Tail single flexible overproduction scheduling method, device and equipment and computer storage medium

Abstract

This invention provides a method, apparatus, equipment, and computer storage medium for flexible overproduction scheduling of last-minute orders. After acquiring business data, this method dynamically identifies orders in the last-minute order stage and introduces proportionally controlled flexible overproduction hours only when last-minute orders exist. This overcomes the shortcomings of existing technologies that suffer from last-minute order delays and frequent production line switching due to strict adherence to hard capacity constraints, while also avoiding plan instability caused by global capacity slack. This method transforms the experience of manually handling last-minute orders into a systematic and controlled decision-making mechanism, preserving the flexibility of on-site management while ensuring the overall executability of the plan through last-minute order identification and flexible capacity proportional constraints, significantly improving the feasibility of scheduling results and the convenience of production organization.

Description

Technical Field

[0001] This invention relates to the field of production scheduling technology, and in particular to a flexible overproduction scheduling method, apparatus, equipment, and computer storage medium for last-minute orders. Background Technology

[0002] Existing production scheduling methods typically treat the upper limit of capacity as an insurmountable hard constraint, and perform limited capacity scheduling for demand orders. When the order demand exceeds the daily available capacity, the entire order delivery date is postponed or it is split into multiple non-continuous sub-orders.

[0003] However, in actual production scenarios, there is often a small amount of leftover stock demand at the end of the order production process. If the hard constraints of production capacity are strictly followed, an additional production cycle needs to be used to complete a small number of leftover stock, which leads to problems such as order delays, frequent production line switching, and work-in-process inventory backlog.

[0004] While some companies rely on on-site experience to handle last-minute orders, this approach is not modeled within the scheduling system, making it difficult to develop reusable and optimizable systematic decision-making solutions. Although existing technologies introduce soft constraints or penalty mechanisms, they typically result in overall capacity slack, failing to differentiate between order production stages or model the specific characteristics of last-minute order production, which can easily lead to uncontrolled capacity or unstable plans. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problem in the prior art that it is impossible to optimize scheduling by making controlled flexible capacity breakthroughs for the final stage of orders while maintaining the overall stability of the plan.

[0006] To address the aforementioned technical problems, this invention provides a flexible overproduction scheduling method, apparatus, equipment, and computer storage medium for end-of-line orders. This method retains the flexibility of on-site management while ensuring the overall executability of the plan through end-of-line order identification and flexible capacity ratio limits, significantly improving the feasibility of scheduling results and the convenience of production organization.

[0007] According to a first aspect of the present invention, the flexible overproduction scheduling method for last-order orders provided by the present invention includes the following steps:

[0008] Obtain the business data required for production scheduling, including at least order data, capacity data, end-of-line order judgment coefficient, and capacity flexibility coefficient;

[0009] The remaining production load of each order in each production cycle is determined based on the order data, and the presence of any orders in the final order production stage in the current cycle is identified based on the final order judgment coefficient.

[0010] If there are orders in the final production stage, then the flexible overtime hours for the current cycle are generated according to the capacity flexibility coefficient, and the flexible overtime hours are introduced into the preset constraints.

[0011] Based on the preset objective function and constraints, a mixed integer programming model is constructed, and the solver is called to solve it, generating a scheduling scheme that includes order production quantity, equipment allocation, and last-minute order identification.

[0012] Optionally, determining the remaining production load of each order in each production cycle based on the order data, and identifying whether there are any orders in the final order production stage in the current cycle based on the final order judgment coefficient, includes:

[0013] Calculate the remaining production load for each order before the start of the current cycle. The remaining production load is the product of the remaining demand quantity of the order and the standard working hours of the product in that order.

[0014] The remaining production load is compared with the standard capacity limit for the current cycle. If the proportion of the remaining production load to the standard capacity limit is lower than the tail-end judgment coefficient, then the order is determined to be in the tail-end production stage for the current cycle.

[0015] Optionally, the generation and use of the flexible overtime work hours shall meet the following conditions:

[0016] The flexible overtime hours shall not exceed the product of the current cycle standard capacity limit and the capacity flexibility coefficient;

[0017] The flexible overtime is only allowed to be subject to constraints if there is at least one order in the final production stage in the current cycle.

[0018] Optionally, the constraints include order demand fulfillment constraints, capacity balance constraints, production continuity constraints, and production changeover constraints, wherein:

[0019] The order demand satisfaction constraint is used to ensure that the total demand for each order must be completed within the planning period;

[0020] The capacity balance constraint is used to ensure that the total working hours occupied by all order production tasks in each production cycle do not exceed the sum of the standard capacity and flexible overtime working hours of that cycle;

[0021] The production continuity constraint is used to identify whether there is a production interruption between different production cycles for the same order;

[0022] The product changeover behavior constraint is used to identify product changeover events that occur in adjacent cycles due to product type switching.

[0023] Optionally, the objective function is a multi-objective optimization function, which includes at least one or a combination of the following optimization objectives:

[0024] Maximize the number of orders delivered on time;

[0025] Minimize the number of production interruptions;

[0026] Minimize the number of production change events;

[0027] Minimize the total amount of flexible overtime work.

[0028] Optionally, the solver is a COPT solver or a similar mixed integer programming solver, used to solve for the optimal scheduling scheme that optimizes the objective function while satisfying the constraints.

[0029] Optionally, the scheduling scheme includes at least the following output information:

[0030] Order number, order production quantity, production cycle, equipment allocation, capacity utilization, last-minute order status, on-time delivery status, and production disruption status.

[0031] According to a second aspect of the present invention, the flexible overproduction scheduling device for last-order orders provided by the present invention includes:

[0032] The data acquisition module is used to acquire the business data required for production scheduling. The business data includes at least order data, capacity data, end-of-line judgment coefficient, and capacity flexibility coefficient.

[0033] The end-of-line order identification module is used to determine the remaining production load of each order in each production cycle based on the order data, and to identify whether there are any orders in the end-of-line production stage in the current cycle based on the end-of-line order judgment coefficient.

[0034] The flexible capacity control module is used to generate flexible overtime hours for the current cycle based on the capacity flexibility coefficient when there are orders in the tail-end production stage in the current cycle, and to introduce the flexible overtime hours into preset constraints.

[0035] The model building and solving module is used to build a mixed integer programming model based on a preset objective function and constraints, call the solver to solve the model, and generate a scheduling plan that includes order production quantity, equipment allocation and last-minute order identification.

[0036] According to a third aspect of the present invention, the electronic device provided by the present invention includes:

[0037] Memory, used to store computer programs;

[0038] A processor is used to implement the steps of the flexible overproduction scheduling method for last-order orders provided in the first aspect when executing the computer program.

[0039] According to a fourth aspect of the present invention, the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described flexible overproduction scheduling method for last-order orders.

[0040] The technical solution of the present invention has the following advantages compared with the prior art:

[0041] The flexible overproduction scheduling method for end-of-line orders described in this invention dynamically identifies orders in the end-of-line stage after acquiring business data, and introduces proportionally controlled flexible overproduction hours only when end-of-line tasks exist. This overcomes the shortcomings of existing technologies that suffer from end-of-line delays and frequent production line switching due to strict adherence to hard capacity constraints, while also avoiding plan instability caused by global capacity slack. This method transforms the experience of manually handling end-of-line orders into a systematic and controlled decision-making mechanism, preserving the flexibility of on-site management while ensuring the overall executability of the plan through end-of-line order identification and flexible capacity proportional restrictions, significantly improving the feasibility of scheduling results and the convenience of production organization. Attached Figure Description

[0042] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0043] Figure 1 This is a flowchart illustrating the implementation of a flexible overproduction scheduling method for last-order orders provided by the present invention.

[0044] Figure 2 This is a structural block diagram of a flexible overproduction scheduling device for last-order orders provided in an embodiment of the present invention. Detailed Implementation

[0045] The core of this invention is to provide a flexible overproduction scheduling method, apparatus, equipment, and computer storage medium for end-of-line orders, which effectively solves the technical problems in the prior art that end-of-line orders are delayed due to strict adherence to hard capacity constraints, production lines are frequently switched, or plans are unstable due to global capacity slack.

[0046] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please refer to Figure 1. Figure 1 The flowchart illustrates the implementation of a flexible overproduction scheduling method for last-order orders provided by this invention; the specific operation steps are as follows:

[0048] S101: Obtain the business data required for production scheduling, including at least order data, capacity data, and end-of-line order judgment coefficient. and capacity flexibility coefficient ;

[0049] S102: Determine the remaining production load of each order in each production cycle based on the order data, and base the determination of the remaining orders on the order completion factor. Identify whether there are any orders in the final production stage of the current cycle;

[0050] S103: If there are orders in the final production stage, then according to the aforementioned capacity flexibility coefficient... Generate flexible overtime for the current cycle. And the flexible overtime is introduced into the preset constraints;

[0051] S104: Based on the preset objective function and constraints, construct a mixed-integer programming model, call the solver to solve it, and generate a model containing order scheduling quantities. Scheduling scheme for equipment allocation and last-minute order identification.

[0052] In some embodiments, the business data also includes product material data, such as material code, name, and category; order data is obtained through the ERP system interface, including order code, customer, material code, outstanding demand quantity, and demand date; capacity data is obtained through the MES system interface, including equipment code, capacity limit, and overtime coefficient; and a final order judgment coefficient. Capacity flexibility coefficient Target algorithm weights , It is maintained manually by the user.

[0053] In some other embodiments, step S101 is followed by a data preprocessing step, which calculates the order working hours and combines the order materials with the classification fields of the acquired business data to process the business data into the style required for algorithm modeling.

[0054] In one specific embodiment, a manufacturing company receives multiple customer orders daily. The system obtains order data through the ERP interface: Order A requires 100 units, with a demand date of day 5; Order B requires 200 units, with a demand date of day 8. Production capacity data is obtained through the MES interface: Equipment R1 has a daily standard production capacity limit of 100 hours, and Equipment R2 has a daily standard production capacity limit of 80 hours. The user sets a leftover order judgment coefficient. Capacity flexibility coefficient Target weight The system generates a scheduling plan after running this method.

[0055] Specifically, the production cycle is in days, but can also be in shifts, hours, or other time granularities; the solver uses the COPT solver, but can also use other mixed integer programming solvers such as Gurobi or CPLEX.

[0056] It should be noted that this method uses mathematical modeling to depict the actual production scheduling scenario, transforming the experience of manually handling leftover orders into a systematic decision-making scheme, and using mixed integer programming algorithm to help customers achieve globally optimal decisions that take into account the actual execution of leftover order production.

[0057] Based on the above embodiments, determining the remaining production load of each order in each production cycle according to the order data, and identifying whether there are any orders in the final order production stage in the current cycle based on the final order judgment coefficient, includes:

[0058] Calculate orders In the current cycle The remaining production load before commencement, wherein the remaining production load is the remaining demand quantity of the order and the standard working hours for the products in that order. The product of, i.e. Indicates from day 1 to day 2. The cumulative production volume per day;

[0059] The remaining production load is compared with the standard capacity ceiling for the current cycle. Comparisons were made, and the tail order marker variable was introduced. and sufficiently large constants An identification logic is constructed: if the proportion of the remaining production load to the standard capacity limit is lower than the tail-end judgment coefficient, then the order is determined to be in the tail-end production stage in the current cycle. The specific formula is as follows:

[0060]

[0061] in, If the variable is 0 or 1, then when the inequality holds true... A value of 1 indicates that the order is in the final production stage of the current cycle. This is a set of production cycles.

[0062] In some embodiments, the identification of leftover orders is performed dynamically at the beginning of each production cycle. The system calculates the remaining load in real time based on the current production schedule and the total order demand, avoiding planning deviations caused by static allocation.

[0063] In other embodiments, the identification of leftover orders not only considers the remaining load ratio, but also takes into account factors such as the urgency of the order's delivery date and the priority of the product type for comprehensive judgment. Orders identified as leftover orders can further obtain priority access to flexible capacity.

[0064] In one specific embodiment, the total demand for a certain order 90 units have been scheduled for production, 10 units remain, and the standard working hours for each product are [not specified]. Hourly rate per piece, remaining load is 5 hours. Total standard daily capacity is 100 hours, with a final order judgment factor. If the remaining load percentage is 5%, which is less than 10%, the system will automatically mark the order as a final production order in the current cycle. .

[0065] Specifically, in the formula For sufficiently large constants, they are used in mathematical programming to implement "if-then" logical constraints, when hour, The inequality degenerates into residual load. Double the production capacity; when When, the right side of the inequality is Double production capacity ,because If the value is sufficiently large, the inequality automatically holds and does not impose constraints on the model.

[0066] It should be noted that the tail-end order identification mechanism in this embodiment ensures that flexible capacity is only activated when the order is truly in the final stage by calculating and determining the proportion of the remaining load in real time. This effectively prevents the abuse of overproduction quotas and avoids resource waste and order delays caused by a small number of tail-end orders occupying the entire production cycle.

[0067] Based on the above embodiments, the generation and use of flexible overtime work hours meet the following conditions:

[0068] Flexible overtime work hours Not exceeding the current cycle standard production capacity limit With the aforementioned capacity flexibility coefficient The product of is expressed by the formula:

[0069]

[0070] in, For production resource indexing, It is a collection of production resources.

[0071] The flexible overtime work hours are only allowed to be subject to constraints when there is at least one order in the final production stage in the current cycle, and the formula is expressed as:

[0072]

[0073] in For a sufficiently large constant, For orders In the cycle The tail order marker variable.

[0074] The scheduling scheme can be used in parallel with the standard capacity, allowing the scheduling scheme to exceed the standard capacity limit within a certain period, but the extent of the exceedance is limited. Strict control.

[0075] In other embodiments, The value setting is related to the type of production resources; different equipment or production lines can be configured with different flexibility coefficients. For example, highly automated production lines can be set with a smaller value. Values ​​(e.g., 5%) can be set for manual assembly lines, while larger values ​​can be set for manual assembly lines. Values ​​(such as 20%) are used to accommodate temporary overtime arrangements.

[0076] In one specific embodiment, the daily standard capacity limit of a certain production line is 100 hours, and the capacity flexibility coefficient is... The maximum flexible overtime hours allowed on that day Hour. The system detected last-minute order tasks for the day. This allows for the introduction of up to 10 hours of flexible working hours in capacity balancing to complete the production of leftover orders.

[0077] Specifically, the introduction of flexible overtime work adopts a switch-type control logic: only when... hour, Only values ​​greater than 0 are allowed; otherwise The limit is set to 0. This mechanism ensures that flexible capacity is activated only when truly needed, preventing planners from abusing their overproduction authority and causing uncontrolled capacity.

[0078] It should be noted that the controlled flexible capacity breakthrough mechanism in this embodiment transforms the temporary overtime work to handle last-minute orders based on manual experience into a systematic modeling decision-making process. This retains the flexibility of on-site management while ensuring the overall stability of the plan through proportional restrictions and triggering conditions.

[0079] Based on the above embodiments, the constraints include order demand fulfillment constraints, capacity balance constraints, production continuity constraints, and production changeover constraints, wherein:

[0080] The order demand satisfaction constraint is used to ensure that the total demand for each order must be completed within the planning period, and its specific formula is expressed as follows:

[0081]

[0082] in For orders In the cycle Production quantity, For orders Total demand;

[0083] The capacity balance constraint is used to ensure that the total working hours occupied by all order production tasks in each production cycle do not exceed the sum of the standard capacity and flexible overtime for that cycle. The specific formula is as follows:

[0084]

[0085] in For orders Corresponding product type Standard working hours per unit product For resources In the cycle Standard working hours limit For period Flexible overtime work hours;

[0086] The production continuity constraint is used to identify whether there is a production interruption between different production cycles for the same order. It does this by introducing a disruption variable. Modeling, Indicates order In the cycle and A production disruption occurred between them;

[0087] The production changeover behavior constraint is used to identify production changeover events caused by product type switching in adjacent cycles. It introduces production changeover variables. Modeling is performed to satisfy:

[0088]

[0089] in For orders In the cycle Whether production is scheduled is a 0-1 variable. For orders In the cycle Whether production is scheduled is a 0-1 variable. For orders Corresponding product types, For orders Corresponding product types, This is a set of production cycles.

[0090] In some embodiments, order demand satisfaction constraints are the basic premise of scheduling, ensuring that the delivery quantity of each order is guaranteed; capacity balance constraints are the core constraints of the model, which unify the modeling of standard capacity and flexible overtime, enabling the solver to optimize scheduling while satisfying hard constraints.

[0091] In other embodiments, production continuity constraints are implemented by penalizing disruption variables. This encourages continuous production of the same order, reducing work-in-process inventory and production changeover costs. The value is automatically determined by the production schedule of adjacent cycles in the production plan, when the order... In the cycle and When production is scheduled, no breakage occurs; otherwise, breakage occurs.

[0092] In one specific embodiment, if an order is scheduled for production in both cycle 1 and cycle 3, but not in cycle 2, the system determines that the order has experienced a production disruption between cycle 1 and cycle 2, and between cycle 2 and cycle 3. , And apply the corresponding penalty to the objective function.

[0093] Specifically, the production changeover constraint determines whether a production changeover should occur by comparing the product types of orders scheduled in adjacent cycles. If the cycle... Product A in production schedule, cycle Product B is scheduled for production, and ,but This indicates that a production change event has occurred. This variable serves as a penalty term in the objective function, encouraging the production line to continuously produce the same product.

[0094] It should be noted that the four constraints in this embodiment together form the complete framework of the scheduling model: the order demand satisfaction constraint ensures delivery, the capacity balance constraint ensures feasibility, and the production continuity constraint and production changeover behavior constraint optimize the executability of the plan from different dimensions, so that the generated scheduling scheme satisfies both resource constraints and actual production organization habits.

[0095] Based on the above embodiments, the objective function is a multi-objective optimization function, which includes at least one or a combination of the following optimization objectives:

[0096] Maximize the number of orders delivered on time by using on-time delivery variables. It means that the weight is ;

[0097] Minimize the number of production interruptions by using breakage variables. It means that the weight is ;

[0098] Minimize the number of production change events by using production change variables. It means that the weight is ;

[0099] Minimize the total amount of flexible overtime work used, through flexible overtime work It means that the weight is ;

[0100] The objective function has the following specific formula:

[0101]

[0102] In some embodiments, the objective function uses a weighted summation method to unify multiple optimization objectives into a single-objective optimization problem, with weight coefficients... It is maintained manually by the user according to business priorities.

[0103] In other embodiments, the objective function can be extended to include more optimization terms, such as minimizing the total production cycle, maximizing equipment utilization, and minimizing material consumption, to adapt to the diverse needs of different enterprises.

[0104] In one specific embodiment, a company sets up... (On-time delivery is the most important) (Minimize production disruptions) (Appropriately reduce production changes) (Minimize the use of flexible capacity). The solver prioritizes on-time order delivery while satisfying all constraints, followed by minimizing disruptions, then minimizing production changes, and finally considering the use of flexible working hours.

[0105] Specifically, timely delivery of variables The definition is: if the order On the demand date Complete all production before that, i.e. ,but ,otherwise This variable appears as a positive term in the objective function, incentivizing the model to complete orders as quickly as possible.

[0106] It should be noted that the multi-objective optimization function in this embodiment incorporates multiple key performance indicators in production management into the mathematical model, so that the scheduling results not only meet capacity constraints, but also achieve a balance between on-time delivery, production continuity, changeover costs, and flexible capacity utilization, thus achieving true global optimization.

[0107] Based on the above embodiments, the solver is a COPT solver or a similar mixed integer programming solver, used to solve for the optimal scheduling scheme that optimizes the objective function while satisfying the constraints.

[0108] In some embodiments, the solver uses a branch and bound algorithm to solve the mixed integer programming model and outputs an optimal or near-optimal scheduling scheme that satisfies all constraints within a finite time.

[0109] In other embodiments, the solver supports a warm-start function, which can perform incremental optimization based on the previous solution results. This is suitable for dynamic adjustment scenarios, such as rapid rescheduling in the event of order changes or equipment failures.

[0110] In one specific embodiment, the system receives a scheduling task for 100 orders, a 30-day production cycle, and 20 pieces of equipment. The constructed mixed-integer programming model contains tens of thousands of variables and constraints. The COPT solver is invoked, with a maximum solution time of 300 seconds. The solver outputs a scheduling scheme that satisfies all constraints and has the optimal objective function value within the time limit.

[0111] Specifically, the COPT solver (Cardinal Operations CPLEX Optimizer) is a high-performance mathematical optimization solver that supports solving large-scale linear programming, integer programming, and mixed integer programming problems. It features fast solution speed and high stability.

[0112] It should be noted that this embodiment adopts a combination of mixed integer programming and commercial solvers to transform the complex production scheduling problem into a computable and solvable mathematical model, which not only ensures the theoretical optimality of the solution, but also has engineering feasibility, and can be applied to large-scale, multi-constraint complex production environments.

[0113] Based on the above embodiments, the scheduling scheme includes at least the following output information:

[0114] Order number, order production quantity Production cycle Equipment allocation Capacity utilization status, and end-of-line order identification. On-time delivery mark Production breakage marking .

[0115] In some embodiments, the scheduling scheme is output in tabular form, such as an order plan scheduling table, which includes fields such as order number, finished product code, outstanding quantity, whether production is scheduled, production quantity, required date, equipment allocation, capacity utilization, last-minute order, on-time delivery, and production interruption.

[0116] In other embodiments, the scheduling scheme is displayed in the form of a visual Gantt chart, which intuitively shows the production time, quantity, and end-of-line status of each order on each device, making it easier for planners to understand and adjust.

[0117] In one specific embodiment, the system outputs the following scheduling results: Order A is scheduled to produce 50 units in cycle 1, using equipment R01, with a capacity utilization of 25 hours and a tail order identifier of 0; in cycle 2, it is scheduled to produce 20 units, using equipment R01, with a capacity utilization of 10 hours and a tail order identifier of 1; the on-time delivery identifier is 1, and the production disruption identifier is 0. The planner arranges production accordingly and schedules temporary overtime for the tail order task in cycle 2.

[0118] Specifically, capacity utilization is displayed in man-hours, i.e., the production quantity is multiplied by the standard man-hours; equipment allocation displays the specific production line or equipment number; on-time delivery indicator shows whether the order can be completed before the required date; production interruption indicator shows whether there is an interruption in the production process of the order.

[0119] It should be noted that the scheduling output in this embodiment not only includes the basic production quantity and time arrangement, but also includes the identification information of key decision variables such as last-minute orders, on-time delivery, and production disruptions, making the scheduling results highly interpretable and executable, which makes it easier for planners to understand the model's decision logic and to intervene manually when necessary.

[0120] Based on the above embodiments, the present invention proposes a flexible overproduction scheduling method for last-order orders. This method constructs a mixed-integer programming model that includes constraints on order demand fulfillment, capacity balance, production continuity, and production changeover behavior. Furthermore, it introduces a dynamic last-order identification mechanism based on the remaining load ratio. Breakthrough Logic of Controlled Flexible Production Capacity Combined with maximizing on-time delivery Minimize production interruptions Minimize the number of production changes Minimize the use of flexible overproduction The multi-objective optimization function with the objective of achieving global optimal scheduling decision-making is realized with the support of solvers such as COPT.

[0121] Compared with the prior art, the present invention has the following beneficial effects:

[0122] First, by linking the identification of leftover orders with controlled flexible production capacity, the temporary experience of manually handling leftover orders is transformed into a systematic mathematical model. This allows a small number of leftover orders to be completed through local capacity breakthroughs without taking up an additional full production cycle, significantly reducing the problems of order delays, frequent production line switching, and work-in-process backlog caused by leftover orders.

[0123] Second, by introducing production continuity constraints and production changeover constraints, the continuity and stability of the production scheduling plan are effectively guaranteed, production interruption and changeover costs are reduced, and the convenience of production organization is improved.

[0124] Third, determine the coefficient through the last order. and capacity flexibility coefficient The configurable design allows this method to flexibly adapt to the differentiated needs of different product types, production line characteristics, and business scenarios. Users can dynamically adjust parameters according to actual production conditions to achieve personalized scheduling strategies.

[0125] Fourth, multiple objectives such as on-time delivery, production continuity, production changeover control, and flexible capacity utilization are unified in the model and weighted by coefficients. , The adjustment achieves synergistic optimization among multiple objectives, enabling the scheduling results to balance delivery fulfillment rate, production efficiency, and resource utilization rate while meeting capacity constraints, thus truly achieving global optimization rather than local optimization.

[0126] Fifth, the output scheduling scheme includes a last-minute order identifier. On-time delivery mark Production breakage marking Key decision-making information, such as the model's decision-making logic, makes the planning results highly interpretable and executable, making it easier for planners to understand the model's decision-making logic and to intervene manually when necessary, thus greatly improving the feasibility of implementing the scheduling scheme in actual production.

[0127] In summary, this invention transforms the complex problem of handling last-minute orders into a systematic, computable, and optimizable decision-making scheme through mathematical modeling. While ensuring the stability of the plan, it enhances production flexibility and provides enterprises with an intelligent scheduling solution that combines theoretical optimality with engineering practicality.

[0128] Based on the same inventive concept, this invention also provides a flexible overproduction scheduling device for last-order orders. (Reference) Figure 2 , Figure 2 A structural block diagram of a flexible overproduction scheduling device for last-minute orders provided in an embodiment of the present invention. The device may include:

[0129] The data acquisition module 100 is used to acquire business data required for production scheduling. The business data includes at least order data, capacity data, end-of-line judgment coefficient, and capacity flexibility coefficient.

[0130] The tail-end order identification module 200 is used to determine the remaining production load of each order in each production cycle based on the order data, and to identify whether there are any orders in the tail-end order production stage in the current cycle based on the tail-end order judgment coefficient.

[0131] The flexible capacity control module 300 is used to generate flexible overtime hours for the current cycle based on the capacity flexibility coefficient when there are orders in the tail-end production stage in the current cycle, and to introduce the flexible overtime hours into preset constraints.

[0132] The model building and solving module 400 is used to build a mixed integer programming model based on a preset objective function and constraints, call the solver to solve the model, and generate a scheduling scheme that includes order production quantity, equipment allocation and tail order identification.

[0133] In some embodiments, the data acquisition module interfaces with the ERP system and MES system to obtain order data and capacity data in real time through API interfaces; the end-of-line judgment coefficient, capacity flexibility coefficient, and target algorithm weight are manually entered through the user configuration interface.

[0134] In other embodiments, the apparatus further includes a data preprocessing module for cleaning, transforming, and calculating the acquired business data, including order time occupancy calculation, order material and classification field combination, etc., to process the business data into a standard format required for algorithm modeling.

[0135] In one specific embodiment, the data acquisition module obtains order A (requiring 100 units) and order B (requiring 200 units) from the ERP system; it obtains the capacity of equipment R1 (100 hours / day) and equipment R2 (80 hours / day) from the MES system; the last-minute order identification module calculates the remaining load of order A and determines it to be a last-minute order on the 5th day; the flexible capacity control module generates... Hours; The model building and solving module calls the COPT solver to generate an order plan schedule.

[0136] Specifically, the modules can be integrated on the same server or deployed in a distributed manner; the model building and solving module can be deployed in the cloud and call remote solving services through API.

[0137] It should be noted that this device implements the above method as a modular software system. The modules work together to automatically complete the entire process from data acquisition to scheduling solution output, providing enterprises with an end-to-end intelligent scheduling solution.

[0138] The flexible overproduction scheduling device for last-order orders in this embodiment is used to implement the aforementioned flexible overproduction scheduling method for last-order orders. Therefore, the specific implementation of the flexible overproduction scheduling device for last-order orders can be found in the previous embodiment section of the flexible overproduction scheduling method for last-order orders. For example, the data acquisition module 100, the last-order identification module 200, the flexible capacity control module 300, and the model construction and solution module 400 are respectively used to implement steps S101, S102, S103, and S104 in the above flexible overproduction scheduling method for last-order orders. Therefore, its specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0139] Based on the same inventive concept, a specific embodiment of the present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the computer program to implement the steps of the above-described flexible overproduction scheduling method for tail orders.

[0140] Based on the same inventive concept, a specific embodiment of the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described flexible overproduction scheduling method for tail orders.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A flexible overproduction scheduling method for last-minute orders, characterized in that, include: Obtain the business data required for production scheduling, including at least order data, capacity data, end-of-line order judgment coefficient, and capacity flexibility coefficient; The remaining production load of each order in each production cycle is determined based on the order data, and the presence of any orders in the final order production stage in the current cycle is identified based on the final order judgment coefficient. If there are orders in the final production stage, then the flexible overtime hours for the current cycle are generated according to the capacity flexibility coefficient, and the flexible overtime hours are introduced into the preset constraints. Based on the preset objective function and constraints, a mixed integer programming model is constructed, and the solver is called to solve it, generating a scheduling scheme that includes order production quantity, equipment allocation, and last-minute order identification.

2. The flexible overproduction scheduling method for last-order orders according to claim 1, characterized in that, The step of determining the remaining production load of each order in each production cycle based on the order data, and identifying whether there are any orders in the final production stage in the current cycle based on the final order judgment coefficient, includes: Calculate the remaining production load for each order before the start of the current cycle. The remaining production load is the product of the remaining demand quantity of the order and the standard working hours of the product in that order. The remaining production load is compared with the standard capacity limit for the current cycle. If the proportion of the remaining production load to the standard capacity limit is lower than the tail-end judgment coefficient, then the order is determined to be in the tail-end production stage for the current cycle.

3. The flexible overproduction scheduling method for last-minute orders according to claim 1, characterized in that, The generation and use of the flexible overtime work hours shall meet the following conditions: The flexible overtime hours shall not exceed the product of the current cycle standard capacity limit and the capacity flexibility coefficient; The flexible overtime is only allowed to be subject to constraints if there is at least one order in the final production stage in the current cycle.

4. The flexible overproduction scheduling method for last-order orders according to claim 1, characterized in that, The constraints include order demand fulfillment constraints, capacity balance constraints, production continuity constraints, and production changeover constraints, among which: The order demand satisfaction constraint is used to ensure that the total demand for each order must be completed within the planning period; The capacity balance constraint is used to ensure that the total working hours occupied by all order production tasks in each production cycle do not exceed the sum of the standard capacity and flexible overtime working hours of that cycle; The production continuity constraint is used to identify whether there is a production interruption between different production cycles for the same order; The product changeover behavior constraint is used to identify product changeover events that occur in adjacent cycles due to product type switching.

5. The flexible overproduction scheduling method for last-minute orders according to claim 1, characterized in that, The objective function is a multi-objective optimization function, which includes at least one or a combination of the following optimization objectives: Maximize the number of orders delivered on time; Minimize the number of production interruptions; Minimize the number of production change events; Minimize the total amount of flexible overtime work.

6. The flexible overproduction scheduling method for last-minute orders according to claim 1, characterized in that, The solver is a COPT solver or a similar mixed integer programming solver, used to find the optimal scheduling scheme for the objective function while satisfying the constraints.

7. The flexible overproduction scheduling method for last-minute orders according to claim 1, characterized in that, The scheduling scheme shall include at least the following output information: Order number, order production quantity, production cycle, equipment allocation, capacity utilization, last-minute order status, on-time delivery status, and production disruption status.

8. A flexible overproduction scheduling device for tail orders, characterized in that, include: The data acquisition module is used to acquire the business data required for production scheduling. The business data includes at least order data, capacity data, end-of-line judgment coefficient, and capacity flexibility coefficient. The end-of-line order identification module is used to determine the remaining production load of each order in each production cycle based on the order data, and to identify whether there are any orders in the end-of-line production stage in the current cycle based on the end-of-line order judgment coefficient. The flexible capacity control module is used to generate flexible overtime hours for the current cycle based on the capacity flexibility coefficient when there are orders in the tail-end production stage in the current cycle, and to introduce the flexible overtime hours into preset constraints. The model building and solving module is used to build a mixed integer programming model based on a preset objective function and constraints, call the solver to solve the model, and generate a scheduling plan that includes order production quantity, equipment allocation and last-minute order identification.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the flexible overproduction scheduling method for last-order orders as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the flexible overproduction scheduling method for last-order orders as described in any one of claims 1 to 7.

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