Production scheduling method, electronic equipment and machine readable storage medium

Abstract

The invention discloses a production scheduling method, electronic equipment and a machine readable storage medium, and relates to the technical field of production scheduling. The method comprises the following steps: acquiring production task information and production resource state information; determining a multi-dimensional priority label of each process based on the production task information and the production resource state information; determining the dynamic priority of each process based on the multi-dimensional priority label and at least one dynamic weight factor, wherein the dynamic weight factor comprises a time dependence factor, a resource state dependence factor and an order value dependence factor; and carrying out production resource allocation and process sorting based on the dynamic priority of each process, and generating a production scheduling scheme. A multi-dimensional priority label and a dynamic weight factor are introduced, a dynamic priority evaluation mechanism comprehensively reflecting multi-dimensional constraints is established, accurate recognition and priority scheduling of high-value and high-emergency tasks are achieved in the face of a complex and changeable production environment, and the dynamic response capability of resource states and time pressure is guaranteed.

Description

Technical Field

[0001] This application relates to the field of production scheduling technology, specifically to a production scheduling method, electronic device, and machine-readable storage medium. Background Technology

[0002] In the fields of intelligent manufacturing and discrete manufacturing, production scheduling systems are the core of achieving efficient resource utilization and on-time order delivery. As market demand shifts towards multi-variety, small-batch, and highly customized production, systems need to simultaneously handle massive orders, complex process paths, and dynamic resource constraints, posing a significant challenge to traditional scheduling methods. Existing methods mainly fall into two categories: fixed-rule-based scheduling strategies, while offering rapid response, struggle to quantify and integrate multi-dimensional constraints such as order urgency, process criticality, and resource scarcity, resulting in high-value orders not being precisely prioritized; while mathematical programming-based optimization methods, while pursuing global optimum, suffer from complex modeling, time-consuming solutions, and a particular lack of real-time response capabilities to production disturbances such as equipment failures and urgent order insertions. Therefore, existing technologies exhibit significant shortcomings in the dynamic integration and real-time response to multi-dimensional constraints, making it difficult to achieve an effective balance between optimization quality and scheduling agility, thus becoming a technical bottleneck restricting further improvements in production efficiency. Summary of the Invention

[0003] In view of the above-mentioned shortcomings of the prior art, the purpose of this application is to provide a production scheduling method, an electronic device, and a machine-readable storage medium.

[0004] To achieve the above objectives, the first aspect of this application provides a production scheduling method, comprising: Obtain production task information and production resource status information; Based on production task information and production resource status information, determine multi-dimensional priority tags for each process in the production task. The dynamic priority of each process is determined based on multi-dimensional priority labels and at least one dynamic weight factor, wherein the dynamic weight factor includes time-dependent factor, resource status dependent factor and order value dependent factor. Production resources are allocated and processes are sorted based on the dynamic priority of each process to generate a production scheduling plan.

[0005] In this embodiment, the multi-dimensional priority tags include order priority tags, process priority tags, and resource priority tags. Based on production task information and production resource status information, the multi-dimensional priority tags for each process in the production task are determined, including: For each process in the production task: Determine the order priority label and process priority label based on production task information; Resource priority labels for processes are determined based on production resource status information.

[0006] In this embodiment of the application, the dynamic priority of each process is determined based on multi-dimensional priority labels and at least one dynamic weighting factor, including: For each process: The first weight is assigned to the order priority label based on the time-dependent factor and the order value-dependent factor. A second weight is assigned to the process priority label based on the resource status dependency factor and the order value dependency factor; The third weight preset for resource priority tags; The dynamic priority of a process is obtained by weighting the order priority label and first weight, the process priority label and second weight, and the resource priority label and third weight.

[0007] In this embodiment of the application, the production scheduling method further includes: For each process in the production task: Based on production task information, determine the delivery date and profit contribution of the order in which the process is located, as well as the resource requirements of the process; The available resources for a process are determined based on the status information of production resources; Determine time-dependent factors based on order delivery dates; Determine the order value dependence factor based on the order's profit contribution; Resource status dependency factors are determined based on available resources and resource demand.

[0008] In this embodiment of the application, production resources are allocated and processes are ordered based on the dynamic priority of each process to generate a production scheduling scheme, including: Determine production needs based on production task information; Determine production resource constraints based on production resource status information; Based on the dynamic priorities of each process, production demands, and production resource constraints, a mixed-integer programming model is constructed. The objective function of the mixed-integer programming model is configured to simultaneously minimize the following three terms: total order delay time, resource idle time, and order delay penalty weighted by dynamic priorities. Solve the mixed-integer programming model to generate a production scheduling scheme.

[0009] In this embodiment of the application, production requirements include order delivery dates and processing times for each process. Production resource constraints include equipment monopoly constraints, process sequence constraints, resource capacity constraints, and resource allocation constraints based on dynamic priority.

[0010] In this embodiment of the application, the production scheduling method further includes: If a pre-defined type of production disturbance event is identified, the set of affected processes is determined based on the production disturbance event; Select the first set of processes with a dynamic priority higher than a preset threshold from the set of affected processes; From the current production scheduling scheme, select the second process set, where the second process in the second process set has a resource occupation conflict with the first process in the first process set, and the dynamic priority of the second process is lower than that of the first process. Schedule the first process in the first process set to the resource time slot occupied by the second process in the second process set; Based on dynamic priority, resources are reallocated for the second process and the start time is updated to generate an updated production scheduling scheme.

[0011] In this embodiment of the application, the production scheduling method further includes: The feasibility of the updated production scheduling scheme is verified using a mixed integer programming model. If the feasibility verification is successful, the updated production scheduling plan will be output. If the feasibility verification fails, the production resource constraints of the mixed integer programming model are adjusted by at least one of the preset adjustment methods, and the step of selecting the second process set from the current production scheduling scheme is re-executed until the updated production scheduling scheme passes the feasibility verification. The preset adjustment methods include: Expand it to a candidate resource set when allocating resources for the first or second process set; Allows the allocation of the second process in the second process set to external resources.

[0012] A second aspect of this application provides an electronic device, comprising: The memory is configured to store instructions; The processor is configured to retrieve instructions from memory and, when executing instructions, to implement the production scheduling method as described in the above embodiments.

[0013] A third aspect of this application provides a machine-readable storage medium storing instructions that cause a machine to execute the production scheduling method described in the above embodiments.

[0014] The above technical solution acquires production task information and production resource status information; based on this information, it determines multi-dimensional priority labels for each process within the production task; and based on these labels and at least one dynamic weighting factor, it determines the dynamic priority of each process, including time-dependent, resource-dependent, and order-value-dependent factors. Production resources are allocated and processes are ordered based on the dynamic priorities of each process, generating a production scheduling plan. By introducing multi-dimensional priority labels and dynamic weighting factors, a dynamic priority evaluation mechanism is established that comprehensively reflects multi-dimensional constraints such as orders, processes, and resources. This enables accurate identification and priority scheduling of high-value, high-urgency tasks in complex and ever-changing production environments, while also ensuring dynamic responsiveness to resource status and time pressures. This effectively overcomes the shortcomings of traditional rule-based scheduling, such as lack of global optimization capabilities and the lag in response to mathematical programming methods, improving on-time order delivery rates and overall production resource utilization efficiency.

[0015] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The illustration shows a schematic flowchart of a production scheduling method according to an embodiment of this application; Figure 2 A schematic block diagram of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0019] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0020] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0021] Figure 1 The illustration shows a schematic flowchart of a production scheduling method according to an embodiment of this application. Figure 1 As shown in the figure, this application provides a production scheduling method, which may include the following steps: Step 100: Obtain production task information and production resource status information; It should be noted that obtaining production task information and production resource status information is a fundamental prerequisite for production scheduling. Only by fully grasping the relevant core data can subsequent priority calculations, resource allocation, and process sequencing be accurately implemented, avoiding scheduling schemes that deviate from the actual production scenario due to missing information. In this embodiment, production task information refers to various key data related to orders to be produced. Common data includes customer delivery dates, order amounts, profit contribution, product process routes, and standard processing times for each process. For example, the standard processing time for the turning process of a gear in an automotive parts order is 2 hours, and the standard processing time for the soldering process of a circuit board in an electronic equipment order is 1 hour. It may also include the customer type corresponding to the order, such as whether it is a long-term strategic customer. Production resource status information focuses on the availability of various resources during the production process, primarily covering equipment-related data such as the quantity, model, maximum continuous working time, precision level, and current operating status of equipment (reported as normal, faulty, or idle). For example, a factory may have 3 high-precision lathes and 5 ordinary milling machines, with one lathe currently under maintenance. It can also include personnel-related data, such as operators' skill levels, on-duty status, and preferred work processes, as well as material inventory status and supply cycles. For instance, a certain process may have sufficient special steel in stock, while another process requires electronic components that will arrive in 3 days. In practice, production task information defines the work content that needs to be completed, while production resource status information characterizes the capability boundaries and current conditions for performing these tasks.

[0022] Specifically, there are various ways to obtain production task information and production resource status information. For example, it can be automatically collected through Enterprise Resource Planning (ERP) systems and Manufacturing Execution Systems (MES), or supplemented through on-site data entry in the workshop and real-time feedback from equipment sensors to ensure the timeliness and accuracy of the information.

[0023] In this implementation, reliable data support is provided in all subsequent scheduling stages to ensure the feasibility and optimization of the scheduling plan, and to reduce problems such as production delays and resource waste caused by information errors.

[0024] Step 200: Based on production task information and production resource status information, determine the multi-dimensional priority tags for each process in the production task; It's important to note that defining multi-dimensional priority tags for processes is crucial for differentiating their importance. This avoids priority imbalances caused by relying on a single dimension, ensuring that high-value, critical processes are prioritized. Multi-dimensional priority tags categorize processes across multiple core dimensions. Their core purpose is to comprehensively consider the importance of processes in order delivery, production processes, and resource utilization, rather than depending on a single indicator. Common dimensions include order-level, process-level, and resource-level, combining production task information and production resource status information. By constructing multi-dimensional priority tags, production constraints from different sources and of different natures can be unified into comparable and calculable parameters, thus preparing for the subsequent generation of refined dynamic priorities.

[0025] Specifically, in one embodiment, the multi-dimensional priority tags include order priority tags, process priority tags, and resource priority tags. Based on production task information and production resource status information, the multi-dimensional priority tags for each process in the production task are determined, including: For each process in the production task: Determine the order priority label and process priority label based on production task information; Resource priority labels for processes are determined based on production resource status information.

[0026] It should be noted that the multi-dimensional priority tags are specifically divided into three categories: order priority tags, process priority tags, and resource priority tags. These three categories of tags mark the processes from three core perspectives: order attributes, process importance, and resource demand characteristics, respectively, comprehensively covering the key considerations for production scheduling.

[0027] Specifically, for each process, both order priority tags and process priority tags are determined based on production task information. Data such as order delivery date, order amount, and strategic customer attributes within the production task information are the core basis for classifying order priority tags. For example, the process corresponding to a strategic customer order with an urgent delivery date can have its order priority tag set to the highest level. The process priority tag level is determined by information such as the process's criticality in the product's process route, whether it is a bottleneck process, and whether it affects the progress of subsequent processes. For example, in mobile phone production, the process priority tag for chip installation is much higher than that for casing packaging. Resource priority tags are determined based on production resource status information, focusing on the scarcity, available quantity, and specialization of resources required by the process. For example, a process requiring high-precision testing equipment unique to the factory has a higher resource priority tag level than a process using general-purpose processing equipment. In practical applications, companies can preset tag level classification rules according to their own production characteristics. For example, order priority tags can be divided into four levels: urgent, urgent, regular, and ordinary; process priority tags into three levels: critical, important, and general; and resource priority tags into three levels: scarce, general, and external. It should be noted that the corresponding tags can be automatically matched based on production task information and production resource status information, or the tag level can be manually adjusted for special orders.

[0028] In this embodiment, a unified value assessment system is established through a detailed and clear label determination method, which enables more accurate priority attributes for each process, provides a reliable basis for subsequent weight allocation and priority calculation, and ensures the rationality of subsequent scheduling decisions.

[0029] Step 300: Determine the dynamic priority of each process based on multi-dimensional priority labels and at least one dynamic weight factor, wherein the dynamic weight factor includes a time-dependent factor, a resource status-dependent factor, and an order value-dependent factor. It should be noted that, to avoid scheduling rigidity caused by fixed priorities and to make priority judgments more closely reflect real-time production status, this embodiment introduces multiple dynamic weighting factors to correct static label values, thereby achieving dynamic adjustment of priorities. Dynamic priority refers to the fact that the priority of a process is not fixed but dynamically adjusted according to various factors changing during the production process; a smaller value generally indicates a higher priority. Multi-dimensional priority labels provide the basic classification basis for dynamic priorities, while dynamic weighting factors are used to quantify the importance of different dimensions in a specific scenario. The core factors include time-dependent factors, resource status dependent factors, and order value dependent factors.

[0030] Understandably, the time-dependent factor is related to the order delivery date, and the closer the delivery date, the larger the value of this factor; the resource status dependent factor is related to the tightness of resources required for the current process. If the current available quantity of a certain type of equipment is far lower than the required quantity, the resource tightness is high, and the value of the resource status dependent factor is larger; the order value dependent factor is determined based on the profit contribution and strategic value of the order, and the value of this factor is larger for high-profit orders or strategic customer orders.

[0031] Specifically, when calculating dynamic priorities, each multi-dimensional priority label must first be assigned a corresponding dynamic weight factor. Then, the final priority value is obtained through a reasonable comprehensive calculation method. For example, the values ​​corresponding to each dimension label are multiplied by the weight factors, summed, and then normalized. In the implementation process, the values ​​of each factor can be calculated in real time using a preset algorithm, and dynamic priorities can be automatically generated by combining the label data; alternatively, the calculation logic of each factor can be adjusted according to the enterprise's production strategy.

[0032] In this embodiment, by accurately matching process priorities with real-time production scenarios, urgent, high-value, and resource-constrained processes are given priority in obtaining resources, thereby improving on-time order delivery rate and resource utilization efficiency. Furthermore, the production plan is no longer a rigid sequence of instructions, but an intelligent decision that can adaptively respond to delivery pressure, resource bottlenecks, and changes in business value, thus improving the agility and economy of the scheduling system.

[0033] In one embodiment, determining the dynamic priority of each process based on multi-dimensional priority labels and at least one dynamic weighting factor includes: For each process: The first weight is assigned to the order priority label based on the time-dependent factor and the order value-dependent factor. A second weight is assigned to the process priority label based on the resource status dependency factor and the order value dependency factor; The third weight preset for resource priority tags; The dynamic priority of a process is obtained by weighting the order priority label and first weight, the process priority label and second weight, and the resource priority label and third weight.

[0034] It should be noted that dynamic priority is calculated by weighting the order priority label, process priority label, and resource priority label with their corresponding weights. The core idea is to quantify the importance of different dimensions through weights. The first weight is specifically assigned to the order priority label, which is jointly determined by the time-dependent factor and the order value-dependent factor. The time-dependent factor reflects the urgency of the order's delivery date, while the order value-dependent factor reflects the economic or strategic value of the order. The combination of the two allows the priority weight of the order dimension to change dynamically with the delivery date and value. For example, orders with close delivery dates and high profits will have a significantly higher first weight than ordinary orders with flexible delivery dates.

[0035] The second weight is assigned to the process priority label and is calculated from the resource status dependency factor and the order value dependency factor. The resource status dependency factor considers the scarcity of resources required by the process. When combined with the order value dependency factor, it can highlight the importance of processes with scarce resources and high order value. For example, if the equipment required for the key process of a high-value order is currently in severe shortage, its second weight will be increased accordingly.

[0036] The third weight is a fixed value preset for the resource priority label. This is because the scarcity and importance of resources are relatively stable, and a preset fixed weight simplifies the calculation while ensuring that resource considerations are not ignored. For example, the third weight can be uniformly set to 0.5 to ensure that resource factors occupy a reasonable proportion in priority calculation. During weighted calculation, the value corresponding to each label is usually multiplied by its corresponding weight, and then the product results are summed and normalized to obtain the total dynamic priority value of the process. The smaller the value, the higher the priority.

[0037] In one embodiment, the dynamic priority can be calculated with reference to the following formula:

[0038]

[0039]

[0040] in, Indicates dynamic priority; Indicates order priority label; Indicates process priority label; Indicates resource priority label; Indicates the first weight; This indicates the second weight; a negative weight indicates that the process priority is too high, which may inhibit excessive resource preemption. Indicates the third weight; Indicates time-dependent factors; Indicates resource state dependency factor; Indicates the order value dependency factor; This indicates the maximum value of the order priority tag; Indicates the maximum value of the process priority label; This indicates the maximum value of the resource priority tag. The multi-dimensional priority tags can be set based on actual circumstances, for example... O∈{0,1,2,3};Opr∈{0,1,2};R∈{0,1,2} .

[0041] In this embodiment, the above calculation method can comprehensively balance the importance of the three dimensions of orders, processes, and resources. At the same time, the priority is adjusted according to the production scenario through dynamic factors, ensuring that the priority results of each process are both comprehensive and in line with actual needs, providing an accurate basis for resource allocation and process sequencing.

[0042] Step 400: Based on the dynamic priority of each process, allocate production resources and sort the processes to generate a production scheduling plan.

[0043] It should be noted that production resource allocation refers to allocating resources such as equipment, personnel, and materials in the production process to corresponding processes based on their dynamic priority. Processes with higher priority receive better resources first. For example, the process with the highest dynamic priority can be allocated high-precision equipment and highly skilled operators, while lower-priority processes can be allocated general-purpose equipment or personnel on standby. Process sequencing determines the production order of each process based on its dynamic priority. High-priority processes are scheduled for production first. If multiple processes need to use the same resource, they are sorted in descending order of priority. If priorities are the same, auxiliary factors such as processing time and resource occupancy can be considered for sequencing. When generating a production scheduling plan, the resource allocation results and process sequencing order must be integrated, clearly defining the start and end times, allocated equipment and personnel, and required materials for each process to form a detailed production plan.

[0044] Specifically, based on the dynamic priority scores of all processes, an optimization algorithm is used for global resource allocation and sorting. In one embodiment, this can be implemented based on priority scheduling rules, that is, allocating the required resources to each process in descending order of dynamic priority and scheduling them in the earliest available time slot. In another embodiment, this can be implemented by constructing a mixed-integer programming mathematical model, taking minimizing the delay of high-priority processes as the core optimization objective, and solving the model while satisfying constraints such as equipment exclusivity and process sequence, thereby deriving a better production scheduling scheme at the global level. This production scheduling scheme will explicitly specify when, by which equipment, and by which operator each process will be executed.

[0045] In this embodiment, the business strategies and real-time status information carried by dynamic priorities are ultimately translated into work plans accurate to the minute and for each device. This improves the on-time delivery rate of high-value, high-urgency orders, while optimizing the resource utilization of the entire production system and reducing equipment idle time, thereby achieving a simultaneous improvement in production efficiency and fulfillment capabilities.

[0046] In this embodiment, production task information and production resource status information are acquired. Based on these information, multi-dimensional priority labels for each process within the production task are determined. The dynamic priority of each process is then determined based on these multi-dimensional priority labels and at least one dynamic weighting factor, where the dynamic weighting factor includes a time-dependent factor, a resource status-dependent factor, and an order value-dependent factor. Production resources are allocated and processes are ordered based on the dynamic priority of each process, generating a production scheduling scheme. By introducing multi-dimensional priority labels and dynamic weighting factors, a dynamic priority evaluation mechanism that comprehensively reflects multi-dimensional constraints such as orders, processes, and resources is established. This enables accurate identification and priority scheduling of high-value, high-urgency tasks in complex and ever-changing production environments, while also ensuring dynamic responsiveness to resource status and time pressures. This effectively overcomes the shortcomings of traditional rule-based scheduling, which lacks global optimization capabilities and suffers from lag in mathematical programming methods, thereby improving on-time order delivery rates and overall production resource utilization efficiency.

[0047] In one embodiment, the production scheduling method further includes: For each process in the production task: Based on production task information, determine the delivery date and profit contribution of the order in which the process is located, as well as the resource requirements of the process; The available resources for a process are determined based on the status information of production resources; Determine time-dependent factors based on order delivery dates; Determine the order value dependence factor based on the order's profit contribution; Resource status dependency factors are determined based on available resources and resource demand.

[0048] In this embodiment, it should be noted that the determination of the dynamic weighting factor relies on real-time calculation of specific production data. The dynamic weighting factor includes a time-dependent factor, an order value-dependent factor, and a resource status-dependent factor. The determination of all three depends on key data in the production task information and production resource status information. The time-dependent factor is determined based on the order's delivery date. The core logic is that the closer the delivery date, the larger the factor value, thus highlighting the priority of urgent orders. For example, if an order's delivery date is 5 days later, the time-dependent factor can be set to 1.0; when only 1 day remains, the factor can be adjusted to 5.0. The specific value can be determined through the company's preset time-factor correspondence rules, or it can be obtained through simple linear calculation. For example, it can be calculated using the following formula:

[0049] in, Indicates time-dependent factors; This indicates the number of days remaining until delivery.

[0050] Furthermore, the order value dependence factor is determined based on the order's profit contribution. The higher the profit contribution, the larger the factor value, thus ensuring the priority of high-value orders. For example, for high-profit-margin orders, the order value dependence factor can be set to 1.2, while for low-profit-margin orders, it can be set to 0.8. If the order includes strategic cooperation value, the factor value can be appropriately increased based on the profit contribution. The resource status dependence factor is determined by the ratio of available resources to resource demand. The greater the difference between available resources and resource demand, the larger the resource status dependence factor value, reflecting the degree of resource scarcity. For example, if a process requires 10 pieces of equipment and currently has 8 pieces available, the ratio is 0.8, and the factor can be set to 1.0; if there are 4 pieces available, the ratio is 0.4, and the resource status dependence factor can be set to 1.8. When determining dynamic weighting factors, the system can automatically extract order delivery dates, profit data, and resource supply and demand data, and calculate them in real time according to a preset algorithm. Alternatively, the calculation rules of the factors can be adjusted according to the enterprise's production strategy. For example, for resource-intensive enterprises, the sensitivity of the resource status dependence factor can be appropriately increased.

[0051] In this embodiment, by determining the dynamic weight factor based on actual production data, an accurate and objective input is provided for the dynamic priority. This allows the value of the dynamic weight factor to precisely match changes in the production scenario, ensuring the scheduling system's ability to perceive and respond to changes in the production environment.

[0052] In one embodiment, production resources are allocated and processes are ordered based on the dynamic priority of each process to generate a production scheduling plan, including: Determine production needs based on production task information; Determine production resource constraints based on production resource status information; Based on the dynamic priorities of each process, production demands, and production resource constraints, a mixed-integer programming model is constructed. The objective function of the mixed-integer programming model is configured to simultaneously minimize the following three terms: total order delay time, resource idle time, and order delay penalty weighted by dynamic priorities. Solve the mixed-integer programming model to generate a production scheduling scheme.

[0053] In this embodiment, it should be noted that Mixed Integer Programming (MIP) is a mathematical optimization model that integrates integer and continuous variables, capable of handling both discrete and continuous decision-making problems in production scheduling. When constructing the MIP model, production requirements are first clarified based on production task information. These requirements typically include order delivery dates and processing times for each process; these data serve as the core objective reference for model optimization. Then, production resource constraints are determined based on production resource status information to ensure that the output solution of the MIP model conforms to actual production capacity. The objective function of the MIP model is configured to simultaneously minimize three components: total order delay time, resource idle time, and a dynamically prioritized order delay penalty. Minimizing the total order delay time improves on-time delivery rate, minimizing resource idle time improves resource utilization efficiency, and the dynamically prioritized order delay penalty ensures that high-priority orders are minimized. The three objectives are balanced by weights, allowing for adjustments to the importance of each objective based on the company's production strategy.

[0054] In one embodiment, the objective function of the mixed-integer programming model can be of the following form:

[0055]

[0056] in, , , These are all weights of the objective function, which can be dynamically adjusted according to actual needs; j Indicates the process number; Indicate process j The start time; Indicates standard processing time; Indicate process j End time; Indicate process j Delivery date; Representing resources i In time period t The amount of free time; Representing resources iThe planned idle time within a time period can be predicted based on historical data. ； Indicate process j Dynamic priority; These are variables with values ​​of 0 or 1, where 1 represents an order. j On-time delivery.

[0057] Specifically, when solving this mixed-integer programming model, a professional mathematical solver, such as Gurobi or CPLEX, can be used. Input data such as production requirements, resource constraints, and dynamic priorities, and the solver will automatically calculate the optimal production scheduling scheme, clarifying key information such as the resource allocation results of each process, the production sequence, and the start and end times.

[0058] In this embodiment, the generation method based on mathematical models can fully consider multiple objectives and constraints, making the production scheduling scheme more scientific and optimized, while reducing the error and workload of manual scheduling and improving the feasibility and execution efficiency of production plans.

[0059] In one embodiment, production requirements include order delivery dates and processing times for each process step; Production resource constraints include equipment monopoly constraints, process sequence constraints, resource capacity constraints, and resource allocation constraints based on dynamic priority.

[0060] It's important to note that production demand is the core objective that production scheduling must meet. This primarily includes order delivery dates and the processing time for each process. Order delivery dates are the deadlines customers require for product delivery and are a key indicator that the scheduling plan must prioritize. Processing time refers to the standard duration required to complete each process, which can be determined based on process documents and historical production data. For example, the drilling process in machining may take 1 hour, and the welding process may take 2 hours. These data form the basis for determining the start and end times of processes. Production resource constraints are the limitations that must be followed in production scheduling to ensure that resource usage and process progression align with actual production capacity. These constraints mainly include equipment monopoly constraints, process sequence constraints, resource capacity constraints, and resource allocation constraints based on dynamic priorities. Among them, equipment exclusivity constraint means that the same equipment can only process one operation at a time to avoid equipment conflicts. For example, a lathe can only process one operation between 9:00 and 10:00 AM. Operation sequence constraint means that each operation of the same product must follow a preset process sequence. The subsequent operation can only begin after the preceding operation is completed. For example, the turning operation of a part must be completed before the milling operation can be performed. Resource capacity constraint means that the operation processing time cannot exceed the maximum continuous working time of the equipment to avoid equipment overload damage. For example, if a machine tool can work continuously for a maximum of 4 hours, the corresponding operation processing time cannot exceed 4 hours. Resource allocation constraint based on dynamic priority means that operations with high dynamic priority will be given priority to resources to ensure that the progress of important operations is not affected.

[0061] This embodiment, by clarifying the above specific content, provides clear rules for the construction of mixed integer programming models, enabling mixed integer programming models to more accurately simulate actual production scenarios, and the resulting scheduling schemes to be more feasible and operable, while avoiding production chaos caused by missing constraints.

[0062] In one embodiment, the production scheduling method further includes: If a pre-defined type of production disturbance event is identified, the set of affected processes is determined based on the production disturbance event; Select the first set of processes with a dynamic priority higher than a preset threshold from the set of affected processes; From the current production scheduling scheme, select the second process set, where the second process in the second process set has a resource occupation conflict with the first process in the first process set, and the dynamic priority of the second process is lower than that of the first process. Schedule the first process in the first process set to the resource time slot occupied by the second process in the second process set; Based on dynamic priority, resources are reallocated for the second process and the start time is updated to generate an updated production scheduling scheme.

[0063] In this implementation, it should be noted that production disruption events refer to various sudden events during the production process that affect the execution of the original scheduling plan. Common examples include equipment failure, new order insertion, and material delays. For instance, a critical processing equipment may suddenly fail, a customer may urgently place an additional order, or materials required for a certain process may not be delivered on time. Upon identifying such disruption events, the first step is to determine the set of affected processes, i.e., all processes that cannot proceed as originally planned due to the disruption event. For example, equipment failure will affect all in-process and pending processes on that equipment, and material delays will affect processes that depend on that material. Next, the first set of processes with a dynamic priority higher than a preset threshold is selected from the affected process set. Processes in the first set are typically urgent, high-value core processes that require priority. For example, processes with a preset threshold of 1 and dynamic priorities of 0 and 1 are included in the first set. Then, a second set of processes with resource conflicts and lower dynamic priorities is selected. For example, if the first process requires a certain piece of equipment, but that equipment is currently occupied by a lower-priority second process, then the second process is included in this set. Then, the first process is scheduled to the resource time slot occupied by the second process to ensure that the high-priority process proceeds first. At the same time, resources are reallocated to the second process and the start time is updated. For example, the second process is adjusted to other idle equipment or continues processing after the first process is completed, generating an updated production scheduling plan.

[0064] In this embodiment, priority-driven resource reallocation is used to achieve rapid response, localize the impact of disturbances, prioritize the progress of core processes, minimize the impact of disturbances on order delivery, and improve the flexibility and anti-interference capability of production scheduling.

[0065] In one embodiment, the production scheduling method further includes: The feasibility of the updated production scheduling scheme is verified using a mixed integer programming model. If the feasibility verification is successful, the updated production scheduling plan will be output. If the feasibility verification fails, the production resource constraints of the mixed integer programming model are adjusted by at least one of the preset adjustment methods, and the step of selecting the second process set from the current production scheduling scheme is re-executed until the updated production scheduling scheme passes the feasibility verification. The preset adjustment methods include: Expand it to a candidate resource set when allocating resources for the first or second process set; Allows the allocation of the second process in the second process set to external resources.

[0066] It should be noted that feasibility verification refers to reviewing the updated scheduling plan using a mixed-integer programming model. This involves checking whether the plan meets all production resource constraints and production requirements, such as whether there are conflicts where equipment processes multiple processes simultaneously, whether a process's start time is earlier than the previous process's end time, and whether a process's end time exceeds the order delivery date. If the verification passes, the plan is considered executable and can be directly output as the final production scheduling plan. If the verification fails, the plan is not immediately discarded; instead, an iterative adjustment process is initiated. The production resource constraints of the mixed-integer programming model are adjusted using preset adjustment methods, and the screening and scheduling steps are re-executed until the plan passes verification.

[0067] Specifically, the preset adjustment methods mainly include two categories. One is to expand the candidate resource set, that is, to increase the range of selectable resources when allocating resources to the first or second process set. For example, if the original candidate resources only included the workshop's own equipment, the expanded set can include collaborative equipment outside the workshop. The other is to allow the second process to be allocated to external resources. External resources refer to other available resources besides the originally required resource type, and these resources can meet the processing requirements of the process. For example, if a process originally required the use of a dedicated milling machine, the adjusted set can use multi-functional processing equipment from an external collaborating unit. In practical applications, the adjustment method can be selected based on the specific reason for the verification failure. For example, if the failure is due to equipment conflict, the candidate resource set can be expanded first; if the failure is due to insufficient resources, external resources can be called.

[0068] In this embodiment, feasible solutions are found through model verification and strategically relaxed constraints, ensuring the quality and reliability of the updated generation and scheduling scheme. This enables the system to not only react quickly when dealing with disturbances, but also to make robust and reliable decisions.

[0069] Figure 2 A schematic block diagram of an electronic device according to an embodiment of this application is shown. Figure 2 As shown, this application provides an electronic device that may include: Memory 1000 is configured to store instructions; The processor 2000 is configured to retrieve instructions from memory 1000 and to implement the aforementioned production scheduling method when executing instructions.

[0070] This application also provides a machine-readable storage medium storing instructions that cause a machine to execute the production scheduling method described above.

[0071] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0072] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function 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.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable 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.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0077] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0079] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A production scheduling method, characterized in that, include: Obtain production task information and production resource status information; Based on the production task information and the production resource status information, determine the multi-dimensional priority tags for each process in the production task. The dynamic priority of each process is determined based on the multi-dimensional priority labels and at least one dynamic weight factor, wherein the dynamic weight factor includes a time-dependent factor, a resource status-dependent factor, and an order value-dependent factor. Production resources are allocated and processes are sorted based on the dynamic priority of each process to generate a production scheduling plan.

2. The production scheduling method according to claim 1, characterized in that, The multi-dimensional priority tags include order priority tags, process priority tags, and resource priority tags. The process of determining the multi-dimensional priority tags for each process within a production task based on the production task information and the production resource status information includes: For each process in the production task: Based on the production task information, determine the order priority tag and process priority tag for the process; The resource priority label of the process is determined based on the production resource status information.

3. The production scheduling method according to claim 2, characterized in that, The determination of the dynamic priority of each process based on the multi-dimensional priority labels and at least one dynamic weighting factor includes: For each of the aforementioned processes: A first weight is assigned to the order priority label based on the time-dependent factor and the order value-dependent factor; A second weight is assigned to the process priority label based on the resource status dependency factor and the order value dependency factor; A third weight is preset for the resource priority label; The dynamic priority of the process is obtained by weighting the order priority label and the first weight, the process priority label and the second weight, and the resource priority label and the third weight.

4. The production scheduling method according to claim 1, characterized in that, Also includes: For each process in the production task: Based on the production task information, determine the delivery date and profit contribution of the order in which the process is located, as well as the resource requirements of the process; The available resource quantity for the process is determined based on the production resource status information; The time-dependent factor is determined based on the delivery date of the order; The order value dependence factor is determined based on the profit contribution of the order. The resource status dependency factor is determined based on the available resources and the resource demand.

5. The production scheduling method according to claim 1, characterized in that, The process of allocating production resources and sequencing processes based on the dynamic priority of each process to generate a production scheduling scheme includes: Production requirements are determined based on the aforementioned production task information; Production resource constraints are determined based on the aforementioned production resource status information; Based on the dynamic priorities of each process, the production demand, and the production resource constraints, a mixed integer programming model is constructed, wherein the objective function of the mixed integer programming model is configured to simultaneously minimize the following three terms: total order delay time, resource idle time, and order delay penalty weighted by dynamic priorities. Solve the mixed-integer programming model to generate a production scheduling scheme.

6. The production scheduling method according to claim 5, characterized in that, The production requirements include order delivery dates and the processing time for each of the aforementioned processes; The production resource constraints include equipment exclusivity constraints, process sequence constraints, resource capacity constraints, and resource allocation constraints based on the dynamic priority.

7. The production scheduling method according to claim 1, characterized in that, Also includes: If a production disturbance event of a preset type is identified, the set of affected processes is determined based on the production disturbance event; Select the first set of processes with a dynamic priority higher than a preset threshold from the set of affected processes; From the current production scheduling scheme, a second set of processes is selected, wherein the second process in the second set of processes has a resource occupation conflict with the first process in the first set of processes, and the dynamic priority of the second process is lower than that of the first process. Schedule the first process in the first process set to the resource time slot occupied by the second process in the second process set; Based on dynamic priority, resources are reallocated for the second process and the start time is updated to generate an updated production scheduling scheme.

8. The production scheduling method according to claim 7, characterized in that, Also includes: The feasibility of the updated production scheduling scheme is verified using a mixed integer programming model. If the feasibility verification is successful, the updated production scheduling plan will be output. If the feasibility verification fails, the production resource constraints of the mixed integer programming model are adjusted by at least one of the preset adjustment methods, and the step of selecting the second process set from the current production scheduling scheme is re-executed until the updated production scheduling scheme passes the feasibility verification. The preset adjustment methods include: Expanded to a candidate resource set when allocating resources for the first or second process set; Allows the second process in the second process set to be assigned to external resources.

9. An electronic device, characterized in that, include: The memory is configured to store instructions; The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the production scheduling method according to any one of claims 1 to 8.

10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the production scheduling method according to any one of claims 1 to 8.

Images