Productivity calculation method and system oriented to process multi-resource priority allocation

Abstract

The invention discloses a productivity calculation method and system oriented to process multi-resource priority allocation. The method comprises the following steps: acquiring process route information of a target product, extracting selectable resources corresponding to each process, priorities of the resources and unit capacity occupation parameters, and acquiring capacity calendar information of the resources under a preset time granularity; calculating an allocable capacity upper limit of each resource in each time period based on the capacity calendar and the unit capacity occupation parameter; allocating the requirements of each process according to the resource priorities, preferentially allocating to high-priority resources, and sequentially allocating to secondary-priority resources when the remaining requirements are not met; identifying productivity bottleneck resources based on the preliminary allocation result, and performing reallocation on the bottleneck related allocation until a preset stop condition is reached on the premise of meeting the priority and allocatable upper limit constraints; and outputting a final distribution result. According to the method, a convergent allocation result can be obtained in each resource allocatable upper limit constraint, so that the production satisfaction rate is integrally improved.

Description

Technical Field

[0001] This invention relates to the field of capacity allocation, and in particular to a capacity calculation method, system, computer equipment, and storage medium for prioritizing the allocation of multiple resources for different processes. Background Technology

[0002] In discrete manufacturing production planning and capacity planning, process routes are typically implemented step-by-step to equipment resources. Existing systems mostly rely on static binding of single processes and single resources or manual experience-based allocation. The substitutability of general-purpose equipment lacks structured expression, and resource priorities can only be reflected in the maintenance interface and are difficult to integrate into the computation engine. At the same time, the time-varying constraints of resource capacity calendars (shifts, downtime, overtime, etc.) and unit capacity occupancy parameters lack unified modeling and constraint propagation, making it difficult to match demand allocation with resource capacity at the time granularity. These limitations prevent the system from achieving priority-based restricted allocation and reallocation at the algorithm level. Bottleneck identification often remains at the level of result comparison rather than a computable iterative closed loop, resulting in low production fulfillment rates, distorted resource load, and unstable bottleneck locations. Summary of the Invention

[0003] The purpose of this invention is to propose a capacity calculation method and system for prioritizing the allocation of multiple resources in a process, in order to solve the technical problem of low production fulfillment rate.

[0004] To address the aforementioned technical problems, this invention provides a capacity calculation method for prioritizing multi-resource allocation across processes, comprising the following steps:

[0005] Obtain the process route information of the target product, extract the optional resources corresponding to each process, the priority of each resource and the unit capacity utilization parameters, and obtain the capacity calendar information of each resource at a preset time granularity;

[0006] Based on the capacity calendar and unit capacity occupancy parameters, calculate the upper limit of allocable capacity for each resource in each time period;

[0007] Under the aforementioned upper limit constraint, the requirements of each process are allocated according to resource priority, with priority given to high-priority resources, and when the remaining requirements are not met, they are allocated to the next lower priority resources in turn.

[0008] Based on the preliminary allocation results, bottleneck resources are identified, and under the premise of meeting the priority and allocable upper limit constraints, the bottleneck-related allocations are redistributed until the preset stopping conditions are met.

[0009] The final allocation results are output, including the capacity load of each resource in each time period, the overall production fulfillment rate, and the bottleneck resource identifier.

[0010] To address the aforementioned technical problems, this invention also provides a capacity calculation system for prioritizing the allocation of multiple resources across production processes, comprising:

[0011] The acquisition module is used to acquire the process route information of the target product, extract the optional resources corresponding to each process, the priority and unit capacity occupancy parameters of each resource, and acquire the capacity calendar information of each resource at a preset time granularity.

[0012] The calculation module is used to calculate the upper limit of allocable capacity for each resource in each time period based on the capacity calendar and the unit capacity occupancy parameter.

[0013] The first allocation module is used to allocate the requirements of each process according to the resource priority under the upper limit constraint, prioritizing allocation to high-priority resources, and allocating to the next lower priority resources in turn when the remaining requirements are not met.

[0014] The second allocation module is used to identify bottleneck resources based on the preliminary allocation results, and to redistribute the bottleneck-related allocations until the preset stop conditions are met, provided that priority and allocable upper limit constraints are satisfied.

[0015] The output module is used to output the final allocation results, including the capacity load of each resource in each time period, the overall production fulfillment rate, and the bottleneck resource identifier.

[0016] To address the aforementioned technical problems, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the capacity calculation method for process-oriented multi-resource priority allocation as described above.

[0017] To address the aforementioned technical problems, the present invention also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of the capacity calculation method for process-oriented multi-resource priority allocation as described above.

[0018] The capacity calculation method and system for prioritizing multi-resource allocation in process operations disclosed in this invention have at least the following advantages compared with the prior art:

[0019] 1) Under a unified time granularity, the optional resources, resource priorities, unit capacity occupancy and resource capacity calendar of the process route are integrated into a model, and the convergence result is obtained by priority-driven constrained allocation, which significantly improves the production fulfillment rate and accurately reflects the resource utilization rate.

[0020] 2) Based on the allocation results, the bottleneck identification and constrained reallocation mechanism can alleviate or transfer bottlenecks without exceeding the upper limit of each resource's allocability and priority constraints, making the load more balanced and the results more stable, and reducing the reliance on manual scheduling experience.

[0021] 3) Arbitration and switching suppression strategies for resources of equal priority, as well as allocation constraints based on process divisibility and minimum processing time, reduce frequent line changes and fragmented scheduling, and balance processing continuity and parallel carrying capacity, thereby shortening the completion cycle.

[0022] 4) For calendar events such as downtime and overtime, adopt windowed incremental recalculation, recalculate only within the affected time window and keep the plan unchanged outside the window, reduce computing overhead and plan disturbance, and improve the real-time response capability and executability of the plan.

[0023] 5) Introduce cost models such as resource substitution penalties and switching costs during the redistribution phase to reduce the number of switching operations and quality / efficiency losses while maintaining or improving the satisfaction rate, thereby achieving synergistic optimization of satisfaction rate, cost and solution stability.

[0024] In summary, this invention integrates the optional resources, resource priorities, unit capacity utilization, and resource capacity calendar of the process route into a unified time granularity model. By employing a closed-loop solution of priority allocation, bottleneck identification, and constrained reallocation, it can obtain convergent allocation results within the upper limit constraints of each resource's allocability. This improves the overall production fulfillment rate, accurately characterizes the load of each resource, stably locates bottlenecks, and reduces errors caused by manual intervention and experience-based allocation. Attached Figure Description

[0025] To more clearly illustrate the solutions in this invention, the accompanying drawings used in the description of this invention will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is an exemplary system architecture diagram in which the present invention can be applied;

[0027] Figure 2 This is a flowchart of an embodiment of the capacity calculation method for prioritizing multi-resource allocation in a process according to the present invention;

[0028] Figure 3 This is a schematic diagram of an embodiment of the capacity calculation system for prioritizing multi-resource allocation in a process according to the present invention.

[0029] Figure 4 This is a schematic diagram of a structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0031] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0032] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0033] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptops, and desktop computers, etc.

[0034] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0035] It should be noted that the capacity calculation method for prioritizing multi-resource allocation in process operations provided by this invention is generally executed by terminal equipment, and correspondingly, the capacity calculation system for prioritizing multi-resource allocation in process operations is generally set up in the terminal equipment. It should be understood that... Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0036] like Figure 2 As shown, the capacity calculation method for prioritizing multi-resource allocation in process operations according to the present invention includes the following steps:

[0037] Step S201: Obtain the process route information of the target product, extract the optional resources corresponding to each process, the priority of each resource and the unit capacity occupancy parameters, and obtain the capacity calendar information of each resource at a preset time granularity.

[0038] In this invention, the capacity calculation method for prioritizing multi-resource allocation in process operations is implemented on electronic devices (e.g., Figure 1 The terminal device shown can send or receive data via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future known wireless connection methods.

[0039] In this invention, the process route for the target product is first retrieved. This process route is a technical data structure used in the Manufacturing Execution and Planning (MEP) system to characterize the processing sequence, typically consisting of several operations arranged according to sequential constraints. For each operation, the system simultaneously extracts the associated set of optional resources, the pre-defined priorities for these resources, and the unit capacity utilization parameter. The unit capacity utilization parameter refers to the calibrated quantity of resource capacity required to complete one unit of the operation, such as in "hours per piece" or "work hours per batch"; the priority is used to determine the allocation order when multiple alternative equipment exist. Corresponding to the static master data mentioned above, the system obtains the capacity calendar for each resource at a set time granularity, which can be daily, shift, or hourly. The capacity calendar includes the available resource capacity for each time period, planned downtime, and overtime, among other time-varying boundary conditions.

[0040] Step S202: Based on the capacity calendar and unit capacity occupancy parameters, calculate the upper limit of allocable capacity for each resource in each time period.

[0041] In this invention, after receiving the above input, the system calculates the allocatable capacity limit for each resource in each time period based on the capacity calendar and unit capacity occupancy parameters. This limit can be understood as the theoretically maximum output that the resource can bear within that time period. The calculation method is to divide the available capacity of the resource in a certain time period by the unit occupancy of that process on that resource, and then round or adjust the lower limit according to the minimum batch size or minimum processing time of the process. For example, if a piece of equipment has an available capacity of 8 hours on Monday daytime, and the unit occupancy of a certain process on that equipment is 0.5 hours per piece, then the allocatable limit for that time period is 16 pieces. If the minimum processing time for that process is specified as 1 hour, then the allocatable limit needs to be adjusted according to this rule. Through this calculation, the system transforms the time-varying resource boundary conditions into a clear capacity upper bound for each time period, providing quantitative constraints for subsequent limited allocation.

[0042] Step S203: Under the upper limit constraint, the requirements of each process are allocated according to resource priority, with priority allocated to high-priority resources, and the remaining requirements are allocated to the next lower priority resources in turn.

[0043] In this invention, the system performs priority-based demand allocation under the constraint of an allocatable upper limit. Specifically, under the constraint of the process route sequence, the system iterates through the demand of each process step according to time granularity, prioritizing the allocation of demand to the highest priority resources of that process. When the allocatable upper limit of a resource for a certain time period is exhausted or there are still unmet demands, the system allocates to the next lower priority resources of that process step. At the implementation level, the system maintains a count of unmet remaining demands for each "process-time period" and a remaining allocatable upper limit for each "resource-time period." Each allocation takes the smaller of these two values, updating the remaining quantities on both sides to ensure that the resource upper limit is never exceeded. For example, the process route for product A includes processes S1 and S2, where S1 can be processed on resources R1 and R2, with R1 having a higher priority than R2. If the demand for S1 during Monday's day shift is 20 units, the upper limit for R1 is 16 units, and the upper limit for R2 is 12 units, then the system first allocates 16 units to R1, and then allocates the remaining 4 units to R2, ensuring that the respective upper limits are not exceeded.

[0044] Step S204: Identify bottleneck resources based on the preliminary allocation results, and redistribute the bottleneck-related allocations until the preset stop conditions are met, provided that the priority and allocable upper limit constraints are satisfied.

[0045] In this invention, after obtaining the initial allocation results, the system identifies bottleneck resources based on the load ratio of each resource. The load ratio can be defined as the ratio of the allocated amount to the upper limit of the allocable resources. Resources that are close to 1 or exceed a preset threshold are identified as bottlenecks. After identifying a bottleneck, the system performs reallocation on the allocation related to the bottleneck, under the premise of satisfying priority and upper limit constraints. That is, within the second-priority resources of the same process and adjacent time periods, a portion of the already allocated amount is moved to alleviate the bottleneck. The reallocation follows two types of constraints: first, priority and substitutability constraints, which only seeks a recipient from the set of resources defined as optional resources for the process, and the priority of the recipient is not higher than that of the original resource; second, capacity and time constraints, which do not exceed the upper limit of the recipient resource in the corresponding time period, and can be pushed back to the nearest time period with spare capacity if the process allows. To ensure solution convergence, the system sets stopping conditions, such as the overall production satisfaction rate improvement being less than a threshold, insufficient improvement caused by a single move, or reaching the maximum number of iterations. For example, when S1 experiences a bottleneck during Monday's day shift, the system can move the two demand items on R1 to R2's Monday day or Monday afternoon shift, as long as it does not exceed the upper limit of R2 in the corresponding time period and the process constraints allow it, thereby reducing the load ratio of R1 from 1.00 to 0.88, while increasing the overall satisfaction rate and avoiding new over-limits.

[0046] Step S205: Output the final allocation results, including the capacity load of each resource in each time period, the overall production fulfillment rate, and the bottleneck resource identifier.

[0047] In this invention, after completing the above calculations, the system outputs the final allocation result, which includes the capacity load of each resource in each time period, the overall production fulfillment rate, and the bottleneck resource identifier. The capacity load is used to drive equipment planning and manpower scheduling, and can form a load curve at the time granularity; the production fulfillment rate reflects the proportion of demand met under given constraints, and can be statistically analyzed by product, process, or time interval; the bottleneck resource identifier is used for subsequent planning decisions and continuous optimization, such as adjusting the priority of general resources, arranging temporary overtime, or modifying process substitution relationships. Continuing the previous example, if after redistribution, R1 on Monday's day shift carries 14 units, R2 on Monday's day shift carries 6 units, and R2 on Monday's afternoon shift carries 0 units, then the system outputs the Monday day shift loads as 14 units and 6 units respectively, corresponding to load ratios of 0.88 and 0.50, with the overall fulfillment rate reaching 100%, and the bottleneck identifier is removed from R1. Through the above steps, the system models and propagates constraints on the process's selectable resources and priorities, unit capacity utilization, and capacity calendar on a common time axis. It first obtains an initial priority allocation that meets the upper limit, and then resolves bottlenecks through constrained redistribution. The final output can be directly used for the execution layer's planning results, ensuring the feasibility and stability of the method.

[0048] In summary, this invention integrates the optional resources, resource priorities, unit capacity utilization, and resource capacity calendar of the process route into a unified time granularity model. By employing a closed-loop solution of priority allocation, bottleneck identification, and constrained reallocation, it can obtain convergent allocation results within the upper limit constraints of each resource's allocability. This improves the overall production fulfillment rate, accurately characterizes the load of each resource, stably locates bottlenecks, and reduces errors caused by manual intervention and experience-based allocation.

[0049] In some optional implementations of the present invention, the step of allocating the requirements of each process according to resource priority under the upper limit constraint, prioritizing allocation to high-priority resources, and sequentially allocating to lower-priority resources when remaining requirements are not met, includes:

[0050] The remaining proportion is calculated based on the allocable capacity limit of each resource within the current time period, and the resources are allocated sequentially after being sorted according to the stated proportion.

[0051] When multiple resources with the same remaining percentage exist, priority is given to allocating them to the resource that has not been switched over recently, in order to reduce frequent switching.

[0052] In this invention, the remaining ratio refers to the ratio of the upper limit of the remaining allocable capacity of a resource within a certain time period to the upper limit of the total allocable capacity within that time period, used to measure the current capacity capacity of the resource. In implementation, before entering the allocation cycle, the system calculates the remaining ratio of resources of the same priority within the current time period, and accepts unmet demands for the process in descending order of ratio. When multiple resources have the same remaining ratio, the system prioritizes the resource that did not switch in the most recent time period to maintain processing continuity and reduce hidden losses caused by line or type changes. For example, if resources R2a and R2b, both of priority level two, have remaining ratios of 0.6 and 0.4 respectively in this shift, R2a is allocated first; if both are 0.5, the equipment that already handled the process in the previous time period is selected for allocation, thereby reducing the number of switches and stabilizing the cycle time.

[0053] This invention sorts resources of the same priority according to their remaining proportions and adopts a strategy of prioritizing those that have not been switched over the most recently. This can achieve load balancing without increasing resource constraints and reduce line switching losses and cycle time fluctuations caused by frequent switching, thereby improving capacity utilization and scheduling stability.

[0054] In some optional implementations of the present invention, before the steps of calculating the remaining proportion based on the allocable capacity limit of each resource within the current time period, and allocating resources sequentially according to the proportion, the method further includes:

[0055] Set a separability flag and a minimum processing time for each process;

[0056] When performing allocation, if the divisibility of a certain process is splittable, it is allowed to allocate the requirements of that process within the same time period to multiple resources;

[0057] If it cannot be split, it is only allowed to be continuously allocated to a single resource within a time frame that meets the minimum processing time.

[0058] In this invention, the divisibility flag indicates whether the demand for a process within the same time period can be split in parallel among multiple resources; the minimum processing time limits the shortest occupancy time required for a single continuous processing run, thereby avoiding fragmented scheduling. When divisibility is set to allow splitting, the system can allocate unmet demands for the process to multiple resources in the same time period according to their remaining proportions to shorten completion time; when divisibility is set to non-divisibility, the system only allows the process to be continuously allocated to a single resource under the condition of meeting the minimum processing time, and can extend it to subsequent time periods if necessary to meet the time constraint. For example, if the minimum processing time for a single batch of a process is 1 hour, and resource R1 has 0.5 hours remaining while R2 has 1 hour remaining, then if it is non-divisible, only R2 can be selected; if it is allowed to split, the 0.5-hour task can be allocated to R1 and the rest to R2 within the same time period.

[0059] This invention sets a divisibility flag and a minimum processing time for each process, and limits parallel splitting or continuous occupation in the allocation accordingly. This can avoid the efficiency decline and frequent start-stop caused by fragmented scheduling, and enable multiple resources to be carried out in parallel in scenarios where splitting is allowed to shorten the completion time. Thus, while ensuring process continuity, it can improve the satisfaction rate and execution feasibility.

[0060] In some optional implementations of the present invention, after the steps of identifying bottleneck resources based on the preliminary allocation results and redistributing bottleneck-related allocations until a preset stopping condition is met, the method further includes:

[0061] Check if there are any new or changed downtime or overtime events in the resource capacity calendar;

[0062] When an event is detected, the affected time period is defined as an incremental calculation window. Only within the window are the allocable capacity and capability allocations recalculated for the affected resources and their associated processes, while the existing allocations outside the window remain unchanged.

[0063] In this invention, the system continuously monitors the calendar for new or changed downtime and overtime events, and defines the smallest affected time range as the incremental calculation window. Only within this window are the allocable upper limits recalculated and priority allocations re-executed for the affected resources and their related processes, while maintaining existing allocations outside the window, ensuring result stability and computational efficiency. For example, if a 2-hour maintenance downtime is added to equipment R3 during the Tuesday afternoon shift, the system defines the Tuesday afternoon shift as the window, and only reclaims the allocated quantities for R3-related processes within this shift and reallocates them to alternative resources, without changing the existing arrangements for the Tuesday morning shift and other dates.

[0064] This invention significantly reduces computational overhead and disturbance range by recalculating windowed incremental recalculations triggered by calendar events such as downtime and overtime, recalculating the allocatable upper limit and allocation results only within the affected time window while keeping the scheme outside the window unchanged, thereby improving the controllability and execution stability of the plan results.

[0065] In some optional implementations of the present invention, the steps of identifying bottleneck resources based on preliminary allocation results and reallocating bottleneck-related allocations until a preset stopping condition is reached, under the premise of satisfying priority and allocable upper limit constraints, include:

[0066] Determine whether the load ratio of each resource exceeds the preset bottleneck threshold; if so, mark it as a bottleneck resource.

[0067] The portion of the demand already allocated to this resource will be reallocated to the next-priority resources within the same process.

[0068] Set a maximum number of iteration rounds and a satisfaction rate improvement threshold. When the satisfaction rate improvement is less than the threshold or the maximum number of rounds is reached, the redistribution will terminate.

[0069] In this invention, the load ratio is defined as the ratio of the allocated amount of a resource within a certain time period to its available upper limit. When this ratio reaches or exceeds a preset bottleneck threshold, it is marked as a bottleneck resource and a reallocation process is triggered. Reallocation only occurs within the second-priority resources of the same process and their available time periods, gradually transferring a portion of the allocated amount to alleviate the bottleneck. To avoid infinite backtracking, the system sets a maximum number of iterations and a satisfaction rate improvement threshold. When the overall production satisfaction rate increase brought about by a consecutive reallocation is lower than the threshold or the iteration reaches the upper limit, the reallocation terminates and the current result is fixed. For example, if the threshold is set to 0.9, and the load ratio of resource R1 is 0.95 during the Wednesday day shift, reallocation is triggered, moving 3 units of output to R2, reducing R1 to 0.85. If another move only brings a 0.3% improvement in satisfaction rate, the satisfaction rate threshold triggers the stop condition.

[0070] This invention enables bounded control of the bottleneck mitigation process by triggering redistribution with a load ratio threshold and setting stopping conditions such as a satisfaction rate improvement threshold and an iteration upper limit. This avoids ineffective backtracking and oscillations, and achieves verifiable improvement of the satisfaction rate and convergence of the allocation results while ensuring computational efficiency.

[0071] In some optional implementations of the present invention, the steps of identifying bottleneck resources based on preliminary allocation results and reallocating bottleneck-related allocations until a preset stopping condition is met, under the premise of satisfying priority and allocable upper limit constraints, further include:

[0072] Establish resource substitution penalty matrices and switching cost factors for each resource and process;

[0073] Provided that resource availability conditions are met, resources with lower penalty coefficients and switching costs should be prioritized for reallocation.

[0074] The number of switching operations and the cumulative cost of switching operations are recorded simultaneously to assess the overall allocation stability.

[0075] In this invention, the resource substitution penalty matrix is ​​used to quantify the performance or suitability differences of different resources for a specific process, and the switching cost factor is used to quantify the time loss or tooling replacement cost caused by resource switching. Under the premise of meeting availability and upper limit constraints, the system calculates the comprehensive cost of candidate resources and prioritizes resources with lower penalties and switching costs to undertake the reassignment task. Simultaneously, it records the number of switches and cumulative switching costs to assess allocation stability and execution risk. For example, if a process has a penalty of 1 and a switching cost of 2 in R2, and a penalty of 2 and a switching cost of 0 in R3, then R2 is prioritized when both are available. If multiple switches have occurred in a short period, causing the cumulative switching cost to approach the warning threshold, the system can relax the penalty preference to obtain fewer switches.

[0076] This invention introduces resource substitution penalties and switching costs into the redistribution decision process and prioritizes receiving resources with lower overall costs. This allows for the reduction of line switching frequency and quality performance loss while maintaining or improving the satisfaction rate, thus achieving synergistic optimization among satisfaction rate, cost, and stability.

[0077] In some optional implementations of the present invention, the step of defining the affected time period as an incremental calculation window when an event is detected, recalculating the allocable capacity and capability allocation only for the affected resources and their associated processes within the window, and keeping the existing allocation unchanged outside the window, includes:

[0078] When the new production capacity within the window comes from overtime adjustments, only the process corresponding to the new production capacity will be given incremental bonus points;

[0079] The process of supplementing points follows the initial priority order and does not change the resource allocation results formed before the window's starting point.

[0080] Furthermore, allocation stability constraints are imposed to ensure that newly added allocations do not cause adjustments to the process sequence or rollback of already allocated resources.

[0081] In this invention, when the new capacity within a window originates from overtime adjustments, the system only performs incremental allocation for the resources corresponding to the new capacity and their associated processes. It first fills in the unmet needs within the window according to the predetermined resource priority order, without making any changes to existing allocations outside the window. Furthermore, it applies allocation stability constraints to the already determined process order and resource bindings within the window, prohibiting any resulting reverse rollback or cross-sorting. For example, if two hours of overtime are added to resource R2 on Thursday evening, the system only allocates previously unmet processes S1 and S3 to R2 during Thursday evening, without adjusting the allocations already fixed for Thursday day shifts or Wednesday, nor does it roll back S2 already allocated to R1 to R2, thus ensuring the locality of recalculation and the controllability of execution.

[0082] This invention improves local satisfaction and resource utilization without triggering a chain reaction of adjustments by implementing local incremental compensation for overtime-related new production capacity and imposing maintenance constraints, thereby ensuring the locality and global stability of the solution update.

[0083] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0084] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0085] Further reference Figure 3 As a response to the above Figure 2 The implementation of the method shown in this invention provides an embodiment of a capacity calculation system for priority allocation of multiple resources in a process. This system embodiment is similar to... Figure 2 Corresponding to the method embodiments shown, the system can be specifically applied to various electronic devices.

[0086] like Figure 3 As shown, the capacity calculation system 300 for priority allocation of multiple resources in a process according to the present invention includes: an acquisition module 301, a calculation module 302, a first allocation module 303, a second allocation module 304, and an output module 305. Wherein:

[0087] The acquisition module 301 is used to acquire the process route information of the target product, extract the optional resources corresponding to each process, the priority and unit capacity occupancy parameters of each resource, and acquire the capacity calendar information of each resource at a preset time granularity.

[0088] The calculation module 302 is used to calculate the upper limit of allocable capacity for each resource in each time period based on the capacity calendar and the unit capacity occupancy parameter.

[0089] The first allocation module 303 is used to allocate the requirements of each process according to the resource priority under the upper limit constraint, prioritizing allocation to high priority resources, and allocating to the next lower priority resources in turn when the remaining requirements are not met.

[0090] The second allocation module 304 is used to identify capacity bottleneck resources based on the preliminary allocation results, and to redistribute the bottleneck-related allocations until the preset stop conditions are met, provided that the priority and allocable upper limit constraints are satisfied.

[0091] Output module 305 is used to output the final allocation results, including the capacity load of each resource in each time period, the overall production fulfillment rate, and the bottleneck resource identifier.

[0092] Figure 3 The specific implementation of each module in the system can be found in the foregoing method embodiments, and will not be repeated here. It should also be noted that the module names described in the system embodiments of this invention do not necessarily constitute a limitation on the module itself under certain circumstances.

[0093] To address the aforementioned technical problems, the present invention also provides a computer device. Please refer to the following for details. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device of the present invention.

[0094] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0095] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0096] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this invention, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for a capacity calculation method oriented towards multi-resource priority allocation in processes. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0097] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this invention, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, for example, to execute computer-readable instructions for the capacity calculation method oriented towards multi-resource priority allocation for processes.

[0098] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0099] The computer device provided by this invention integrates the optional resources, resource priorities, unit capacity occupancy, and resource capacity calendar of the process route at a unified time granularity, and adopts a closed-loop solution of priority allocation, bottleneck identification, and constrained reallocation. It can obtain convergent allocation results within the upper limit constraints of each resource allocation limit, thereby improving the overall production fulfillment rate, accurately characterizing the load of each resource, stably locating bottlenecks, and reducing errors caused by manual intervention and experience-based allocation.

[0100] The present invention also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the capacity calculation method for process-oriented multi-resource priority allocation as described above.

[0101] The computer-readable storage medium provided by this invention integrates the optional resources, resource priorities, unit capacity occupancy, and resource capacity calendar of the process route at a unified time granularity, and adopts a closed-loop solution of priority allocation, bottleneck identification, and constrained reallocation. It can obtain convergent allocation results within the upper limit constraints of each resource allocation limit, thereby improving the overall production fulfillment rate, accurately characterizing the load of each resource, stably locating bottlenecks, and reducing errors caused by manual intervention and experience-based allocation.

[0102] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

Claims

1. A capacity calculation method for prioritizing the allocation of multiple resources across production processes, characterized in that, Includes the following steps: Obtain the process route information of the target product, extract the optional resources corresponding to each process, the priority of each resource and the unit capacity utilization parameters, and obtain the capacity calendar information of each resource at a preset time granularity; Based on the capacity calendar and unit capacity occupancy parameters, calculate the upper limit of allocable capacity for each resource in each time period; Under the aforementioned upper limit constraint, the requirements of each process are allocated according to resource priority, with priority given to high-priority resources, and when the remaining requirements are not met, they are allocated to the next lower priority resources in turn. Based on the preliminary allocation results, bottleneck resources are identified, and under the premise of meeting the priority and allocable upper limit constraints, the bottleneck-related allocations are redistributed until the preset stopping conditions are met. The final allocation results are output, including the capacity load of each resource in each time period, the overall production fulfillment rate, and the bottleneck resource identifier.

2. The capacity calculation method for prioritizing multi-resource allocation in process operations according to claim 1, characterized in that, The step of allocating the needs of each process according to resource priority under the upper limit constraint, prioritizing allocation to high-priority resources, and allocating to lower-priority resources in sequence when remaining needs are not met includes: The remaining proportion is calculated based on the allocable capacity limit of each resource within the current time period, and the resources are allocated sequentially after being sorted according to the stated proportion. When multiple resources with the same remaining percentage exist, priority is given to allocating them to the resource that has not been switched over recently, in order to reduce frequent switching.

3. The capacity calculation method for prioritizing multi-resource allocation in process operations according to claim 2, characterized in that, Before the step of calculating the remaining proportion based on the allocable capacity limit of each resource within the current time period, and allocating resources sequentially according to the stated proportion, the method further includes: Set a separability flag and a minimum processing time for each process; When performing allocation, if the divisibility of a certain process is splittable, it is allowed to allocate the requirements of that process within the same time period to multiple resources; If it cannot be split, it is only allowed to be continuously allocated to a single resource within a time frame that meets the minimum processing time.

4. The capacity calculation method for prioritizing multi-resource allocation in process operations according to claim 1, characterized in that, After the step of identifying bottleneck resources based on the preliminary allocation results, and redistributing bottleneck-related allocations until a preset stopping condition is met, the method further includes: Check if there are any new or changed downtime or overtime events in the resource capacity calendar; When an event is detected, the affected time period is defined as an incremental calculation window. Only within the window are the allocable capacity and capability allocations recalculated for the affected resources and their associated processes, while the existing allocations outside the window remain unchanged.

5. The capacity calculation method for priority allocation of multiple resources for each process according to claim 4, characterized in that, The step of identifying bottleneck resources based on the preliminary allocation results, and then reallocating bottleneck-related allocations until a preset stopping condition is met, under the premise of satisfying priority and allocable upper limit constraints, includes: Determine whether the load ratio of each resource exceeds the preset bottleneck threshold; if so, mark it as a bottleneck resource. The portion of the demand already allocated to this resource will be reallocated to the next-priority resources within the same process. Set a maximum number of iteration rounds and a satisfaction rate improvement threshold. When the satisfaction rate improvement is less than the threshold or the maximum number of rounds is reached, the redistribution will terminate.

6. The capacity calculation method for priority allocation of multiple resources for each process according to claim 5, characterized in that, The step of identifying bottleneck resources based on the preliminary allocation results, and redistributing bottleneck-related allocations until a preset stopping condition is met, under the premise of satisfying priority and allocable upper limit constraints, further includes: Establish resource substitution penalty matrices and switching cost factors for each resource and process; Provided that resource availability conditions are met, resources with lower penalty coefficients and switching costs should be prioritized for reallocation. The number of switching operations and the cumulative cost of switching operations are recorded simultaneously to assess the overall allocation stability.

7. The capacity calculation method for priority allocation of multiple resources for each process according to claim 4, characterized in that, The step of defining the affected time period as an incremental calculation window when an event is detected, and recalculating the allocable capacity and capability allocation only for the affected resources and their associated processes within the window, while keeping the existing allocation unchanged outside the window, includes: When the new production capacity within the window comes from overtime adjustments, only the process corresponding to the new production capacity will be given incremental bonus points; The process of supplementing points follows the initial priority order and does not change the resource allocation results formed before the window's starting point. Furthermore, allocation stability constraints are imposed to ensure that newly added allocations do not cause adjustments to the process sequence or rollback of already allocated resources.

8. A capacity calculation system for prioritizing the allocation of multiple resources across production processes, characterized in that, include: The acquisition module is used to acquire the process route information of the target product, extract the optional resources corresponding to each process, the priority and unit capacity occupancy parameters of each resource, and acquire the capacity calendar information of each resource at a preset time granularity. The calculation module is used to calculate the upper limit of allocable capacity for each resource in each time period based on the capacity calendar and the unit capacity occupancy parameter. The first allocation module is used to allocate the requirements of each process according to the resource priority under the upper limit constraint, prioritizing allocation to high-priority resources, and allocating to the next lower priority resources in turn when the remaining requirements are not met. The second allocation module is used to identify bottleneck resources based on the preliminary allocation results, and to redistribute the bottleneck-related allocations until the preset stop conditions are met, provided that priority and allocable upper limit constraints are satisfied. The output module is used to output the final allocation results, including the capacity load of each resource in each time period, the overall production fulfillment rate, and the bottleneck resource identifier.

9. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the capacity calculation method for process-oriented multi-resource priority allocation as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the capacity calculation method for process-oriented multi-resource priority allocation as described in any one of claims 1 to 7.

Images