CN117557232A - Advanced scheduling method based on production plan - Google Patents

Advanced scheduling method based on production plan Download PDF

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Publication number
CN117557232A
CN117557232A CN202311607957.8A CN202311607957A CN117557232A CN 117557232 A CN117557232 A CN 117557232A CN 202311607957 A CN202311607957 A CN 202311607957A CN 117557232 A CN117557232 A CN 117557232A
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production
planning
data
resources
scheduling
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肖义军
何子乔
王凯莅
占任平
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Hangzhou Zhongwang Technology Co ltd
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Hangzhou Zhongwang Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract

The invention discloses an advanced scheduling method based on a production plan, and relates to the technical field of production scheduling. The method comprises the following steps of: determining potential bottleneck resources in the existing resources, and establishing a model for the potential bottleneck resources; acquiring basic data for scheduling production of products, extracting available data from the basic data, and establishing production conditions by decision-making staff; inputting available data into a model, and generating a scheduling plan for a given production condition; approval of the production situation and updating of the production schedule and/or execution. The scheme can reduce production scheduling time, reduce production and marketing coordination meeting time, provide immediate rearrangement function when customers insert orders or order are abnormal, control capacity and materials, transparence complex manufacturing management, and predict future capacity conditions and long-term material purchase.

Description

Advanced scheduling method based on production plan
Technical Field
The invention belongs to the technical field of production scheduling, and particularly relates to an advanced scheduling method based on a production plan.
Background
With the increasing globalization and market competition, modern manufacturing enterprises need more efficient and flexible production planning and scheduling systems to improve production efficiency and product quality. However, production planning and scheduling is a complex problem involving many uncertainties and variables such as equipment failure, human resource limitations, material supply, etc. Therefore, developing an advanced scheduling method based on production planning has important practical significance for solving these problems.
Currently, many production planning and scheduling systems employ static scheduling methods that suffer from significant drawbacks in dealing with dynamic production environments. For example, they cannot respond in real time to sudden events such as equipment failures, market demand changes, etc., nor do they fully consider the limitations and optimizations of production resources. Furthermore, these methods often ignore the coupling relationships between the production processes, resulting in inaccurate or non-executable scheduling results.
Disclosure of Invention
Aiming at the problems that the existing production plan and scheduling system cannot respond to sudden events such as equipment faults, market demand changes and the like in real time, and cannot comprehensively consider the limitation and optimization of production resources, the invention provides a high-level scheduling method based on the production plan. The method can acquire and process the sudden events such as equipment faults, market demand changes and the like in real time, and meanwhile, the limitation and optimization of production resources are considered.
The invention provides the following technical scheme:
an advanced scheduling method based on a production plan, comprising the steps of:
s1: determining potential bottleneck resources in the existing resources, and establishing a model for the potential bottleneck resources;
s2: acquiring basic data for scheduling production of products, extracting available data from the basic data, and establishing production conditions by decision-making staff;
s3: inputting available data into a model, and generating a scheduling plan for a given production condition;
s4: approval of the production situation and updating of the production schedule and/or execution.
Preferably, in the step S1, the model is defined by associated data, and the data is divided into structural data and condition data.
Preferably, the structural data includes: production sites, workpieces, bill of materials, process paths and related operating instructions, production resources, supplier inventory, preparation time matrices and schedules; the condition data includes: initial inventory, readiness of the resources, and a set of orders to be processed within a given time interval.
Preferably, the basic data in step S2 is derived from the ERP system, the primary production plan and the demand plan.
Preferably, in the step S3, the scheduling plan is to split the production sequence into a plurality of nodes for a given production condition, generate a corresponding production node and a corresponding time sequence, allocate bottleneck resources in the production sequence according to the node occupancy rate, and generate the distribution thereof by using a poisson algorithm. .
Preferably, in the step S3, the scheduling plan is generated in one step or is completed through two levels of planning hierarchy, wherein the two levels of planning hierarchy are a first comprehensive production plan and a later detailed scheduling plan.
Preferably, in the two-level planning hierarchy, the comprehensive production plan is analyzed before a detailed scheduling plan is generated, and if problems and infeasibilities occur, the APS issues a warning. These warnings are filtered and passed to the correct organizational units in the supply chain and the planning path is specified by the decision maker to balance production capacity.
Preferably, the updating of the schedule in step S4 includes downloading data in ERP by APS and automatically updating the model when the model structure is unchanged by only the number; the model is manually adjusted by an expert as new stages with certain new features are introduced.
Compared with the prior art, the invention has the following advantages:
(1) The method provides an advanced scheduling method based on a production plan, and the scheme can reduce production scheduling time and production and marketing coordination meeting time.
(2) When encountering unexpected situations, the method can provide an immediate rearrangement function, analyze all order abnormal conditions and prevent order delay in advance.
Drawings
FIG. 1 is a flow chart of a high level scheduling method based on a production plan;
FIG. 2 is a matrix of expiration times and preparation times for a production sequence;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. In addition, the technical solutions of the embodiments of the present invention may be combined with each other, but it is necessary to be based on the fact that those skilled in the art can implement the technical solutions, and when the technical solutions are contradictory or cannot be implemented, the combination of the technical solutions should be considered as not existing, and not falling within the scope of protection claimed by the present invention.
Examples
As shown in fig. 1, an advanced scheduling method based on a production plan includes the steps of:
s1, determining potential bottleneck resources in existing resources, and establishing a model for the potential bottleneck resources;
s2, obtaining basic data for scheduling production of the product, extracting available data from the basic data, and establishing production conditions by decision-making staff;
s3, inputting the available data into a model, and generating a scheduling plan for a given production condition;
s4, approving the production condition, and updating and/or executing the production schedule.
In the present solution, the scope of modeling described in step S1 limits the operations performed on the potential bottleneck resources, since only these resources limit the plant yield. Since the production plan schedule is not intended to control the workshops, some of the details of the workshops may be ignored, such as control points that monitor the current status of the order.
Between two consecutive activities of the model, all flow steps performed on non-bottleneck resources are expressed only as fixed advance phase differences. Here, the advance period difference includes only the processing and transportation time on the aforementioned non-bottleneck resources, since there is no waiting time.
The model may be defined by associated data, which may be divided into structural data and condition-related material.
Specifically, the structural data includes: production sites, workpieces, bill of materials, process paths and associated operating instructions, production resources, supplier inventory, preparation time matrix and factory calendar.
For a large supply chain with workshops distributed in different places, all data is collected to a specific place.
Bill of materials is typically based on a single layer description, i.e., those part numbers where each part number is only connected to the material of its next layer. The complete bill of materials for a given part is easily constructed on a computer by concatenating these single layer representations.
The resource consumption per workpiece can be derived from the process path and the operating specification. The number of work pieces per order and the resource consumption per work piece are necessary to calculate the individual order sequence and schedule. Thus, the material processing path and production operations can be clearly expressed with the production flow model.
The factory calendar indicates breaks in holidays and other resource hours, and additionally includes information as to whether the plant or resource is operating in one, two or three shifts. APS typically provide several typical calendars for selection.
The condition-related data is a function of the current condition of the plant, and includes: initial inventory, readiness of the resources, and a set of orders to be processed within a given time interval. The operation rule data specified by the user includes: batch rules, priority rules, and process path selection.
In the disclosed embodiment, the production plan schedule assumes that all data is deterministic, i.e., that the decision conditions are deterministic. Although this is a desirable assumption, adjustments may be made for some time periods. To deal with uncertainties such as unplanned productivity variations or unexpected resource downtime, the software tool allows monitoring of changes that one assumes to occur in the shop floor and generates an updated expected order completion time. Whether these changes are so large that re-optimization of the schedule is required will be based on the decision maker's discretion. Decision-makers' judgment can be aided by the generation and testing capabilities of virtual production sequences that provide a large number of alternative conditions prior to implementation of a planned actual delivery plant.
When the node occupancy rate among all production nodes is obtained, the virtual production sequence provided by the embodiment of the disclosure allocates the flow according to the node occupancy rate, and generates a table index according to a first poisson algorithm, wherein the table index is the distribution index of all bottleneck resources in the written production sequence in all production nodes.
The first poisson algorithm in the embodiment of the disclosure changes the node allocation rate in a preset time period of the system, obtains the ratio between the node allocation rate at each moment and the unit time of occurrence, obtains the second unit time allocation rate in each node in the unit time, obtains the total second unit time allocation rate of all nodes, obtains the first poisson rate and the second poisson rate about the node allocation rate change, wherein the first poisson rate is poisson distribution of the allocation rate change of the same node in the preset time period, and the second poisson rate is poisson distribution of bottleneck resources entering the production sequence in the preset time period and distributed among all production nodes.
The first poisson rate and the second poisson rate are then written into the table index of this distribution, in particular the second poisson rate provided by the node included in its feedback value every time the bottleneck resource enters the production sequence, and the first poisson rate provided within the group, i.e. the format value of the table index is the first poisson rate # group-node index code # second poisson rate, so the table index is in fact an array of format values provided by a number of embodiments of the present disclosure. According to the table index, since the poisson ratio in the preset time period has a unique type, the corresponding resource amount in the node can be accurately retrieved.
Furthermore, in the embodiment of the present disclosure, the table index is always stored in the scheduling node, and after the resource amount is modified, both the first poisson rate and the second poisson rate may change to a certain extent, so that the requester cannot directly read the data through the table index.
The poisson ratio has the characteristic of being generally similar, and a fuzzy addressing effect is provided when a sequence is searched, so that in the embodiment of the disclosure, a time sequence when writing is also provided, therefore, the production node indexes the sequence together according to the table index and the time sequence, the first poisson ratio and the second poisson ratio set a threshold value, and the first poisson ratio and the second poisson ratio within the threshold value range can be addressed to the same virtual sequence. The embodiments of the present disclosure further illustrate that setting of the threshold does not interfere with accurate addressing, as the first poisson rate and the second poisson rate are resource distributions provided when writing to the same sequence in a unit time period, a newly written file may be written by a production node to a node that occupies less time in the unit time period, and thus its overall walk may only change within the threshold.
The table index provides the specific distribution of the modified resource to the requester, the predicted distribution of the resource can be obtained according to the table index obtained by the modified resource, the random distribution and the two sequence predictions according to the distribution rate are provided to the requester, the requester does not directly read the corresponding data in the production nodes, but basically controls the random distribution and the prediction conditions of the distribution according to the distribution rate, and the occupation of the system to each node can be managed more accurately.
The scheduling node then sends a virtual sequence to the source of the write request, and then obtains the approximate distribution of the resource and the subsequent probability distribution from the table index.
The other is an incremental program. Assume that a new order arrives. If it falls within the planning scope of the production planning schedule, the activity of this new customer order can be inserted into the ordered order on the resources it needs. The time slots are found in the current scheduling plan so that the scheduling of new orders only needs to be slightly adjusted. If the feasibility of the scheduling plan can be maintained, a planned delivery period for the new order can be derived and returned to the customer.
Since this basic scheduling above can be improved by a different order sequence, re-optimization is often considered to reduce costs by new ordering.
As shown in fig. 2, given that there are 4 orders to be scheduled on a machine, table 1 gives order delivery times, the objective of optimization is to minimize the sum of the sequentially related production preparation times. If the actual start time is 100 (time units), the processing time is the same for all orders (1 time unit), and the sequentially related preparation time is 0,1/3,2/3 or 1 time unit (the preparation time matrix is given in Table 2). Then the optimal schedule is obviously ABCD.
After the start of processing order a, we are required to check whether a new order E with delivery time 107 can be received. Assuming that it is not allowed to interrupt an order that has already been performed because of a new (urgent) order, we can check that work E is inserted directly into the current schedule after order a, B, C or D is completed. Since there is a positive preparation time between the sub-orders of order a and E, the lead time of order B would be violated after insertion of a and is therefore not feasible. Three possible schedules can be found in principle, with option c) having the smallest sum of the preparation times. Thus, a new order E of delivery period 107 may be accepted (assuming an order E value of one time unit of additional production preparation time).
When re-performing scheduling optimization we can get a new viable scheduling scheme including order E, which reduces the production preparation time by 1/3.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (8)

1. An advanced scheduling method based on a production plan, comprising the steps of:
s1: determining potential bottleneck resources in the existing resources, and establishing a model for the potential bottleneck resources;
s2: acquiring basic data for scheduling production of products, extracting available data from the basic data, and establishing production conditions by decision-making staff;
s3: inputting available data into a model, and generating a scheduling plan for a given production condition;
s4: approval of the production situation and updating of the production schedule and/or execution.
2. The advanced scheduling method based on production planning according to claim 1, wherein in step S1, the model is defined by associated data, which is divided into structural data and condition data.
3. An advanced scheduling method based on production planning according to claim 2, characterized in that the structural data comprises: production sites, workpieces, bill of materials, process paths and related operating instructions, production resources, supplier inventory, preparation time matrices and schedules; the condition data includes: initial inventory, readiness of the resources, and a set of orders to be processed within a given time interval.
4. The advanced scheduling method based on production planning according to claim 1, wherein the basic data in step S2 is derived from ERP system, main production planning and demand planning.
5. The advanced scheduling method according to claim 1, wherein in the step S3, the scheduling plan is for a given production situation, the production sequence is split into a plurality of nodes, corresponding production nodes and corresponding time sequences are generated, bottleneck resources in the production sequence are allocated according to node occupancy, and the distribution is generated by poisson algorithm.
6. The advanced scheduling method based on production planning according to claim 5, wherein in the step S3, the production planning is generated in one step or is completed by two levels of planning hierarchy, namely, a first comprehensive production planning and a later detailed production planning.
7. The advanced scheduling method according to claim 6, wherein the two-level planning hierarchy analyzes the integrated production plan before generating a detailed production planning, and the APS gives a warning if there is a problem or impossibility. These warnings are filtered and passed to the correct organizational units in the supply chain and the planning path is specified by the decision maker to balance production capacity.
8. The advanced scheduling method based on production schedule according to claim 1, wherein the updating of the production schedule in step S4 includes downloading data in ERP by APS and automatically updating the model when the model structure is unchanged by only the number; the model is manually adjusted by an expert as new stages with certain new features are introduced.
CN202311607957.8A 2023-11-29 2023-11-29 Advanced scheduling method based on production plan Pending CN117557232A (en)

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