Material optimization scheduling method, system and equipment for machining

Abstract

The invention relates to the technical field of machining scheduling optimization, and discloses a material optimization scheduling method for machining. The method comprises the following steps: collecting production and material consumption data of a workshop in past 30 days, and predicting by using an ARIMA model based on historical data; a scheduling priority is distributed for each production process, the material flow condition of each process is monitored, and material supply of low-efficiency processes is automatically identified and adjusted. Compared with the prior art that production demand changes cannot be dynamically responded, and particularly under the condition that demand fluctuation is large or production procedures are complex, an existing scheduling system is difficult to achieve real-time adjustment, so that production efficiency is low, and resources are wasted. According to the method, the demand prediction and priority scheduling algorithm based on the time sequence is introduced, and the real-time material flow monitoring is combined, so that the dynamic optimization scheduling of the material supply is realized, the problem that the material supply is delayed is avoided, and the production efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of material optimization scheduling for mechanical processing, and in particular, relates to a material optimization scheduling method, system and equipment for mechanical processing. Background Art

[0002] At present, the production scheduling system in the field of mechanical processing has certain shortcomings when dealing with complex production environments and demand fluctuations. For example, existing scheduling methods usually rely on static production plans and inventory management, which are difficult to adjust quickly according to changes in real-time production demand, resulting in problems such as untimely material supply, inventory backlog or out-of-stock. In addition, there is often poor coordination between processes in the production process, inefficient material flow, and easy production bottlenecks, which affects the overall production efficiency. Existing technologies cannot fully meet the needs of modern manufacturing for dynamic scheduling, flexible response to demand changes, and improved material flow efficiency. Therefore, there is an urgent need for a scheduling method that can still accurately predict material demand, dynamically adjust material supply according to real-time data, and promptly identify production bottlenecks in a complex production environment, so as to improve production efficiency, reduce resource waste, and optimize the overall production process. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a material optimization scheduling method for mechanical processing, aiming to solve the problem that the prior art cannot dynamically respond to changes in production demand, especially under conditions of large demand fluctuations or complex production processes, the existing scheduling system is difficult to achieve real-time adjustment, resulting in low production efficiency and waste of resources.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a material optimization scheduling method for mechanical processing,

[0005] The material optimization scheduling method for mechanical processing includes:

[0006] Step S10: Collect the production and material consumption data of the workshop in the past 30 days through the material management system, including the material inbound information, outbound information, inventory quantity and waiting time of each process;

[0007] Step S20: Based on historical data, use the ARIMA model to predict future material demand using the formula:

[0008]

[0009] Among them, X t represents the material demand at time t, c represents the mean of the time series, φ i is the autoregressive coefficient, which indicates the weight of the demand at time ti in the past on the demand at time t at the current moment.t-i represents the material demand data at time ti, θ j is the moving average coefficient, which represents the weight of the impact of past forecast errors on the current time t, ∈ t-j is the difference between the predicted value and the actual value at that time, ∈ t is the error term at the current moment, p indicates how many past moments of demand data the model uses to predict the current moment’s demand, and q indicates how many past moments of forecast error the model uses to adjust the current forecast value;

[0010] Step S30: According to the material demand forecast results and the real-time inventory, a scheduling priority is assigned to each production process. The priority is calculated by the formula:

[0011]

[0012] Among them, P i is the priority of process i, W i is the waiting time of process i, X t is the predicted material demand of process i at time t, k is a constant to prevent the denominator from being zero, μ is the weight coefficient for adjusting the waiting time, and W i The unit of μ is consistent with the material demand, S i It is the real-time inventory quantity, indicating the inventory quantity, and the unit is consistent with the material demand quantity;

[0013] Step S40: According to the process priority, the system dynamically plans and executes the material in and out process of the production warehouse and the outsourcing warehouse, and this plan is updated every 5 minutes;

[0014] Step S50: The system monitors the material input and output of each process by drawing a real-time updated material flow chart and calculates the material flow efficiency using the formula:

[0015]

[0016] Among them, E i is the material flow efficiency of process i, Q i is the material flow rate, T i The time taken for material input to output;

[0017] Step S60: Automatically identify bottleneck links with low material flow efficiency in production according to the material flow efficiency, and give priority to adjusting the material supply of low-efficiency processes.

[0018] Preferably, in step S10, the collected production and material consumption data also includes equipment operation status information of each process.

[0019] Preferably, in step S20, the parameters p and q of the ARIMA model are adjusted according to the scale of the actual historical data volume.

[0020] Preferably, step S20 also includes automatically eliminating abnormal data based on holidays and production shutdown information in historical data.

[0021] Preferably, in step S30, the constant k in the priority calculation formula is preset according to the urgency of different production processes.

[0022] Preferably, in step S30, the system can automatically trigger an early warning and send it to a front-end display device according to the predicted tightness of material supply.

[0023] Preferably, in step S50, the material flow diagram can be dynamically updated according to the material demand conditions of different production nodes.

[0024] The present invention also provides a material optimization scheduling system for mechanical processing, comprising:

[0025] The data collection module is used to collect the production and material consumption data of the workshop in the past 30 days through the material management system, including the material inbound and outbound information, inventory quantity and waiting time of each process;

[0026] The demand forecasting module is used to forecast future material demand based on historical data using the ARIMA model, using the formula:

[0027]

[0028] Among them, X t represents the material demand at time t, c represents the mean of the time series, φ i is the autoregressive coefficient, which indicates the weight of the demand at time ti in the past on the demand at time t at the current moment. t-i represents the material demand data at time ti, θ j is the moving average coefficient, which represents the weight of the impact of past forecast errors on the current time t, ∈ t-j is the difference between the predicted value and the actual value at that time, ∈ t is the error term at the current moment, p indicates how many past moments of demand data the model uses to predict the current moment’s demand, and q indicates how many past moments of forecast error the model uses to adjust the current forecast value;

[0029] The priority calculation module is used to assign scheduling priorities to each production process based on the material demand forecast results and real-time inventory. The priority is calculated using the formula:

[0030]

[0031] Among them, P i is the priority of process i, W i is the waiting time of process i, k is a constant to prevent the denominator from being zero, μ is the weight coefficient for adjusting the waiting time, W i The unit of μ is consistent with the material demand, S i It is the real-time inventory quantity, indicating the inventory quantity, and the unit is consistent with the material demand quantity;

[0032] Dynamic planning module, which is used to dynamically plan and execute the material in and out of the production warehouse and the outsourcing warehouse according to the process priority, and this plan is updated every 5 minutes;

[0033] The material monitoring module is used to monitor the material input and output of each process by drawing a real-time updated material flow chart and calculate the material flow efficiency using the formula:

[0034]

[0035] Among them, E i is the material flow efficiency of process i, Q i is the material flow rate, T i The time taken for material input to output;

[0036] The bottleneck identification module is used to automatically identify the bottleneck links with low material flow efficiency in production based on the material flow efficiency, and give priority to adjusting the material supply for low-efficiency processes.

[0037] The present invention also provides a material optimization scheduling device for mechanical processing, comprising a memory, a processor, and a material optimization scheduling program for mechanical processing stored in the memory and executable on the processor. When the material optimization scheduling program for mechanical processing is executed by the processor, the material optimization scheduling method for mechanical processing is implemented.

[0038] The present invention also provides a computer program product, including a material optimization scheduling program for mechanical processing, and when the material optimization scheduling program for mechanical processing is executed by a processor, the material optimization scheduling method for mechanical processing is implemented.

[0039] The beneficial effect of the present invention is that compared with the problem that the existing technology cannot dynamically respond to changes in production demand, especially under conditions of large demand fluctuations or complex production processes, the existing scheduling system is difficult to achieve real-time adjustment, resulting in low production efficiency and waste of resources. Since the present application introduces a demand forecast and priority scheduling algorithm based on time series, combined with real-time material flow monitoring, it realizes dynamic optimization scheduling of material supply, thereby avoiding the problem of delayed material supply and improving production efficiency and resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is a schematic flow chart of a first embodiment of a material optimization scheduling method for mechanical processing according to the present invention.

[0042] Figure 2 It is a schematic diagram of equipment for a method for optimizing scheduling of mechanically processed materials according to the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Embodiment 1: Figure 1 , which is a flow chart of the first embodiment of the material optimization scheduling method for mechanical processing of the present invention, and proposes the first embodiment of the material optimization scheduling method for mechanical processing of the present invention.

[0045] In a first embodiment, the material optimization scheduling method for mechanical processing includes:

[0046] Step S10: Collect the production and material consumption data of the workshop in the past 30 days through the material management system, including the material inbound information, outbound information, inventory quantity and waiting time of each process;

[0047] It should be noted that the purpose of collecting this data is to fully understand the material flow and consumption of the workshop, and to provide accurate historical basic data for subsequent demand forecasting. The inbound and outbound information of materials can help the system understand the dynamic changes of inventory, while the inventory volume provides the number of currently available materials, and the waiting time of the process helps analyze the bottlenecks or inefficient links of the production line. Through the comprehensive analysis of these data, the system can better optimize material allocation in scheduling and ensure the efficient operation of the production line.

[0048] Step S20: Based on historical data, use the ARIMA model to predict future material demand using the formula:

[0049]

[0050] Among them, X t represents the material demand at time t, c represents the mean of the time series, φ i is the autoregressive coefficient, which indicates the weight of the demand at time ti in the past on the demand at time t at the current moment. t-i represents the material demand data at time ti, θ j is the moving average coefficient, which represents the weight of the impact of past forecast errors on the current time t, ∈ t-j is the difference between the predicted value and the actual value at that time, ∈ t is the error term at the current moment, p indicates how many past moments of demand data the model uses to predict the current moment’s demand, and q indicates how many past moments of forecast error the model uses to adjust the current forecast value;

[0051] It should be noted that when using the ARIMA model to predict future material demand, the model can automatically adjust parameters based on historical data and ensure more accurate prediction results by considering the effects of autoregression and moving average. i and the moving average coefficient θ j With the introduction of , the system can make full use of past demand change trends and error information to achieve accurate prediction of future demand. This process helps the system prepare future material supply plans in advance, avoid material shortages or surpluses during the production process, and thus improve the efficiency and accuracy of production scheduling.

[0052] It should be understood that in the process of machining or production, the demand for some materials has obvious seasonal fluctuations. For example, the demand is higher in certain time periods (quarters, months), and lower in other time periods. The present invention supports adding a seasonal adjustment factor to the ARIMA model, and the improved model formula is:

[0053]

[0054] Among them, S t It is an adjustment item extracted based on seasonal fluctuations in historical data and is used to correct demand fluctuations caused by seasonal changes.

[0055] Step S30: According to the material demand forecast results and the real-time inventory, a scheduling priority is assigned to each production process. The priority is calculated by the formula:

[0056]

[0057] Among them, P i is the priority of process i, W i is the waiting time of process i, k is a constant to prevent the denominator from being zero, μ is the weight coefficient for adjusting the waiting time, Wi The unit of μ is consistent with the material demand, S i It is the real-time inventory quantity, indicating the inventory quantity, and the unit is consistent with the material demand quantity;

[0058] It should be noted that the scheduling priority formula comprehensively considers the waiting time W of the process. i , Forecast demand X t and real-time inventory S i To determine the priority of each process. The waiting time W in the formula i It reflects the time that the process has been in a waiting state. The longer the time, the more urgently the process needs material supply. t The ARIMA prediction result from the previous step S20 indicates the material demand of the process in the future. The greater the demand, the higher the priority of the process. i It indicates the actual situation of the current inventory. The less inventory, the higher the priority. The constant k is introduced to avoid the denominator being zero and to ensure the stability of the formula calculation. The specific value is set manually according to the actual scenario.

[0059] It can be understood that this formula quantifies the actual data in the production process, allowing the system to dynamically adjust the material supply sequence according to the actual needs of the process and allocate material resources in the best way, thereby reducing bottlenecks and delays in production.

[0060] It should be understood that the parameters in the priority formula can be adjusted according to different production scenarios. For example, for urgent processes, W may be increased. i The weight of S can be increased to make the waiting time have a greater impact; in the case of extremely tight inventory, S i The impact weight of the scheduling can be adjusted to ensure the priority supply to key processes. This flexible scheduling method greatly improves the adaptability and scheduling efficiency of the system in a complex production environment.

[0061] Step S40: According to the process priority, the system dynamically plans and executes the material in and out process of the production warehouse and the outsourcing warehouse, and this plan is updated every 5 minutes;

[0062] It should be noted that the system dynamically plans the material in and out of the warehouse according to the priority of each process to ensure that high-priority processes can get material supply in time. The plan includes the scheduling of the production warehouse (internal inventory) and the outsourcing warehouse (external supply). The system will decide whether the material is transferred from the internal inventory or purchased from the outside based on the actual inventory situation, the availability of external supply and the urgency of the process. The plan updated every 5 minutes enables the system to respond quickly based on real-time data and respond to changes in demand or unexpected situations in the production process in a timely manner, such as sudden interruptions in the process or sudden increases in demand. Through such frequent updates, the system can ensure the continuous supply of materials in the production process, reduce shutdowns caused by insufficient materials, and avoid inventory backlogs, thereby optimizing inventory management.

[0063] Step S50: The system monitors the material input and output of each process by drawing a real-time updated material flow chart and calculates the material flow efficiency using the formula:

[0064]

[0065] Among them, E i is the material flow efficiency of process i, Q i is the material flow rate, T i The time taken for material input to output;

[0066] Understandably, this formula is used to measure how smoothly materials flow through the production process. A higher flow efficiency means that materials can flow between processes in a more timely manner with less waiting time, thereby improving overall production efficiency. By continuously monitoring the flow of materials, the system can identify factors that affect efficiency and make timely adjustments.

[0067] Step S60: Automatically identify bottleneck links with low material flow efficiency in production according to the material flow efficiency, and give priority to adjusting the material supply of low-efficiency processes.

[0068] It should be noted that the system automatically identifies bottlenecks in the production process where material flow efficiency is low based on the material flow efficiency of each process. Low material flow efficiency usually means that the material supply of the process is not timely, the waiting time is too long, or the material flow is insufficient, which may lead to a decrease in the overall efficiency of the production line or stagnation.

[0069] It should be understood that once the system identifies a process with inefficient material flow, the system will prioritize adjusting the material supply of that process, assigning a higher priority to the bottleneck process to ensure that its material needs are met first during the scheduling process.

[0070] In addition, the material optimization scheduling system for mechanical processing provided by the present invention adopts a material optimization scheduling method for mechanical processing in the above embodiment, which can solve the technical problem of material optimization scheduling for mechanical processing. Compared with the prior art, the beneficial effects of the material optimization scheduling system for mechanical processing provided by the present invention are the same as the beneficial effects of the material optimization scheduling method for mechanical processing provided by the above embodiment, and other technical features of the material optimization scheduling system for mechanical processing are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0071] The present invention provides a material optimization scheduling device for mechanical processing, please refer to Figure 2, a material optimization scheduling device for mechanical processing includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a material optimization scheduling method for mechanical processing in the above-mentioned embodiment 1. A material optimization scheduling device for mechanical processing in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A material optimization scheduling device for mechanical processing is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. A material optimization scheduling device for mechanical processing may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. Various programs and data required for the operation of a material optimization scheduling device for mechanical processing are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow a material optimization scheduling device for mechanical processing to communicate wirelessly or wired with other devices to exchange data. Although a material optimization scheduling device for mechanical processing with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0072] The present invention also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the steps of the material optimization scheduling method for mechanical processing as described above are implemented. The computer program product provided by the present invention can solve the technical problem of material optimization scheduling for mechanical processing. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the material optimization scheduling method for mechanical processing provided by the above embodiment, and will not be described in detail here.

[0073] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0074] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0075] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A material optimization scheduling method for mechanical processing, characterized in that: The material optimization scheduling method for mechanical processing includes: Step S10: Collect the production and material consumption data of the workshop in the past 30 days, including the material inbound information, outbound information, inventory quantity and waiting time of each process; Step S20: Based on historical data, use the ARIMA model to predict future material demand using the formula: Among them, X t represents the material demand at time t, c represents the mean of the time series, φ i is the autoregressive coefficient, which indicates the weight of the demand at time ti in the past on the demand at time t at the current moment. t-i represents the material demand data at time ti, θ j is the moving average coefficient, which represents the weight of the impact of past forecast errors on the current time t, ∈ t-j is the difference between the predicted value and the actual value at that time, ∈ t is the error term at the current moment, p indicates how many past moments of demand data the model uses to predict the current moment’s demand, and q indicates how many past moments of forecast error the model uses to adjust the current forecast value; Step S30: According to the material demand forecast results and the real-time inventory, a scheduling priority is assigned to each production process. The priority is calculated by the formula: Among them, P i is the priority of process i, W i is the waiting time of process i, k is a constant to prevent the denominator from being zero, μ is the weight coefficient for adjusting the waiting time, W i The unit of μ is consistent with the material demand, S i It is the real-time inventory quantity, and the unit is consistent with the material demand; Step S40: Dynamically plan and execute the material in and outflow process of the production warehouse and the outsourcing warehouse according to the process priority, and this plan is updated every 5 minutes; Step S50: Monitor the material input and output of each process by drawing a real-time updated material flow chart, and calculate the material flow efficiency using the formula: Among them, E i is the material flow efficiency of process i, Q i is the material flow rate, T i The time taken for material input to output; Step S60: sort the material flow efficiency in ascending order, and adjust the material supply priority of the process from low to high according to the material flow efficiency.

2. A material optimization scheduling method for mechanical processing as claimed in claim 1, characterized in that: In step S10, the collected production and material consumption data also includes equipment operation status information of each process.

3. The material optimization scheduling method for mechanical processing according to claim 1, characterized in that: In step S20, the parameters p and q of the ARIMA model are adjusted according to the scale of the actual historical data volume.

4. The material optimization scheduling method for mechanical processing according to claim 1, characterized in that: Step S20 also includes automatically eliminating abnormal data based on holidays and production shutdown information in historical data.

5. The material optimization scheduling method for mechanical processing according to claim 1, characterized in that: In step S30, the constant k in the priority calculation formula is preset according to the urgency of different production processes.

6. The material optimization scheduling method for mechanical processing according to claim 1, characterized in that: In step S30, based on the predicted tightness of material supply, an early warning is automatically triggered and sent to the front-end display device.

7. The material optimization scheduling method for mechanical processing according to claim 1, characterized in that: In step S50, the material flow diagram can be dynamically updated according to the material demand conditions of different production nodes.

8. A material optimization scheduling system for mechanical processing, characterized in that: The material optimization scheduling system for mechanical processing includes: The data collection module is used to collect the production and material consumption data of the workshop in the past 30 days through the material management system, including the material inbound and outbound information, inventory quantity and waiting time of each process; The demand forecasting module is used to forecast future material demand based on historical data using the ARIMA model, using the formula: Among them, X t represents the material demand at time t, c represents the mean of the time series, φ i is the autoregressive coefficient, which indicates the weight of the demand at time ti in the past on the demand at time t at the current moment. t-i represents the material demand data at time ti, θ j is the moving average coefficient, which represents the weight of the impact of past forecast errors on the current time t, ∈ t-j is the difference between the predicted value and the actual value at that time, ∈ t is the error term at the current moment, p indicates how many past moments of demand data the model uses to predict the current moment’s demand, and q indicates how many past moments of forecast error the model uses to adjust the current forecast value; The priority calculation module is used to assign scheduling priorities to each production process based on the material demand forecast results and real-time inventory. The priority is calculated using the formula: Among them, P i is the priority of process i, W i is the waiting time of process i, k is a constant to prevent the denominator from being zero, μ is the weight coefficient for adjusting the waiting time, W i The unit of μ is consistent with the material demand, S i It is the real-time inventory quantity, indicating the inventory quantity, and the unit is consistent with the material demand quantity; Dynamic planning module, which is used to dynamically plan and execute the material in and out of the production warehouse and the outsourcing warehouse according to the process priority, and this plan is updated every 5 minutes; The material monitoring module is used to monitor the material input and output of each process by drawing a real-time updated material flow chart and calculate the material flow efficiency using the formula: Among them, E i is the material flow efficiency of process i, Q i is the material flow rate, T i The time taken for material input to output; The bottleneck identification module is used to automatically identify the bottleneck links with low material flow efficiency in production based on the material flow efficiency, and give priority to adjusting the material supply for low-efficiency processes.

9. A material optimization scheduling device for mechanical processing, characterized in that: The material optimization scheduling equipment for mechanical processing includes: A memory, a processor, and a material optimization scheduling program for mechanical processing stored in the memory and executable on the processor. When the material optimization scheduling program for mechanical processing is executed by the processor, the material optimization scheduling method for mechanical processing described in any one of Claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product includes a material optimization scheduling program for mechanical processing, and when the material optimization scheduling program for mechanical processing is executed by a processor, it implements the material optimization scheduling method for mechanical processing described in any one of Claims 1 to 7.

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