An order processing method, system, electronic device and storage medium

Abstract

The application provides an order processing method and system, an electronic device and a storage medium. The method comprises the following steps: determining at least one to-be-processed order and an optimization target corresponding to the to-be-processed order; inputting information of the to-be-processed order at each production node into a decision optimization model matched with the optimization target, and obtaining an optimization strategy corresponding to each to-be-processed order, wherein the optimization target is used for optimizing a processing flow of the to-be-processed order from at least one dimension, and the processing flow comprises a plurality of production nodes; and processing the to-be-processed order according to the optimization strategy corresponding to each to-be-processed order. In the application, the processing flow of the order is guided by the obtained optimization strategy, which can make each order complete processing under the condition of meeting various expected targets, thereby enhancing the flexibility of order processing, and on the other hand, each link of the production process can be better scheduled through the optimization strategy, so that the optimization strategy can be used as technical support for collaborative production, thereby promoting efficient production scheduling of enterprises.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an order processing method, system, electronic device, and storage medium. Background Technology

[0002] Collaborative manufacturing is a new model emerging from the context of next-generation information technology. In this model, multiple manufacturers and supply chains can connect via the internet (digital supply chain) to collaboratively complete production tasks. Collaborative production and transportation within the enterprise supply chain can improve overall supply chain performance, increase customer satisfaction, and thus enhance the production efficiency of the entire manufacturing system.

[0003] However, collaborative production processes face more complex situations compared to traditional workshop production processes. For example, in warp knitting enterprises, the production process is characterized by numerous and complex procedures and order splitting. Therefore, collaborative production requires more efficient scheduling decision-making technology, which is currently lacking in warp knitting enterprises. Thus, how to provide an efficient scheduling decision-making technology has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides an order processing method, system, electronic device, and storage medium. The method can obtain an optimization strategy for each order, which can be used to guide the order processing flow, thereby better scheduling each stage of the production process.

[0005] The first aspect of this application provides an order processing method, including:

[0006] Identify at least one pending order and the optimization objective corresponding to the pending order;

[0007] The information of the pending orders at each production node is input into a decision optimization model that matches the optimization objective to obtain the optimization strategy corresponding to each pending order. The optimization objective is used to optimize the processing flow of the pending orders from at least one dimension. The processing flow includes multiple production nodes.

[0008] The pending orders are processed according to the optimization strategies corresponding to each of the pending orders.

[0009] Optionally, the method further includes:

[0010] Based on at least one set optimization objective, obtain functions for each of the optimization objectives;

[0011] The functions of each of the aforementioned optimization objectives are combined into a joint objective function;

[0012] The decision optimization model is constructed based on the joint objective function, the constraints of each function, and the constraints of the joint objective function.

[0013] Optionally, the joint objective function is:

[0014] F(x)=∑ i∈I wi*fi(x)

[0015] Where I is the set of optimization objective types, indexed by i, and wi is the weight corresponding to the function of the i-th type of optimization objective, ∑ i∈I wi = 1.

[0016] Optionally, the information of the orders to be processed at each production node is input into a decision optimization model that matches the optimization objective to obtain the optimization strategy corresponding to each order to be processed, including:

[0017] Find the minimum value of the decision optimization model when the information of the order to be processed at each production node is taken as input, and obtain the optimization strategy corresponding to each order to be processed according to the set and parameters involved in the decision optimization model.

[0018] Optionally, the information of the orders to be processed at each production node is input into a decision optimization model that matches the optimization objective to obtain the optimization strategy corresponding to each order to be processed, including:

[0019] Obtain information input by the user;

[0020] The information of the order to be processed at each production node is determined from the input information;

[0021] The information of the orders to be processed at each production node is input into a decision optimization model that matches the optimization objective to obtain the optimization strategy corresponding to each order to be processed.

[0022] Optionally, the optimization objective includes at least one of the following: lowest transportation cost, shortest delivery time, and lowest unreliability penalty cost.

[0023] Optionally, the information of the pending orders at each production node includes at least: order information of the pending orders, information of the factories used to process the pending orders, transportation time between different factories, transportation cost between different factories, and information of the equipment used to process the pending orders.

[0024] A second aspect of this application provides an order processing system, comprising:

[0025] A determination module is used to determine at least one order to be processed and an optimization objective corresponding to the order to be processed;

[0026] The acquisition module is used to input the information of the pending orders at each production node into a decision optimization model that matches the optimization objective, and to obtain the optimization strategy corresponding to each pending order. The optimization objective is used to optimize the processing flow of the pending orders from at least one dimension. The processing flow includes multiple production nodes.

[0027] The processing module is used to process the orders to be processed according to the optimization strategies corresponding to each order.

[0028] Optionally, the order processing system includes:

[0029] The first obtaining submodule is used to obtain functions of each of the optimization objectives based on at least one set optimization objective;

[0030] The combination submodule is used to combine the functions of the various optimization objectives into a joint objective function;

[0031] A submodule is constructed to build the decision optimization model based on the joint objective function, the constraints of each function, and the constraints of the joint objective function.

[0032] Optionally, the joint objective function is:

[0033] F(x)=∑ i∈I wi*fi(x)

[0034] Where I is the set of optimization objective types, indexed by i, and wi is the weight corresponding to the function of the i-th type of optimization objective, ∑ i∈I wi = 1.

[0035] Optionally, the obtaining module includes:

[0036] The solution submodule is used to solve the minimum value of the decision optimization model when the information of the order to be processed at each production node is taken as input, and to obtain the optimization strategy corresponding to each order to be processed according to the set and parameters involved in the decision optimization model.

[0037] Optionally, the obtaining module includes:

[0038] The second acquisition submodule is used to obtain information input by the user;

[0039] The determination submodule is used to determine the information of the order to be processed at each production node from the input information;

[0040] The third acquisition submodule is used to input the information of the orders to be processed at each production node into a decision optimization model that matches the optimization objective, so as to obtain the optimization strategy corresponding to each order to be processed.

[0041] Optionally, the optimization objective includes at least one of the following: lowest transportation cost, shortest delivery time, and lowest unreliability penalty cost.

[0042] Optionally, the information of the pending orders at each production node includes at least: order information of the pending orders, information of the factories used to process the pending orders, transportation time between different factories, transportation cost between different factories, and information of the equipment used to process the pending orders.

[0043] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executed, implements the steps of the order processing method described in the first aspect of this application.

[0044] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the order processing method as described in the first aspect of this application.

[0045] The order processing method of this embodiment first determines at least one order to be processed and its corresponding optimization objective. Then, the order to be processed is input into a decision optimization model that matches the information of each production node with the optimization objective to obtain the optimization strategy for each order to be processed. This optimization objective is used to optimize the processing flow (including multiple production nodes) of the order to be processed from at least one dimension. Finally, the order to be processed is processed according to the optimization strategy corresponding to each order to be processed. This application obtains an optimization strategy for each order and uses the optimization strategy to guide the order processing flow. On the one hand, this allows each order to be processed while meeting various expected objectives, enhancing the flexibility of order processing. On the other hand, the optimization strategy can better schedule various links in the production process. Therefore, the optimization strategy can serve as technical support for collaborative production and promote efficient production scheduling for enterprises. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1This is a schematic diagram illustrating the implementation environment of one embodiment of this application;

[0048] Figure 2 This is a flowchart illustrating an order processing method according to an embodiment of this application;

[0049] Figure 3 This is a schematic diagram illustrating multiple sets according to an embodiment of this application;

[0050] Figure 4 This is a schematic diagram illustrating multiple parameters according to an embodiment of this application;

[0051] Figure 5 This is a schematic diagram illustrating multiple decision variables in one embodiment of this application;

[0052] Figure 6 This is a schematic diagram illustrating an order splitting result according to an embodiment of this application;

[0053] Figure 7 This is a schematic diagram illustrating order information for an order to be processed, as shown in one embodiment of this application;

[0054] Figure 8 This is a schematic diagram illustrating an order processing result according to an embodiment of this application;

[0055] Figure 9 This is a table showing the patterns and their meanings in one embodiment of this application;

[0056] Figure 10 This is a structural block diagram of an order processing system shown in one embodiment of this application. Detailed Implementation

[0057] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] The technical solution of this application is applicable to any production process and can significantly optimize the order processing flow. Since the effect of optimizing the order processing flow is particularly significant when applied to the warp knitting field, the following text uses the production process of the warp knitting field as an example to explain the order processing method of this application in detail. The implementation principle when applied to other production processes is the same as that in the warp knitting field, and will not be repeated here.

[0059] Figure 1 This is a schematic diagram illustrating the implementation environment of one embodiment of this application. Figure 1In China, the internal supply chain of warp knitting enterprises mainly consists of four levels: raw material suppliers, weaving mills (including weaving mills 1-3), dyeing and finishing mills (including dyeing and finishing mills 1-3), and customer orders (including customer orders 1-6). The raw material suppliers provide the yarn, the weaving mills use it to produce fabric through warping and knitting processes, and the dyeing and finishing mills dye the fabric to obtain the final product. The raw material suppliers, weaving mills, and dyeing and finishing mills work together to obtain the finished product and deliver it to the customer, thus completing the customer order processing.

[0060] The order processing method of this application is applied to the order processing system of warp knitting enterprises. The order processing method can significantly optimize the order processing process of raw material plants, weaving plants and dyeing plants. Figure 2 This is a flowchart illustrating an order processing method according to an embodiment of this application. (Refer to...) Figure 2 The order processing method of this application may include the following steps:

[0061] Step S21: Determine at least one order to be processed and the optimization objective corresponding to the order to be processed.

[0062] In this embodiment, users can place orders through the client. For example, a user may place an order for 1,000 tons of Class A fabrics through the client.

[0063] The order processing system is used to collect all orders initiated by clients and treat these orders as at least one pending order.

[0064] The order processing system can also split certain orders to obtain multiple sub-orders, and then treat the split orders as at least one pending order. For example, the order processing system collects orders 1-3, then splits order 3 into sub-orders 1-3, and then treats order 1, order 2, sub-order 1, sub-order 2, and sub-order 3 as at least one pending order.

[0065] For example, when splitting the order "order 1,000 tons of Class A fabrics", it can be split into two sub-orders: "400 tons to be processed by Factory A" and "600 tons to be processed by Factory B". This embodiment does not impose specific restrictions on the way orders are split.

[0066] In this embodiment, the optimization objective refers to the goal that needs to be achieved when completing order processing, i.e., the expected goal.

[0067] In one embodiment, the optimization objectives of this application may include the following types: lowest transportation cost (costs incurred when transporting raw materials between various factories), shortest delivery time (time required from placing an order to delivering the finished product to the user), and lowest unreliability penalty cost (additional costs incurred when a product cannot be completed on schedule due to equipment failure). Of course, optimization objectives may also have other types, and this embodiment does not impose specific limitations on them. When performing step S21, either one optimization objective or multiple optimization objectives can be determined, and this embodiment does not impose specific limitations on them. If one optimization objective is determined, it means that the order to be processed needs to meet this objective upon completion; if multiple optimization objectives are determined, it means that the order to be processed needs to simultaneously meet multiple objectives upon completion.

[0068] Step S22: Input the information of the pending orders at each production node into a decision optimization model that matches the optimization objective to obtain the optimization strategy corresponding to each pending order. The optimization objective is used to optimize the processing flow of the pending orders from at least one dimension. The processing flow includes multiple production nodes.

[0069] In this embodiment, the order production process includes multiple production nodes, which include a raw material plant, a weaving plant, a dyeing and finishing plant, and a delivery location. The weaving plant further includes multiple sub-nodes, with each weaving machine constituting one sub-node. Similarly, the dyeing and finishing plant includes multiple sub-nodes, with each dyeing and finishing machine constituting one sub-node. The information for each production node refers to information related to each node or sub-node, as well as the correlation information between nodes (e.g., transportation time and costs between two factories).

[0070] In this application, the information of the production nodes in the decision optimization model is known information (e.g., the number of weaving mills, the number of dyeing and printing mills, etc.). The information of the production nodes that are unknown beforehand is obtained by solving the decision optimization model (e.g., which weaving machine or dyeing and printing machine should handle a certain order). Finally, the optimization strategy for each order to be processed is obtained based on the information of the unknown production nodes.

[0071] In one embodiment, the information of the pending orders at each production node includes at least: order information of the pending orders, information of the factories used to process the pending orders, transportation time between different factories, transportation cost between different factories, and information of the equipment used to process the pending orders.

[0072] The order information for orders to be processed may include the order quantity, order time, product type, and quantity of products ordered. The order information can be selected according to the user's actual needs, and this embodiment does not impose specific restrictions on it.

[0073] Information about factories used to process pending orders can include: raw material plant information (type of raw materials, inventory of each type of raw material, etc.), weaving plant information (name and location of the weaving plant, type and quantity of looms in each weaving plant, reliability probability of each weaving plant, etc.), and dyeing plant information (name and location of the dyeing plant, type and quantity of dyeing machines in each dyeing plant, reliability probability of each dyeing plant, etc.).

[0074] Transportation time between different factories can include: transportation time from the raw material plant to the weaving plant, transportation time from the weaving plant to the dyeing and printing plant, and transportation time from the dyeing and printing plant to the delivery location, etc.

[0075] Transportation costs between different factories can include: transportation costs from the raw material plant to the weaving plant, transportation costs from the weaving plant to the dyeing plant, and transportation costs from the dyeing plant to the delivery location, etc.

[0076] Information about the equipment used to process pending orders may include: the weaving rate of each type of loom, the reliability probability of each type of loom, the dyeing rate of each type of dyeing machine, and the reliability probability of each type of dyeing machine.

[0077] Of course, the information of orders to be processed at each production node can be of other types, and this embodiment does not impose specific restrictions on this.

[0078] In this embodiment, the decision optimization model can be pre-built (the construction process will be described later). Regardless of whether one or multiple optimization objectives are determined in step S21, the order processing system in this embodiment has a corresponding decision optimization model.

[0079] In one implementation, inputting the information of the orders to be processed at each production node into a decision optimization model that matches the optimization objective to obtain the optimization strategy corresponding to each order to be processed may include:

[0080] Find the minimum value of the decision optimization model when the information of the order to be processed at each production node is taken as input, and obtain the optimization strategy corresponding to each order to be processed according to the set and parameters involved in the decision optimization model.

[0081] In this embodiment, after inputting the known information of each order to be processed at each production node (the known information of each production node is represented by sets and parameters in the decision optimization model) into the decision optimization model, the model is solved to obtain the minimum value. At this point, the information of each production node that was previously unknown can be determined. Then, based on this determined information of each previously unknown production node (the information of each previously unknown production node is represented by decision variables in the decision optimization model), the optimization strategy corresponding to each order to be processed can be further derived. In other words, the decision optimization model can obtain the previously unknown decision variables based on the input known sets and parameters.

[0082] In the process of solving the decision optimization model, we are actually simulating the processing flow of each order to be processed. After finding the minimum value, it means that the current simulated processing flow is the best, that is, the overall benefit is the best. Therefore, the processing flow of each order to be processed at this time can be used as the optimization strategy corresponding to each order to be processed.

[0083] In this embodiment, the optimization objective is used to optimize the processing flow of the order to be processed from at least one dimension. Specifically, one optimization objective can optimize the processing flow of the order to be processed from one dimension, and multiple optimization objectives can comprehensively optimize the processing flow of the order to be processed from multiple dimensions. For example, when the optimization objective is to minimize transportation costs, the processing flow of the order to be processed can be optimized from the dimension of transportation costs. As another example, when the optimization objectives are to minimize delivery time and minimize unreliability penalty costs, the processing flow of the order to be processed can be comprehensively optimized from the dimensions of delivery time and unreliability penalty costs.

[0084] Step S23: Process the pending orders according to the optimization strategy corresponding to each pending order.

[0085] In this embodiment, the optimization strategy can be understood as the optimized processing flow of each order (the processing flow can be represented by the production nodes mentioned above), including the time period for transporting raw yarn to the weaving mill, the time period for which the weaving machine is responsible for processing, the time period for transporting the fabric output from the weaving machine to the dyeing and finishing plant, the time period for which the dyeing and finishing machine is responsible for dyeing and finishing, the processing time of each weaving machine, and the processing time of each dyeing and finishing machine. This embodiment does not impose specific restrictions on the order processing flow.

[0086] Each order has a corresponding optimization strategy. After obtaining the corresponding optimization strategy, each order can be processed according to the processing flow in the optimization strategy.

[0087] In this embodiment, the order processing system can control each link of the production process according to the optimization strategy, control the operation of each piece of equipment in the factory to achieve the purpose of collaborative production, and can better complete the order processing. Therefore, the optimization strategy can serve as technical support for collaborative production and promote the efficient scheduling of enterprises.

[0088] The order processing method of this embodiment first determines at least one order to be processed and its corresponding optimization objective. Then, the order to be processed is input into a decision optimization model that matches the information of each production node with the optimization objective to obtain the optimization strategy for each order to be processed. This optimization objective is used to optimize the processing flow (including multiple production nodes) of the order to be processed from at least one dimension. Finally, the order to be processed is processed according to the optimization strategy corresponding to each order to be processed. This application obtains an optimization strategy for each order and uses the optimization strategy to guide the order processing flow. On the one hand, this allows each order to be processed while meeting various expected objectives, enhancing the flexibility of order processing. On the other hand, the optimization strategy can better schedule various links in the production process. Therefore, the optimization strategy can serve as technical support for collaborative production and promote efficient production scheduling for enterprises.

[0089] In conjunction with the above embodiments, in one implementation, this application also provides a method for constructing a decision optimization model, the method specifically including:

[0090] Based on at least one set optimization objective, obtain functions for each of the optimization objectives;

[0091] The functions of each of the aforementioned optimization objectives are combined into a joint objective function;

[0092] The decision optimization model is constructed based on the joint objective function, the constraints of each function, and the constraints of the joint objective function.

[0093] In this embodiment, each optimization objective has a corresponding functional representation. After determining at least one optimization objective, the functions of each optimization objective can be combined into a joint objective function. Since decision optimization models are usually subject to resource constraints, this application also needs to set constraints for the joint objective function. These constraints include constraints that the joint objective function must satisfy and constraints that individual functions must satisfy. Constraints are used to limit the range of values ​​for parameters or decision variables, such as each order being processed at most once per weaving mill, or each weaving mill processing at most one order at the same time.

[0094] In this embodiment, by processing the information of the production nodes of the orders to be processed through a pre-built decision optimization model, the efficiency of obtaining the optimization strategies corresponding to each order to be processed can be improved.

[0095] In one implementation, based on the above embodiments, the joint objective function is:

[0096] F(x)=∑ i∈I wi*fi(x)

[0097] Where I is the set of optimization objective types, indexed by i, and wi is the weight corresponding to the function of the i-th type of optimization objective, ∑ i∈I wi = 1.

[0098] In this embodiment, wi is a manually set weight that can be determined based on the importance of each optimization objective. Generally, when the importance of an optimization objective is high, the weight of the function corresponding to that optimization objective can be set higher; when the importance of an optimization objective is low, the weight of the function corresponding to that optimization objective can be set lower.

[0099] For example, if there are four types of optimization objectives, the joint objective function can be: F(x) = w1*f1(x) + w2*f2(x) + w3*f3(x) + w4*f4(x), where w1 + w2 + w3 + w4 = 1. Here, f1(x) is the function of the first type of optimization objective, f2(x) is the function of the second type of optimization objective, f3(x) is the function of the third type of optimization objective, and f4(x) is the function of the fourth type of optimization objective. w1 is the weight of the function corresponding to the first type of optimization objective, w2 is the weight of the function corresponding to the second type of optimization objective, w3 is the weight of the function corresponding to the third type of optimization objective, and w4 is the weight of the function corresponding to the fourth type of optimization objective.

[0100] In practice, the joint objective function can be adjusted in real time according to the determined optimization objectives. For example, when the optimization objective is to minimize delivery time, the joint objective function can be F(X) = f1(x). As another example, when the optimization objectives are to minimize both delivery time and transportation cost, the objective function can be F(X) = w1'*f1(x) + w2'*f2(x), where f1(x) is a function of the corresponding delivery time, f2(x) is a function of the corresponding transportation cost, and w1' + w2' = 1.

[0101] After obtaining the joint objective function, the known information of the orders to be processed at each production node can be used as the input value of the joint objective function F(x). Then, under the condition of satisfying the pre-set constraints, the minimum value of F(x) is obtained. When the minimum value is obtained, the processing flow of each order to be processed is used as the optimization strategy corresponding to each order to be processed.

[0102] This embodiment provides an optimization strategy for each order. Using this optimization strategy to guide the order processing flow enables each order to be processed while meeting various expected goals, thus enhancing the flexibility of order processing.

[0103] In one implementation, combining the above embodiments, the information of the orders to be processed at each production node is input into a decision optimization model that matches the optimization objective to obtain the optimization strategy corresponding to each order to be processed, including:

[0104] Obtain information input by the user;

[0105] The information of the order to be processed at each production node is determined from the input information;

[0106] The information of the orders to be processed at each production node is input into a decision optimization model that matches the optimization objective to obtain the optimization strategy corresponding to each order to be processed.

[0107] After identifying at least one pending order, the user can select known information about that order at each production node from all types of order-related information. For example, the user can select the number of pending orders, the order placement time of each order, the type and number of weaving machines, the type and number of dyeing and printing machines, the processing speed of different types of weaving machines, and the processing speed of different types of dyeing and printing machines as known information for each pending order at each production node. This order information is then input into the order processing system, allowing the system to obtain optimization strategies for each pending order based on the input known information at each production node.

[0108] In this embodiment, the information of the order to be processed at each production node can be manually entered by the user, which improves the flexibility of order processing.

[0109] The following detailed description of the joint objective function and corresponding constraints used in the order processing method of this application, using a specific embodiment, illustrates these principles. In this embodiment, the applicable optimization objectives include: minimizing transportation costs, minimizing delivery time, and minimizing unreliability penalty costs. The meanings of the various sets involved in the joint objective function and corresponding constraints are as follows: Figure 3 As shown, the meanings of the various parameters involved are as follows: Figure 4 As shown, the meanings of the various decision variables involved are as follows: Figure 5 As shown. Figure 3 This is a schematic diagram illustrating multiple sets according to an embodiment of this application. Figure 4 This is a schematic diagram illustrating multiple parameters in one embodiment of this application. Figure 5 This is a schematic diagram illustrating multiple decision variables in one embodiment of this application.

[0110] exist Figure 3 In this diagram, J = {1, 2, ..., |J|} represents the set of orders to be processed, indexed by j. O = {1} represents the set of raw material factories, indexed by o. A = {1, 2, ..., |A|} represents the set of weaving factories, indexed by a. B = {1, 2, ..., |B|} represents the set of dyeing and printing factories, indexed by b. K = {1, 2, ..., |K|} represents the set of processing locations of orders within factories, indexed by k.

[0111] exist Figure 4 In the middle, d j This indicates the quantity of products that need to be processed for order j. j This indicates the release time of order j (i.e., the time when order j was placed). This indicates the rate at which a weaving factory completes processing a unit of fabric. This indicates the rate at which a printing and dyeing factory completes processing a unit of fabric. This indicates the transportation time from raw material plant o to weaving plant a. This represents the unit transportation cost from raw material plant o to weaving plant a. This indicates the transportation time from weaving factory A to dyeing and printing factory B. This represents the unit transportation cost from weaving factory A to dyeing and printing factory B. This indicates the shipping time from dyeing and printing factory b to the user corresponding to order j. This represents the unit transportation cost from dyeing and printing factory b to the user corresponding to order j. Let m represent the reliability penalty cost of order j. m is a very large number. This represents the reliability probability of fabrication factory a. s represents the reliability probability of fabric mill b. j This indicates the minimum level of reliability (reliability probability) that needs to be met.

[0112] exist Figure 5 middle, This indicates whether order j is being woven at position k in weaving mill a. This indicates the amount of fabric woven by order j at weaving mill a. This indicates whether order j is being printed at position k in dyeing and printing factory b. This indicates the amount of dyeing done by order j at dyeing factory b. This indicates whether order j will be shipped between weaving factory a and dyeing factory b. This represents the volume of goods transported for order j between weaving mill a and dyeing mill b. (T) max This represents the longest processing time for all orders, which is also the product delivery time. C represents the total transportation cost. R j This represents the reliability level (probability) of order j. These are auxiliary variables used to calculate time.

[0113] In this embodiment, the joint objective function corresponds to the following formula (1):

[0114]

[0115] In formula (1), f1(x) = T max Let f2(x) = C, which is a function of the delivery time, and let C be a function of the transportation cost. This is a function that corresponds to the unreliability penalty cost. w1, w2, and w3 can be set arbitrarily; for example, w1 = w2 = 0.3 and w3 = 0.4 can be set.

[0116] In the joint objective function described above, when w1 = 1 and w2 = w3 = 0, the decision optimization model can optimize the order processing from the dimension of delivery time. When w1 = w2 = 0.5 and w3 = 0, the decision optimization model can optimize the order processing from the dimensions of delivery time and transportation cost. By setting the value of w, the type of optimization objective can be controlled.

[0117] When using formula (1), users can input a portion of known production node information (represented by sets and parameters) into the order processing system as information of the order to be processed at each production node. The order processing system then processes this information using a decision optimization model. Specifically, when formula (1) reaches its minimum value under constraints, the optimization strategy for the order to be processed can be obtained based on the values ​​of other previously unknown decision variables in the current model.

[0118] The constraints of formula (1) include the following formulas (2)-(24).

[0119]

[0120] Constraint (2) means that each order can be processed at most once in each weaving factory.

[0121]

[0122] Constraint (3) means that each order can be processed at most once in each printing and dyeing plant.

[0123]

[0124] Constraint (4) means that each weaving mill can process at most one order at the same time.

[0125]

[0126] Constraint (5) means that each printing and dyeing factory can process at most one order at the same time.

[0127]

[0128] Constraint (6) means that if the earlier processing position is available in the same weaving mill, the later processing positions will not be used.

[0129]

[0130] Constraint (7) means that if the processing position at the front of the dyeing and printing plant is available, the processing position at the back will not be used.

[0131]

[0132] Constraint (8) means that if order j is not processed at the corresponding weaving factory, then the processing quantity of j at that factory is 0.

[0133]

[0134] Constraint (9) means that if order j is not processed in the corresponding dyeing and printing factory, then the processing quantity of j in that factory (the quantity of products corresponding to order j) is 0.

[0135]

[0136] Constraint (10) means that if order j is not transported between weaving factory a and dyeing factory b, the transport volume (product transport quantity) of j on this route is 0.

[0137]

[0138] Constraint (11) means that the total amount of fabric woven in order j is equal to the demand.

[0139]

[0140] Constraint (12) means that the amount of order j processed at weaving mill a is equal to the sum of the amounts shipped to each printing and dyeing mill b.

[0141]

[0142] Constraint (13) means that the amount of order j processed at dyeing and printing plant b is equal to the sum of the amounts transported from each weaving plant a.

[0143]

[0144] Constraint (14) means that the completion time of order j at weaving mill a is not less than the sum of its release time, the transportation time from the raw material plant to weaving mill a, and its processing time at the weaving mill.

[0145]

[0146] Constraint (15) means that the completion time of order j at weaving mill a is not less than the completion time of the previous order plus its processing time at the weaving mill.

[0147]

[0148] Constraint (16) indicates: auxiliary constraint used to calculate the final completion time of order j at weaving mill a.

[0149]

[0150] Constraint (17) means that the completion time of order j at dyeing and printing plant b is not less than the sum of its completion time at weaving plant, transportation time from weaving plant to dyeing and printing plant b, and processing time at dyeing and printing plant b.

[0151]

[0152] Constraint (18) means that the completion time of order j at dyeing and printing plant b is not less than the completion time of its previous order plus its processing time at the dyeing and printing plant.

[0153]

[0154] Constraint (19) indicates: auxiliary constraint, used to calculate the final completion time of order j at dyeing and printing plant b.

[0155]

[0156] Constraint (20) means that the final completion time of order j is not less than the completion time of its dyeing plant plus the time it takes to transport from the dyeing plant to the client.

[0157]

[0158] Constraint (21) means: calculate the maximum completion time.

[0159]

[0160] Constraint (22) means: calculate transportation costs.

[0161]

[0162] Constraint (23) states: Calculate the reliability of each order. The reliability of an order is determined by the reliability of the weaving mill and the dyeing mill that process the order. For example, if order j is woven by weaving mill a (reliability 0.9) and then sent to dyeing mill b (reliability 0.9) for dyeing, then the reliability of the order is 0.9 * 0.9 = 0.81. If the order is split into multiple sub-orders for processing (i.e., multiple paths), then the final reliability of the entire order is equal to the product of the reliability of the multiple paths.

[0163]

[0164] Constraint (24) means: Guarantee that the reliability of each order is greater than the minimum reliability level.

[0165] Regarding constraint (23), it is assumed that the order is split into the following parts: Figure 6 As shown, the reliability of the final order is Figure 6 This is a schematic diagram illustrating an order splitting result according to an embodiment of this application. (Combined with...) Figure 6 Specifically, suppose there are 3 weaving mills, and their respective reliability probabilities are as follows: Suppose there are 3 printing and dyeing factories, and their respective reliability probabilities are as follows: Suppose an order is split into two sub-orders, and the processing path of one sub-order is from weaving factory 1 to dyeing factory 2. Then the reliability of this path is: The processing path for another sub-order is from weaving mill 2 to dyeing and printing mill 1. The reliability of this path is: Therefore, for this entire order, the reliability of the final order is: Therefore, for a complete order, the reliability of the final order is: (when as well as Take 1). The reasoning process for constraint (23) is described below:

[0166] Introducing the following approximation, if e1 and 22 ≈ 1, then e1 × e2 = (1 - (1 - e1))(1 - (1 - e2)) = 1 - (1 - e1) - (1 - e2) + (1 - e2)(1 - e1) ≈ 1 - (1 - e1) - (1 - e2).

[0167] Similarly: e1×e2×e3×e4=e1e2×e3e4≈1-(1-e1e2)-(1-e3e4)≈1-(1-(1-(1-e1)-(1-e2)))-(1-(1-(1-e3)-(1-e4)))≈1-(1-e1)-(1-e2)-(1-e3)-(1-e4). Therefore:

[0168] It can be approximated as follows:

[0169]

[0170] Therefore, the above constraint condition (23) can be obtained.

[0171] In this application, raw materials originate from a raw material factory, are processed by a weaving factory and a dyeing factory, and finally delivered to the customer. Processing follows a sequence of weaving first, followed by dyeing. In this embodiment, to ensure product quality, it is assumed that only one raw material factory is purchased for raw materials. There are multiple weaving factories and multiple dyeing factories. Sub-orders can be assigned to several different weaving and dyeing factories for collaborative completion, with the completion time of each sub-order being the longest among the sub-orders. Weaving and dyeing factories process products at a certain processing rate. Generally, for the same order, a factory processes the required quantity of products at once, and at most one order can be processed at a time. The quantity of products to be processed for each order is calculated in advance based on process losses. Each factory (weaving factory, dyeing factory) has a certain reliability probability; unreliability is caused by problems such as machine malfunctions. If historical data analysis shows that a factory has never experienced delays, the reliability probability is 100%. Furthermore, because the reliability of each factory differs, and the processing path of each order is different, the final delay probability of each order is also different.

[0172] In this application, after obtaining the above joint objective function (1) and constraints (2)-(24), the optimization strategy for each order to be processed can be obtained by using the joint objective function (1) and constraints (2)-(24).

[0173] For example, when the order processing system obtains the order information of the pending orders, such as... Figure 7 As shown, the following can be obtained: Figure 8 The results shown Figure 8 This refers to the optimized processing flow for each order. Figure 7 In this context, Uniform(X, Y) represents a random number between X and Y. For example, Uniform(30, 40) represents a random number between 30 and 40. Figure 7 This is a schematic diagram illustrating order information for an order to be processed, as shown in one embodiment of this application. Figure 8 This is a schematic diagram illustrating an order processing result according to an embodiment of this application. Figure 8 The meanings of the various patterns in the text can be as follows: Figure 9 As shown. Figure 9 This is a table showing the patterns and their meanings in one embodiment of this application.

[0174] In the order processing system, the Gurobi (a mathematical programming optimizer) solver is used to solve the decision optimization model, and the optimal gap is set to 4%. Figure 8 This corresponds to the case where there are 5 orders pending processing.

[0175] exist Figure 8 and Figure 9 In the diagram, pattern number 1 represents the order placement time; pattern number 2 represents the shipping time; pattern number 3 represents the time required to complete a certain order quantity (the quantity of products corresponding to the order) in the weaving factory; and pattern number 4 represents the time required to complete a certain order quantity in the dyeing and printing factory. Figure 8 The image shows five orders (one box per order), labeled j0, j1, j2, j3, and j4. The horizontal axis represents time in hours.

[0176] In this case, the symbol "j*A*num" on pattern number 3 represents the quantity of order j to be processed by weaving factory A, which is num in size. For example, for Figure 8 In the order j0, “j0 A1 20” means that order j0 will process 20 tons of fabric in weaving mill A1.

[0177] The marking "j*B*num_1, num_2from A*" on pattern number 4 represents that order j processes an order of size num_1 at dyeing and printing factory B, while the order of size num_2 comes from weaving factory A. For example, for Figure 8 The phrase “J0 B0 20,1from A1,19from A2” indicates that order j0 processes 20 tons of fabric in dyeing and printing factory B0, of which 1 ton comes from weaving factory A1 and 19 tons come from weaving factory A2.

[0178] This application obtains an optimization strategy for each order and uses this strategy to guide the order processing flow. On the one hand, this ensures that each order is processed while meeting various expected goals, enhancing the flexibility of order processing. On the other hand, the optimization strategy can better schedule various stages of the production process, thus serving as technical support for collaborative production and promoting efficient production scheduling for enterprises. It should be noted that, for the method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0179] like Figure 8As shown, the order processing system provides the processing flow (i.e. optimization strategy) for each order. Therefore, the various links in the production workshop can be scheduled according to this flow to complete the order processing.

[0180] Based on the same inventive concept, this application also provides an order processing system 1000. Figure 10 This is a structural block diagram of an order processing system according to an embodiment of this application. (Refer to...) Figure 10 The order processing system 1000 of this application may include:

[0181] A determination module is used to determine at least one order to be processed and an optimization objective corresponding to the order to be processed;

[0182] The acquisition module is used to input the information of the pending orders at each production node into a decision optimization model that matches the optimization objective, and to obtain the optimization strategy corresponding to each pending order. The optimization objective is used to optimize the processing flow of the pending orders from at least one dimension. The processing flow includes multiple production nodes.

[0183] The processing module is used to process the orders to be processed according to the optimization strategies corresponding to each order.

[0184] Optionally, the order processing system includes:

[0185] The first obtaining submodule is used to obtain functions of each of the optimization objectives based on at least one set optimization objective;

[0186] The combination submodule is used to combine the functions of the various optimization objectives into a joint objective function;

[0187] A submodule is constructed to build the decision optimization model based on the joint objective function, the constraints of each function, and the constraints of the joint objective function.

[0188] Optionally, the joint objective function is:

[0189] F(x)=∑ i∈I wi*fi(x)

[0190] Where I is the set of optimization objective types, indexed by i, and wi is the weight corresponding to the function of the i-th type of optimization objective, ∑ i∈I wi = 1.

[0191] Optionally, the obtaining module includes:

[0192] The solution submodule is used to solve the minimum value of the decision optimization model when the information of the order to be processed at each production node is taken as input, and to obtain the optimization strategy corresponding to each order to be processed according to the set and parameters involved in the decision optimization model.

[0193] Optionally, the obtaining module includes:

[0194] The second acquisition submodule is used to obtain information input by the user;

[0195] The determination submodule is used to determine the information of the order to be processed at each production node from the input information;

[0196] The third acquisition submodule is used to input the information of the orders to be processed at each production node into a decision optimization model that matches the optimization objective, so as to obtain the optimization strategy corresponding to each order to be processed.

[0197] Optionally, the optimization objective includes at least one of the following: lowest transportation cost, shortest delivery time, and lowest unreliability penalty cost.

[0198] Optionally, the information of the pending orders at each production node includes at least: order information of the pending orders, information of the factories used to process the pending orders, transportation time between different factories, transportation cost between different factories, and information of the equipment used to process the pending orders.

[0199] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0200] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0201] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products 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.

[0202] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will 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 program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0203] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0204] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0205] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0206] Finally, it should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0207] The above provides a detailed description of the order processing method, system, electronic device, and storage medium provided by the present invention. Specific examples have been used in this application to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An order processing method applied to a warp knitting enterprise, characterized in that, The method comprises: determining at least one to-be-processed order and an optimization target corresponding to the to-be-processed order; inputting information of the to-be-processed order at each production node into a decision optimization model matched with the optimization target, to obtain an optimization strategy corresponding to each to-be-processed order, the optimization target being used for optimizing a processing flow of the to-be-processed order from at least one dimension, the processing flow comprising a plurality of production nodes, and the optimization target comprising at least one of the following: lowest transportation cost, shortest delivery time length, and lowest unreliability penalty cost; processing the to-be-processed order according to the optimization strategy corresponding to each to-be-processed order; the decision optimization model comprises at least a constraint condition, and the constraint condition comprises at least reliability of each order and ensuring that the reliability of each order is greater than a minimum reliability level; solving a minimum value of the decision optimization model when the information of the to-be-processed order at each production node is input, under the constraint condition, and obtaining the optimization strategy corresponding to each to-be-processed order according to a set and parameters involved in the decision optimization model; the reliability of each order is determined according to the following formula: the reliability of each order is ensured to be greater than the minimum reliability level according to the following formula: where J represents the set of orders to be processed, indexed by j, A represents the weaving mill, indexed by a, B represents the printing and dyeing mill, indexed by b, K represents the set of processing positions of the order in the mill, indexed by k, represents the reliability probability of the weaving mill a, represents the reliability probability of the printing and dyeing mill b, represents the number of times the order j is processed for weaving at the kth position of the weaving mill a, represents the number of times the order j is processed for printing and dyeing at the kth position of the printing and dyeing mill b, represents the minimum reliability level that needs to be met, and are obtained by analyzing historical data on whether there has been a delay in the weaving mill and the printing and dyeing mill, respectively.

2. The method of claim 1, wherein, The method further comprises: obtaining a function of each optimization target according to at least one set optimization target; combining the functions of each optimization target into a joint objective function; constructing the decision optimization model according to the joint objective function, constraint conditions of each function, and constraint conditions of the joint objective function.

3. The method of claim 2, wherein, The joint objective function is: F(x)= wherein I is a set of types of the optimization objectives, indexed by i, and w i is a weight of a function corresponding to the i th type of the optimization objectives, .

4. The method of claim 3, wherein, inputting information of the to-be-processed order at each production node into a decision optimization model matched with the optimization target, to obtain an optimization strategy corresponding to each to-be-processed order, comprises: obtaining information input by a user; determining information of the to-be-processed order at each production node from the input information; inputting information of the to-be-processed order at each production node into a decision optimization model matched with the optimization target, to obtain an optimization strategy corresponding to each to-be-processed order.

5. The method of claim 1, wherein, The information of the to-be-processed order at each production node comprises at least: order information of the to-be-processed order, information of a factory used for processing the to-be-processed order, transportation time between different factories, transportation cost between different factories, and information of equipment used for processing the to-be-processed order.

6. An order processing system for a warp knitting enterprise, characterized by The system comprises: a determination module configured to determine at least one to-be-processed order and an optimization target corresponding to the to-be-processed order; an obtaining module configured to input information of the to-be-processed order at each production node into a decision optimization model matched with the optimization target, to obtain an optimization strategy corresponding to each to-be-processed order, the optimization target being used for optimizing a processing flow of the to-be-processed order from at least one dimension, the processing flow comprising a plurality of production nodes, and the optimization target comprising at least one of the following: lowest transportation cost, shortest delivery time length, and lowest unreliability penalty cost; a processing module configured to process the to-be-processed order according to the optimization strategy corresponding to each to-be-processed order; and The decision optimization model at least comprises a constraint condition, and the constraint condition at least comprises reliability of each order and ensuring that the reliability of each order is greater than a minimum reliability level; solving a minimum value of the decision optimization model when taking information of the to-be-processed orders at each production node as input under the constraint condition, and obtaining an optimization strategy corresponding to each to-be-processed order according to a set and a parameter involved in the decision optimization model; the reliability of each order is determined according to the following formula: the reliability of each order is ensured to be greater than the minimum reliability level according to the following formula: where J represents the set of orders to be processed, indexed by j, A represents the weaving mill, indexed by a, B represents the printing and dyeing mill, indexed by b, K represents the set of processing positions of the order in the mill, indexed by k, represents the reliability probability of the weaving mill a, represents the reliability probability of the printing and dyeing mill b, represents the number of times the order j is processed for weaving at the kth position of the weaving mill a, represents the number of times the order j is processed for printing and dyeing at the kth position of the printing and dyeing mill b, represents the minimum reliability level that needs to be met, and are obtained by analyzing historical data on whether there has been a delay in the weaving mill and the printing and dyeing mill, respectively.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that The program is executed by the processor to implement the steps in the order processing method of any one of claims 1-5.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor is executed to implement the steps in the order processing method of any one of claims 1-5.

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