Order automatic scheduling method and system based on dynamic capacity connection and constraint optimization

Abstract

This invention relates to the field of intelligent order dispatching technology, and in particular to an automatic order scheduling method and system based on dynamic capacity linkage and constraint optimization. The method first receives production orders in real time, then parses and matches these orders with historical supply information to obtain supplier information, associated capacity group data, and procurement cycles. Next, it filters out raw material procurement data from the capacity group data that has not yet been scheduled. Then, it inputs the procurement cycles and raw material procurement data into a preset historical linkage analysis script and a dynamic capacity allocation script to output production scheduling orders for multiple capacity groups. Finally, it pushes these production scheduling orders to the corresponding suppliers. Compared to existing technologies, this invention solves the problems of poor driving force and large lag in existing production scheduling technologies.

Description

Technical Field

[0001] This invention relates to the field of intelligent order dispatch technology. More specifically, this invention relates to an automatic order scheduling method and system based on dynamic capacity matching and constraint optimization. Background Technology

[0002] In the industrial product production flow, the quantity and timing of raw material supply, as well as production line allocation, often determine whether industrial products can be delivered on time. To ensure on-time delivery, relevant personnel need to coordinate information with raw material suppliers and continuously monitor and track the product production schedule in real time. However, this approach has several major problems: On the one hand, the poor driving force of the continuous connection of raw materials is affected by subjective factors. This is reflected in the fact that most raw material suppliers, in the absence of fine management, will take advantage of their own emotional connections with relevant personnel and mostly choose to deliver goods in batches according to the "delivery deadline". Although they do not violate the total delivery deadline agreed in the contract, they disrupt the production line scheduling of the receiving production party, causing the automated production line to be idle before the material is delivered and then work at high load later. Moreover, for warehousing, the pressure of warehousing in the later stage is huge.

[0003] On the other hand, affected by the upstream and downstream environment of the supply chain, the delivery time of industrial product production orders will change with the changes in market demand. However, the existing manual coordination (information tracking or upstream and downstream docking) has a certain lag. This is because product market consumption itself will not stop due to holidays or personal factors, but relevant personnel need to take leave or adjust their status. The extension of these leave or status adjustment times will lead to the expansion of lag and cause the delivery rhythm of industrial products to be out of sync.

[0004] In summary, existing production scheduling technologies mainly suffer from poor driving force and significant lag. Summary of the Invention

[0005] To address the issues of poor driving force and significant lag in the aforementioned production scheduling technologies, this invention discloses an automatic order scheduling method and system based on dynamic capacity connection and constraint optimization.

[0006] In a first aspect, this invention discloses an automatic order scheduling method based on dynamic capacity matching and constraint optimization, comprising: Receive production orders in real time; The production orders are analyzed and matched with historical supply information to obtain supplier information, as well as associated capacity group data and procurement cycles. Filter out raw material procurement data that has not yet been scheduled from the capacity group data; Input the procurement cycle and raw material procurement data into the preset historical connection analysis script and capacity dynamic allocation script to output production schedule orders for multiple capacity groups; The production schedule order will be pushed to the corresponding supplier.

[0007] Beneficial effects: Through the above technical solution, when the producer receives a production order, it will automatically parse and match the production order to obtain the raw material supplier information and related data. Then, it will use a preset script to automatically generate the production schedule order, thereby avoiding the influence of human subjective emotions, realizing the automation and objectification of raw material supply urging, with a stronger driving effect and almost no lag problem.

[0008] Preferably, the historical continuity analysis script is configured as follows: Query the last historical schedule of raw material procurement data; Obtain the effective date of the changes; Calculate the new schedule start time and schedule verification time based on the change effective time and procurement cycle; Determine if the schedule verification time is later than the last historical schedule; If so, the start time of the production schedule order is defined as the start time of the new schedule; If not, the start time of the production scheduling order is defined as the last historical schedule.

[0009] Preferably, the dynamic capacity allocation script is configured as follows: In response to the input of raw material procurement data, query whether the corresponding raw material inventory exists; If so, calculate the daily supply and production schedule days for production orders based on raw material procurement data, raw material inventory, and the capacity of the corresponding production capacity group; If not, calculate the daily supply and production schedule days for the production order based on the raw material procurement data and the capacity of the corresponding production capacity group.

[0010] Preferably, the daily supply and production schedule days for production orders are calculated based on raw material procurement data, raw material inventory, and the capacity of the corresponding production capacity group, specifically as follows: Subtracting raw material inventory from raw material procurement data yields the order supply gap. Determine whether the supply gap of the order is greater than or equal to 0; If so, divide the order supply difference by the capacity of the corresponding capacity group to get the production scheduling days; allocate the first day's supply in the daily supply to raw material inventory, and allocate the daily supply other than the first day's supply to the capacity of the corresponding capacity group; If not, define the production scheduling days as a single day and configure the single-day supply as raw material procurement data.

[0011] Preferably, the daily supply and production schedule days for production orders are calculated based on raw material procurement data and the capacity of the corresponding production capacity group, specifically as follows: Divide the raw material procurement data by the capacity of the corresponding capacity group to obtain the production scheduling days; Configure the daily supply to the capacity of the corresponding capacity group.

[0012] Preferably, if there is a remainder in the number of production days, then only the integer part is taken and 1 is added.

[0013] Preferred options also include: Update production orders in response to changes in user interaction; Let's return to the steps of receiving production orders in real time.

[0014] Preferably, user interaction changes include at least custom delivery blocking dates, custom deadlines, and custom expedited labels.

[0015] Preferred options also include: In response to the generation of production schedule orders, the production schedule orders and their corresponding user interaction changes are stored in the database.

[0016] Secondly, the present invention also discloses an automatic order scheduling system based on dynamic capacity connection and constraint optimization, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the automatic order scheduling method based on dynamic capacity connection and constraint optimization described in the first aspect is implemented.

[0017] The beneficial effects of this invention are as follows: (1) Compared with the prior art, the method of the present invention solves the problems of poor driving effect and large lag in the existing production scheduling technology.

[0018] (2) Compared with the prior art, the method of the present invention has a dynamic capacity inheritance mechanism, which can realize the intelligent transfer of remaining capacity / inventory between materials, between capacity groups and between orders, thereby improving capacity utilization.

[0019] (3) Compared with the prior art, the method of the present invention supports high-precision scheduling with multiple constraints through manual interaction. Attached Figure Description

[0020] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein: Figure 1 This is a flowchart of the automatic order scheduling method based on dynamic capacity connection and constraint optimization in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the automatic order scheduling system based on dynamic capacity connection and constraint optimization in Embodiment 2 of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0023] Example 1 like Figure 1 As shown, this embodiment discloses an automatic order scheduling method based on dynamic capacity matching and constraint optimization, including: S10: Receive production orders in real time.

[0024] In this embodiment, the relevant production orders and the final generated production schedule can be used for the production of related products in the e-cigarette industry. The production orders are generally provided online by the client, and the readable medium for these orders can be contracts, invoices, or other electronic forms. In this step, manual interaction is also possible to add, delete, modify, and query relevant information about suppliers or production capacity groups.

[0025] For example, the parsing / matching information for the above production order can be:

[0026] S20: Analyze production orders and match them with historical supply information to obtain supplier information and associated capacity group data and procurement cycles.

[0027] It should be explained that in automated production processes, a production line is typically assigned one or more capacity groups. The capacity group data mainly includes the corresponding production line's daily capacity, production data, and raw material procurement data that has not yet been scheduled. A supplier can be configured with multiple capacity groups. The production order placement date precedes the actual procurement date, and the actual procurement date precedes the delivery schedule. The procurement cycle refers to the preparation period allowed for supplier delivery after the formal production scheduling order is placed.

[0028] Specifically, parsing production orders directly yields textual / identifiable information, such as "supplier" and "order number," as well as data constraints like "order quantity" and "procurement cycle." Beyond this direct parsing, the textual / identifiable information needs to be matched with historical data in the supporting system's database to obtain relevant capacity group data.

[0029] S30: Filter out raw material procurement data that has not yet been scheduled in the capacity group data.

[0030] It should be clarified that the aforementioned raw material procurement data that has not yet been scheduled refers to relevant data for production lines that have not yet started production. This data is directly proportional to the quantity ordered in the production orders. For example, based on the supplier code, material data that has not yet been delivered but can be scheduled is filtered from the database. Simultaneously, the material data is grouped by order number and sorted according to the effective date of any changes.

[0031] S40: Input the procurement cycle and raw material procurement data into the preset historical connection analysis script and capacity dynamic allocation script to output production schedule orders for multiple capacity groups.

[0032] Furthermore, the aforementioned historical continuity analysis script is configured as follows: S410: Query the last historical schedule of raw material procurement data.

[0033] S411: Get the effective date of the change.

[0034] S412: Calculate the new schedule start time and schedule verification time based on the change effective time and procurement cycle.

[0035] In this embodiment, the aforementioned scheduling verification time is later than the new scheduling start time.

[0036] For example, the formula for calculating the above-mentioned scheduling verification time is: D3 = D2 + Z + 1 In the formula, D3 represents the schedule verification time, D2 represents the change effective time, and Z represents the procurement cycle. Therefore, the start time of the new schedule is expressed as: D3 = D2 + Z It should be noted that if there are no manual changes, the above changes will take effect on the default date, which is the day the production order is issued.

[0037] S413: Determine if the schedule verification time is later than the last historical schedule: If so, the start time of the production schedule order is defined as the start time of the new schedule; If not, the start time of the production scheduling order is defined as the last historical schedule.

[0038] It should be explained that when the scheduling verification time is later than the last historical scheduling, it means that the product has not been produced for a considerable period of time and the relevant production capacity group has ceased production. In this case, a completely new scheduling model needs to be adopted to clear the relevant production capacity, and the scheduling start time is defined as the new scheduling start time. Conversely, when the scheduling verification time is earlier than the last historical scheduling, it means that the order is a "continuation order" that needs to be urgently inserted. In this case, its scheduling start time follows the last historical scheduling, thus inheriting the remaining historical production capacity.

[0039] For example, in the e-cigarette industry, the same e-liquid (raw material) may have flavors such as orange, strawberry, and blueberry. The delivery order needs to be configured according to the flavor of the order. Assuming that August 13th to 15th is the supplier's material preparation period, and the daily production capacity of the production line is 500 pieces (pieces) starting delivery on August 16th, and the first flavor has an order quantity of 400, then production can continue on the same day to produce the next flavor, producing 100 pieces. The above step S413 is precisely to determine whether the current schedule is a continuation from the historical schedule or a completely new schedule starting from the effective date of the change. This method can maximize the utilization of the production line capacity, minimize the waiting time for materials on the automated production line, and reduce warehousing pressure.

[0040] Furthermore, in addition to automatically defining the start time of raw material supply scheduling, it is also necessary to automatically plan the daily supply and actual production days within the procurement cycle. Therefore, the above-mentioned dynamic capacity allocation script is configured as follows: S420: In response to the input of raw material procurement data, query whether there is a corresponding raw material inventory.

[0041] S421: If so, calculate the daily supply and production schedule days for production orders based on raw material procurement data, raw material inventory, and the capacity of the corresponding capacity group.

[0042] More specifically, step S421 above involves first subtracting the raw material inventory from the raw material procurement data to obtain the order supply gap, and then determining whether the order supply gap is greater than or equal to 0; if so, dividing the order supply gap by the capacity of the corresponding capacity group to obtain the production scheduling days; configuring the first day's supply in the daily supply as raw material inventory, and configuring the daily supply other than the first day's supply as the capacity of the corresponding capacity group; if not, defining the production scheduling days as a single day, and configuring the single day's supply as raw material procurement data.

[0043] S422: If not, calculate the daily supply and production schedule days for production orders based on raw material procurement data and the capacity of the corresponding capacity group.

[0044] More specifically, step S422 above involves first dividing the raw material procurement data by the capacity of the corresponding capacity group to obtain the number of production days, and then configuring the daily supply to the capacity of the corresponding capacity group.

[0045] It should be noted that in the above steps S421-S422, there may be a remainder in the production schedule days. When the calculation result has a remainder, it is necessary to round the calculation and add 1 to indicate that the remaining products after the whole number of days of production need to be postponed to the next day.

[0046] S50: Push production schedule orders to the corresponding suppliers.

[0047] More specifically, production schedule orders can be exported from the supporting system and pushed to suppliers regularly via email.

[0048] Through the above steps S10-S50, the method of this embodiment automatically parses and matches production orders to obtain raw material supplier information and related data. Then, it uses a preset script to automatically generate production schedule orders, thereby avoiding the influence of subjective emotions of "people" and realizing the automation and objectification of raw material supply urging. This has a stronger driving effect and almost no lag problem.

[0049] Furthermore, before the aforementioned production schedule orders are automatically pushed out, a schedule confirmation mechanism can be added. That is, after the production schedule order is generated, a confirmation button pops up on the interaction interface of the relevant person in charge. When the confirmation is pressed, the order is then pushed out.

[0050] During steps S10-S50 above, or before the confirmation button is pressed, the method of this embodiment further includes: S100: In response to changes in user interaction, update the production order and return to step S10.

[0051] Through the above technical solution, the method of this embodiment can support free switching between manual and automatic modes, and supports information updates based on changes in user interaction. These user interaction changes can include custom delivery blocking dates, custom deadlines, and custom expedited tags.

[0052] It should be noted that the aforementioned custom delivery shield date typically refers to special holidays, such as the "Chinese New Year." When a custom delivery shield date is configured, the start time and number of days in the production schedule will be adaptively extended to working days based on the custom delivery shield date. The custom deadline affects the number of days in the production schedule and the daily supply; this custom deadline must be earlier than the scheduled delivery date. The custom expedited label is a priority scheduling label. When a product is configured with this label, it indicates that the order is an expedited order and requires priority supply of raw materials.

[0053] For example, the following is a production schedule order that was automatically generated without any user interaction:

[0054] If order number FW3 is notified as an expedited order, a custom expedited label needs to be assigned to FW3 in the user interaction change. At this point, execute step S100, and the regenerated production schedule order will be:

[0055] In this way, production schedules can be automatically rearranged through simple label assignment. It should be noted that the table above is only a specific example; in actual applications, the data involves more dimensions and the actual order volume is much larger.

[0056] Furthermore, the method in this embodiment also includes: In response to the generation of production schedule orders, the production schedule orders and their corresponding user interaction changes are stored in the database.

[0057] Through the above technical solution, every production schedule order generation and user interaction change will be archived to facilitate data traceability and accountability.

[0058] Example 2 Based on steps S10-S50 described in Embodiment 1 above, and integrated into the field of electronic cigarette production, the specific implementation process of this embodiment is as follows: By performing steps S10 and S20, we obtain: Supplier: A; Production Capacity Group: E-liquid; Matching daily production capacity: 1000 pieces / day; Procurement cycle: 3 days, delivery will begin 3 days after the order is placed; Weekend delivery: No; Current date: 2024-01-15 (Thursday).

[0059] Then, step S30 is executed to make the system filter out all unscheduled material data under the supplier's capacity group and sort them by order and change effective date.

[0060] The script for driving historical continuity analysis was executed as follows: The last scheduled end date was found to be 2024-01-09, with a remaining capacity of 150 units; the effective date of the change for the first order was 2024-01-08; Calculation: 2024-01-08 + procurement cycle + 1 day = 2024-01-12, which is later than 2024-01-09, therefore a new scheduled date was adopted, and the remaining capacity was cleared.

[0061] By driving the dynamic allocation script to run and scheduling orders one by one, we can obtain: Production schedule order 1: Material A has a purchase quantity of 3000 units; Number of days required = 3000 / 1000 = 3 days; Planned production: 1000 items on January 12, 2024 (Friday); Skip to January 13, 2024 (Saturday, no delivery); Skip to 2024-01-14 (Sunday, no delivery); 1000 items were shipped on Monday, January 15, 2024. 1000 items were produced on Tuesday, January 16, 2024. Remaining capacity: 0.

[0062] Production schedule order 2: Material B was purchased in quantities of 2500 units, with the change taking effect on January 10, 2024. The follow-up order 1 is scheduled to start from January 16, 2024; Number of days required = 2500 / 1000 = 3 days (500 items on the last day); Planned production: 1000 items on January 17, 2024 (Wednesday); 1000 items were sold on Thursday, January 18, 2024. 2024-01-19 (Friday) 500 items; Remaining production capacity (excess capacity): 500 units (1000-500).

[0063] Then, the above production schedule orders are stored in the data, the order status is marked as "pending delivery", and pushed to the corresponding raw material suppliers.

[0064] Example 3 like Figure 2 As shown, this embodiment also discloses an automatic order scheduling system based on dynamic capacity connection and constraint optimization, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the automatic order scheduling method based on dynamic capacity connection and constraint optimization described in Embodiment 1 is implemented.

[0065] The system in this embodiment also includes other components well known to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art, and therefore will not be described in detail here.

[0066] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0067] In the description of this specification, "multiple" means at least two, such as two, three or more, etc., unless otherwise expressly and specifically defined.

[0068] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. An order automatic scheduling method based on dynamic capacity linkage and constraint optimization, characterized in that, include: Receive production orders in real time; The production orders are parsed and matched with historical supply information to obtain supplier information and associated capacity group data and procurement cycles. Filter out the raw material procurement data that has not yet been scheduled from the aforementioned production capacity group data; Input the procurement cycle and the raw material procurement data into the preset historical connection analysis script and capacity dynamic allocation script to output production schedule orders for multiple capacity groups; The production schedule order is pushed to the corresponding supplier.

2. The method of claim 1, wherein, The historical continuity analysis script is configured as follows: Query the last historical schedule of the raw material procurement data; Obtain the effective date of the changes; Based on the effective time of the change and the procurement cycle, calculate the new schedule start time and schedule verification time; Determine whether the schedule verification time is later than the last historical schedule; If so, the start time of the production scheduling order shall be defined as the start time of the new schedule; If not, the start time of the production scheduling order is defined as the last historical scheduling.

3. The method of claim 1, wherein, The capacity dynamic allocation script is configured as follows: In response to the input of the raw material procurement data, query whether there is a corresponding raw material inventory; If so, calculate the daily supply and production days of the production schedule order based on the raw material procurement data, the raw material inventory, and the capacity of the corresponding capacity group; If not, calculate the daily supply and production days of the production schedule order based on the raw material procurement data and the capacity of the corresponding capacity group.

4. The method of claim 3, wherein, Based on the raw material procurement data, the raw material inventory, and the capacity of the corresponding production capacity group, the daily supply and production days of the production schedule order are calculated, specifically as follows: Subtracting the raw material inventory from the raw material procurement data yields the order supply gap. Determine whether the supply gap of the order is greater than or equal to 0; If so, divide the order supply difference by the capacity of the corresponding capacity group to obtain the production scheduling days; configure the first day's supply in the daily supply as the raw material inventory, and configure the daily supply other than the first day's supply as the capacity of the corresponding capacity group; If not, define the production scheduling days as a single day and configure the single-day supply as the raw material procurement data.

5. The method of claim 3, wherein, Based on the raw material procurement data and the corresponding production capacity group, the daily supply and production days of the production schedule order are calculated as follows: Divide the raw material procurement data by the capacity of the corresponding capacity group to obtain the production scheduling days; Configure the daily supply to the capacity of the corresponding capacity group.

6. The method of claim 4 or 5, wherein, If the number of production days has a remainder, then only the integer part is taken and 1 is added.

7. The method of claim 1, wherein, Also includes: Update the production order in response to changes in user interaction; Let's return to the steps of receiving production orders in real time.

8. The automatic order scheduling method based on dynamic capacity connection and constraint optimization according to claim 7, characterized in that, The user interaction changes include at least custom delivery blocking dates, custom deadlines, and custom expedited tags.

9. The automatic order scheduling method based on dynamic capacity connection and constraint optimization according to claim 7, characterized in that, Also includes: In response to the generation of the production schedule order, the production schedule order and its corresponding user interaction changes are stored in the database.

10. An automatic order scheduling system based on dynamic capacity matching and constraint optimization, characterized in that, It includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the automatic order scheduling method based on dynamic capacity connection and constraint optimization as described in any one of claims 1-9 is implemented.

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