out and the traversal continues until the resource consumption of all orders has been traversed. In the traversal, if only the inventory replenishment quantity is received, the default schedule is used as the first schedule; During the traversal, if at least one skip order operation is received, the orders that have been filtered out are removed from the default schedule to obtain the first schedule.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 5 when executing instructions stored in the memory.

8. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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