CN117494973A - Method, device, storage medium and processor for determining scheduling scheme - Google Patents
Method, device, storage medium and processor for determining scheduling scheme Download PDFInfo
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Abstract
The embodiment of the application provides a method, a device, a storage medium and a processor for determining a production scheduling scheme. The method comprises the following steps: determining target sales data of a target product at a target time point; determining demand fluctuation data and supply fluctuation data of a target product in a product delivery period corresponding to a target time point; determining a safety stock of the target product at a target time point according to the demand fluctuation data and the supply fluctuation data; predicting yield requirements of the target product at a target time point according to the target sales data, the safety stock and the plurality of influencing parameters; determining a predicted supply of the target product; and determining a scheduling scheme corresponding to the target time point according to the production and marketing requirements and the predicted supply quantity. Analysis is performed by various influencing elements and various interference information in the business process is considered. And the business influence is eliminated, the relationship between sales and production is coordinated, and the accuracy and timeliness of the scheduling scheme are ensured.
Description
Technical Field
The present application relates to the field of production planning technology, and in particular, to a method, an apparatus, a storage medium, and a processor for determining a scheduling scheme.
Background
For large-scale enterprises producing goods, the produced products have a plurality of types, the market demand changes rapidly, and the following production modes mainly exist. The first is to produce on a per order basis, and to design and manufacture the product required by the customer according to the customer's order. The second is to assemble on a single basis, where customers place requirements on certain configurations of parts or products, and where businesses offer products tailored to customers according to the customer's requirements. The third is to make a decision as to whether or not to schedule production as the market demands and with reference to the inventory itself. However, the demand fluctuation according to single production is relatively large, the customer exchange period is not satisfied, the inventory available according to single assembly is insufficient, the supply is not up, and the inventory production easily causes stock backlog and capital occupation. Therefore, all the three production modes cannot coordinate and match production and demand, and cannot accurately guide production plans and purchasing.
Disclosure of Invention
The embodiment of the application aims to provide a method, a device, a storage medium and a processor for determining a production scheduling scheme, which are used for solving the problems that production modes in the prior art cannot be matched with production and requirements in a coordinated manner and cannot accurately guide production plans and purchasing.
To achieve the above object, a first aspect of the present application provides a method for determining a scheduling scheme, comprising:
determining target sales data of a target product at a target time point;
determining demand fluctuation data and supply fluctuation data of a target product in a product delivery period corresponding to a target time point;
determining a safety stock of the target product at a target time point according to the demand fluctuation data and the supply fluctuation data;
predicting yield requirements of the target product at a target time point according to the target sales data, the safety stock and the plurality of influencing parameters;
determining a predicted supply of the target product;
and determining a scheduling scheme corresponding to the target time point according to the production and marketing requirements and the predicted supply quantity.
In an embodiment of the present application, determining a safety stock of a target product at a target point in time from demand fluctuation data and supply fluctuation data includes: acquiring an expected service level of a target product; the safety stock is determined based on the demand fluctuation data, the supply fluctuation data, and the desired service level.
In an embodiment of the present application, the influencing parameters include a non-stock inventory, strategic placement, and development of a new machine plan, and predicting the yield requirement of the target product at the target point in time based on the target sales data, the safety stock, and the plurality of influencing parameters includes calculating the yield requirement according to the following formula (1):
D＝a 1 O+a 2 X+a 3 C+a 4 S-a 5 F+a 6 P (1)
Wherein D refers to the yield requirement of the target product at the target time point, O refers to the ascertained business machine of the target product at the target time point, X refers to the target sales data of the target product at the target time point, C refers to the non-standard inventory of the target product, S refers to the strategic placement of the target product, F refers to the safe inventory of the target product, P refers to the development new machine plan of the target product, a 1 Refers to the business machine realization coefficient, a 2 Refers to a duration prediction distribution coefficient, a 3 Is a specified system demand coefficient, a 4 Refers to strategic goods laying coefficients, a 5 Refers to the safety stock proportion, a 6 Refers to the innovation investment coefficient.
In an embodiment of the present application, determining target sales data for a target product at a target point in time includes: acquiring a plurality of historical sales data of a target product; and after the plurality of historical sales data are subjected to differential processing, inputting the processed plurality of historical sales data into a preset regression model so as to output target sales data through the preset regression model.
In an embodiment of the present application, inputting the processed plurality of historical sales data into a preset regression model, wherein outputting the target sales data through the preset regression model includes a functional expression of the preset regression model as shown in formula (2):
X t ＝α 1 X t-1 +α 2 X t-2 +…+α p X t-p +ε t +β 1 ε t-1 +…+β q ε t-q (2)
Wherein X is t Refers to target sales data of target products at a target time point t, X t-1 、X t-2 …X t-p Respectively refers to historical sales data epsilon corresponding to the target product at the historical time points t-1 and t-2 … t-p t 、ε t-1 …ε t-q All refer to the white noise of the preset regression model, p refers to the quantity of historical sales data, q refers to the quantity of white noise, alpha 1 …α q Refers to autoregressive coefficients describing the relationship between the current observations and the top p historical sales data, beta 1 …β q Refers to a moving average coefficient describing the relationship between the current observation and the first q noise terms.
In an embodiment of the present application, determining the predicted supply of the target product includes: determining a supply chain of the target product, wherein the supply chain comprises a plurality of supply nodes and demand nodes corresponding to each supply node; for each supply node, determining an in-transit inventory currently being transported from the supply node to the corresponding demand node; determining the current stock sum of the target product according to the in-transit stock of each supply node; acquiring the upper limit of the capacity of a target product and the planned capacity; and determining the predicted supply quantity of the target product according to the upper limit of the capacity, the planned capacity and the current stock sum.
In an embodiment of the present application, determining a scheduling scheme corresponding to a target time point according to a yield requirement and a predicted supply amount includes: determining a production task set for producing the target product according to the production and marketing requirements and the predicted supply quantity; initializing a production task set to obtain an initial production task set; sequencing the production task set according to order sequencing rules to determine target production; determining target equipment according to the priority of the optional equipment produced by the target; distributing the target production to the target equipment and updating the load capacity of the target equipment; under the condition that the load capacity updating of the target equipment is completed, moving the target production from the production task set to the initial production task set; judging whether all the pre-production of the preset production in the production task set is in the initial production task set; moving the preset production from the production task set to the initial production task set under the condition that all the pre-production of the preset production in the production task set is judged to be in the initial production task set; judging whether the production task set is an empty set or not; and ending the scheduling algorithm based on the heuristic rule under the condition that the production task set is an empty set so as to obtain the production scheduling scheme.
A second aspect of the present application provides a processor configured to perform the above-described method for determining a scheduling scheme.
A third aspect of the present application provides an apparatus for determining a scheduling scheme, comprising:
a memory configured to store instructions; and
such as the processor described above.
A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which when executed by a processor, cause the processor to be configured to perform the above-described method for determining a scheduling scheme.
Through the technical scheme, analysis is performed through various influencing factors, and various interference information in the business process is considered. The business influence is eliminated, the relationship between sales and production is coordinated, the accuracy and timeliness of the scheduling scheme are ensured, and the change of factors such as sales plans, inventory and the like can be flexibly dealt with.
Additional features and advantages of embodiments of the present application will be set forth in the detailed description that follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the present application and are incorporated in and constitute a part of this specification, illustrate embodiments of the present application and together with the description serve to explain, without limitation, the embodiments of the present application. In the drawings:
FIG. 1 schematically illustrates a flow diagram of a method for determining a scheduling scheme according to an embodiment of the present application;
FIG. 2 schematically illustrates a flow chart of a method for determining product supply according to an embodiment of the present application;
FIG. 3 schematically illustrates a block diagram of an apparatus for determining a scheduling scheme in accordance with an embodiment of the present application;
FIG. 4 schematically illustrates a flow chart of a production and marketing planning algorithm according to an embodiment of the present application;
fig. 5 schematically shows an internal structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the specific implementations described herein are only for illustrating and explaining the embodiments of the present application, and are not intended to limit the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
It should be noted that, in the embodiment of the present application, directional indications (such as up, down, left, right, front, and rear … …) are referred to, and the directional indications are merely used to explain the relative positional relationship, movement conditions, and the like between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the protection scope of the present application.
Fig. 1 schematically shows a flow diagram of a method for determining a scheduling scheme according to an embodiment of the present application. As shown in fig. 1, in an embodiment of the present application, there is provided a method for determining a scheduling scheme, including the steps of:
S102, determining target sales data of a target product at a target time point.
S104, determining the demand fluctuation data and the supply fluctuation data of the target product in the product delivery period corresponding to the target time point.
S106, determining the safety stock of the target product at the target time point according to the demand fluctuation data and the supply fluctuation data.
S108, predicting the yield requirement of the target product at the target time point according to the target sales data, the safety stock and the plurality of influencing parameters.
S110, determining the predicted supply quantity of the target product.
And S112, determining a scheduling scheme corresponding to the target time point according to the production and marketing requirements and the predicted supply quantity.
In the embodiments of the present application, the target product refers to a product manufactured in batch by a large enterprise. For example, in the vehicle enterprise manufacturing industry, the product models are many, the market demand changes rapidly, and the production guidance of production supply needs to consider various influences. The processor may determine target sales data for the target product at the target point in time. The target time point refers to a time point selected by a technician, and may specifically refer to a future time point. The target sales data includes sales and sales amount of the product. Specifically, the target sales data can be obtained through data analysis obtained by a marketing business platform (MBP-Marketing Business Platform).
Further, the processor may determine demand fluctuation data and supply fluctuation data for the target product within a product lead time corresponding to the target point in time. Within a product lead time, there may be certain demand and supply fluctuations for the target product. The demand fluctuation data refers to a fluctuation in demand quantity of a certain commodity due to a fluctuation in other factors under the condition that the price of the certain commodity is unchanged. Other factor variations herein refer to variations in consumer revenue levels, price variations for related goods, variations in consumer preferences, and variations in consumer price expectations for goods, among others. Supply fluctuation refers to supply fluctuation caused by fluctuation of other factors (such as technical improvement, reduction of price of product production elements, reduction of cost due to fluctuation of other factors, etc.) when the price of the commodity itself is unchanged, and the supply fluctuation is represented as fluctuation of the whole supply curve. The processor may determine a safe inventory of the target product at the target point in time based on the demand fluctuation data and the supply fluctuation data, preventing future supply or demand uncertainty factors (e.g., volume sudden orders, unexpected breaks or sudden delays in delivery, etc.). Specifically, the supply fluctuation data and the demand play data can be acquired by a material management platform (SAPMM-System Application and Products Material management), and the acquired data is analyzed to obtain a safety stock.
Further, the processor may predict a yield requirement of the target product at the target point in time based on the target sales data, the safety stock, and the plurality of influencing parameters. The influencing parameters refer to the relevant parameters that influence the yield requirements. The processor may then also determine a predicted supply of the target product at the target point in time. The forecast supply quantity is the production quantity of the target product estimated by the pointer. The processor may then formulate a scheduling for the target product for the target point in time based on the sales demand and the forecast supply. Specifically, the production and sales demand and the predicted supply can be analyzed by an advanced planning and scheduling system (APS-Advanced Planning and Schedulingm) to obtain a scheduling scheme. Through the technical scheme, the analysis is performed through various influencing elements, various interference information in the business process is considered, business influence is eliminated, the relationship between sales and production is coordinated, and the accuracy and timeliness of the production scheduling scheme are ensured. Therefore, the disadvantages are avoided, the delivery period is shortened, the inventory stock quantity is reduced, and the change of factors such as sales plans, inventory and the like can be flexibly dealt with.
In one embodiment, determining target sales data for a target product at a target point in time includes: acquiring a plurality of historical sales data of a target product; and after the plurality of historical sales data are subjected to differential processing, inputting the processed plurality of historical sales data into a preset regression model so as to output target sales data through the preset regression model.
The processor may obtain a plurality of historical sales data for the target product. Specifically, sales data corresponding to a plurality of consecutive time periods may be provided. After the plurality of historical sales data are subjected to differential processing, the processor can input the processed plurality of historical sales data into a preset regression model so as to output target sales data through the preset regression model. Specifically, the preset regression model may be an autoregressive integrated moving average model formed by combining an autoregressive model and a moving average model. The d-order difference smoothing processing can be carried out on the historical sales data corresponding to the continuous time periods, and the target sales data is predicted through the autoregressive comprehensive moving average model.
Specifically, the processor may input the processed plurality of historical sales data into a preset regression model, wherein the outputting the target sales data through the preset regression model includes a functional expression of the preset regression model as shown in formula (2):
X t ＝α 1 X t-1 +α 2 X t-2 +…+α p X t-p +ε t +β 1 ε t-1 +…+β q ε t-q (2)
wherein X is t Refers to target sales data of target products at a target time point t, X t-1 、X t-2 …X t-p Respectively, the target products are in historyHistorical sales data corresponding to the points t-1, t-2 … t-p, ε t 、ε t-1 …ε t-q All refer to the white noise of the preset regression model, p refers to the quantity of historical sales data, q refers to the quantity of white noise, alpha 1 …α q Refers to autoregressive coefficients that describe the relationship between the current observations and the top p historical sales data. Beta 1 …β q Refers to a moving average coefficient describing the relationship between the current observation and the first q noise terms.
White noise is a random signal that is characterized by a uniform distribution of energy over all frequencies, similar to a purely random signal. In time series analysis, white noise is used as a model to model the randomness and unpredictability that is not captured, and as an error term to measure the prediction error of the model. In the autoregressive integrated moving average model, it is assumed that the error term (white noise term) is white noise subject to a mean of 0 and a variance of constant. One of the goals of the model is to minimize the errors between the actual observations and the model predictions by adjusting the coefficients of the autoregressions and moving averages, where these errors are considered to be caused by white noise.
In one embodiment, determining a safety stock for a target product at a target point in time based on demand fluctuation data and supply fluctuation data comprises: acquiring an expected service level of a target product; the safety stock is determined based on the demand fluctuation data, the supply fluctuation data, and the desired service level.
Specifically, the safety stock can be calculated according to the following formula (3):
wherein Z is the quantile of a standard normal distribution corresponding to the desired service level, and the common values include 1.64 (90% service level), 1.96 (95% service level), 2.33 (99% service level), etc., σ d 2 Refers to the variance, σs, of the demand fluctuation data 2 Refers to the variance of the supply fluctuation data, LT refers to the time of supply delay, i.e. from order placement to actualTime of receipt of the goods.
In embodiments of the present application, the influencing parameters include non-stock inventory, strategic placement, and developing new machine plans. Information such as non-stock inventory, strategic placement, etc. may be obtained from the marketing business platform. The development of new machine plans may be through product lifecycle management (PLM-Product Lifecycle Management) tiger hills. The processor may predict a yield requirement of the target product at the target point in time based on the target sales data, the safety stock, and the plurality of influencing parameters including the yield requirement calculated according to the following equation (1):
D＝a 1 O+a 2 X+a 3 C+a 4 S-a 5 F+a 6 P (1)
wherein D refers to the yield requirement of the target product at the target time point, O refers to the ascertained business machine of the target product at the target time point, X refers to the target sales data of the target product at the target time point, C refers to the non-standard inventory of the target product, S refers to the strategic placement of the target product, F refers to the safe inventory of the target product, P refers to the development new machine plan of the target product, a 1 Refers to the business machine realization coefficient, a 2 Refers to a duration prediction distribution coefficient, a 3 Is a specified system demand coefficient, a 4 Refers to strategic goods laying coefficients, a 5 Refers to the safety stock proportion, a 6 Refers to the innovation investment coefficient. The technician can adjust the calculated recommended output requirement according to the actual situation. Such as safety stock proportions, strategic placement factors, etc. The conservative or general market forecast distribution coefficients and service levels are selected according to the fluctuation degree of the market and the company policy.
Fig. 2 schematically shows a flow diagram of a method for determining product supply according to an embodiment of the present application. As shown in fig. 2, in one embodiment of the present application, a method for providing a product supply is provided, comprising the steps of:
s202, determining a supply chain of the target product, wherein the supply chain comprises a plurality of supply nodes and demand nodes corresponding to each supply node.
S204, for each supply node, determining an in-transit inventory currently transported from the supply node to the corresponding demand node.
S206, determining the current stock sum of the target products according to the in-transit stock of each supply node.
S208, obtaining the upper limit of the productivity of the target product and the planned productivity.
S210, determining the predicted supply quantity of the target product according to the upper limit of the capacity, the planned capacity and the current stock sum.
The supply chain refers to the network structure formed by the enterprises upstream and downstream in the production and distribution process that involve providing the product or service to the end user activities, i.e., the entire chain of products from the merchant to the consumer. The supply chain includes a plurality of supply nodes, and a demand node corresponding to each supply node. For each supply node, the processor may determine an in-transit inventory currently being transported from the supply node to the corresponding demand node. In-transit inventory refers to inventory that is reserved in a transport that has not arrived at a destination, is in a state of being transported, or is waiting for transportation. Such as materials on transportation lines of aviation, railways, highways, pipelines, etc., work in progress on assembly lines, etc. The processor may then determine a current inventory sum for the target product based on the in-transit inventory of each supply node, in combination with the inventory of the supply node and the demand node. The current inventory sum refers to the total inventory of the target product throughout the supply chain. Further, the processor may obtain an upper capacity limit and a planned capacity of the target product. The upper capacity limit refers to the maximum capacity that the product can produce based on the production scale and the equipment number limit of the enterprise. The upper capacity limit may be obtained from a manufacturing execution system (MES-Manufacturing Execution System). The planned capacity refers to the number of product productions planned based on annual production in the annual outline of the enterprise. The projected capacity may be obtained from an advanced planning and scheduling system. The processor may input to the advanced planning and scheduling system based on the upper capacity limit, the planned capacity and the current inventory sum to analyze the predicted supply of the target product. According to the scheme, the sum of the current inventory is obtained through analysis of the supply chain, and the predicted supply quantity of the target product is predicted by combining the influence of the upper limit of the capacity, the planned capacity and the sum of the current inventory. The analysis is carried out through the factors of various influences, various interference information in the business process is considered, the business influence is eliminated, and a relatively accurate predicted goods supply result is obtained.
In one embodiment, for each supply node, determining the in-transit inventory currently being transported from the supply node to the corresponding demand node comprises: for each supply node, determining a maximum traffic volume and a shortest path currently being transported from the supply node to the corresponding demand node; for each supply node, determining the average transportation time length of the current transportation from the supply node to the corresponding demand node according to the shortest path; for each supply node, an in-transit inventory is determined from the maximum traffic volume and the average length of transportation.
In particular, the processor may build a supply chain network model including nodes (supply nodes and demand nodes) and wiring (transport paths) between them. Analyzing the maximum capacity of the product transported per transport path, for each supply node, the processor may calculate the maximum traffic transported to the corresponding demand node using a maximum flow algorithm, finding the shortest path from each supply node to the corresponding demand node by a shortest path algorithm (such as Dijkstra's algorithm or Floyd-Warshall's algorithm). For each supply node, the processor may determine an average length of transportation currently being transported from the supply node to the corresponding demand node based on the shortest path. The average transport duration refers to the time required for average transport of the product from the supply node to the demand node. The processor may then determine the in-transit inventory based on the maximum traffic volume and the average traffic duration. Specifically, in-transit inventory=average transportation time length×maximum transportation volume.
In one embodiment, determining the predicted supply of the target product based on the upper capacity limit, the projected capacity, and the current inventory sum includes: acquiring related heavy part resources corresponding to a target product; and determining the predicted supply quantity of the target product according to the target product, the upper limit of the capacity, the planned capacity and the current stock sum.
The weight-related resource refers to the fact that the weight-related resource is a key and a collective term for the weight-related resource. The heavyweight resources may be key and critical elements that make up the target product. When the related parts resources change, the productivity of the target product is directly affected. The processor may then analyze the target product, the upper capacity limit, the projected capacity, and the current inventory sum in the advanced planning and scheduling system to obtain a predicted supply of the target product.
Specifically, in one embodiment, determining the predicted supply of the target product based on the target product, the upper limit on capacity, the projected capacity, and the current inventory sum includes calculating the predicted supply according to the following equation (4):
A＝θ 1 I+θ 2 R+θ 3 L+θ 4 Y (4)
wherein A refers to the predicted supply amount of the target product, I refers to the current stock sum of the target product, R refers to the related weight resource corresponding to the target product, L refers to the upper limit of the capacity for producing the target product, Y refers to the planned capacity of the target product, and θ 1 Refers to the supply of elastic factors, theta 2 Refers to the resource scarcity factor, theta 3 Refers to the productivity factor, theta 4 Refers to strategic planning factors.
In one embodiment, determining the predicted supply of the target product based on the upper capacity limit, the projected capacity, and the current inventory sum includes: determining the resource utilization rate of related heavy part resources corresponding to the target product; and determining the predicted supply quantity of the target product according to the resource utilization rate, the upper limit of the capacity, the planned capacity and the current stock sum.
For predicting the supply of goods, the resource utilization of the related heavy part resource may also be considered. Specifically, in one embodiment, determining the predicted supply of the target product based on the resource utilization, the upper limit of capacity, the planned capacity, and the current inventory sum includes calculating the predicted supply according to the following equation (5):
A＝I+min(U×L，U×Y) (5)
wherein A refers to the predicted supply amount of the target product, I refers to the current stock sum of the target product, U refers to the resource utilization rate of the related piece resource corresponding to the target product, and Y refers to the planned capacity of the target product. The resource utilization ranges from 0 to 1, where 0 indicates unused and 1 indicates fully utilized. The calculation method is based on the utilization ratio of the key weight resource multiplied by the capacity limit, and represents the theoretical capacity under the key resource limit. Taking smaller values of available resources and theoretical capacity to ensure that the limits of the resources are not exceeded, the end result is within annual production supply capacity.
In one embodiment, the method further comprises: after determining the current inventory sum, acquiring a supply influence factor of the target product, wherein the supply influence factor comprises a resource factor, a resource productivity and a time factor; determining a predicted supply amount of the target product according to the current inventory sum supply influence factor, wherein the predicted supply amount is calculated according to the following formula (6):
A＝I×(1-RF)+RC×RF×TF (6)
wherein A refers to the predicted supply of the target product, I refers to the current stock sum of the target product, RC refers to the resource factor, RF refers to the resource capacity, and TF refers to the time factor. The resource factor RC considers the comprehensive impact factors of the related resources, human resources, etc., and may range from 0 to 1, where 0 indicates no impact and 1 indicates the greatest impact. The resource capacity RF is calculated according to the related key resource, the human resource, and the like. The time factor TF takes into account the time impact factor of order urgency, production period, etc., which may range from 0 to 1, where 0 means no impact and 1 means time is most urgent. The calculation method takes available inventory and in-transit inventory as existing available resources, takes resource factors as the reverse influence of the resources, and considers the comprehensive influence of the resource productivity, the resource factors and the time factors to obtain the actual productivity under the limitation of the resources and the time. The final result integrates the existing resources and productivity and is regulated by the resource factors and the time factors.
In one embodiment, determining a scheduling scheme corresponding to a target point in time based on a production demand and a predicted supply includes: determining a production task set for producing the target product according to the production and marketing requirements and the predicted supply quantity; initializing a production task set to obtain an initial production task set; sequencing the production task set according to order sequencing rules to determine target production; determining target equipment according to the priority of the optional equipment produced by the target; distributing the target production to the target equipment and updating the load capacity of the target equipment; under the condition that the load capacity updating of the target equipment is completed, moving the target production from the production task set to the initial production task set; judging whether all the pre-production of the preset production in the production task set is in the initial production task set; moving the preset production from the production task set to the initial production task set under the condition that all the pre-production of the preset production in the production task set is judged to be in the initial production task set; judging whether the production task set is an empty set or not; and ending the scheduling algorithm based on the heuristic rule under the condition that the production task set is an empty set so as to obtain the production scheduling scheme.
Specifically, the scheduling algorithm may include a heuristic rule-based scheduling algorithm. Heuristic algorithms may refer to an intuitively or empirically constructed algorithm that gives a viable solution to each instance of the combinatorial optimization problem to be solved at acceptable costs (e.g., computational time and space costs) that generally cannot be predicted from the optimal solution. When a scheduling algorithm based on heuristic rules is performed, a process task set O is firstly obtained task ，O task The first pass of the critical process path and the non-critical process path of all orders may be included. For process task set O task Performing initialization processing to obtain an initial procedure task set O finish Initial process task set O finish Is an empty set. Integrating the working procedure tasks into a set O task Ordering according to order ordering rules to determine target procedure O ij Wherein, when sorting, the critical process path process is prioritized, and the target process is also the optimal process. Then according to the target procedure O ij Optional device priority determination target device M of (1) m And target process O ij Assigned to target device M m And updates the load capacity of the target device. In the case where the load capacity update of the target equipment is completed, the target process O ij From process task set O task Move to initial procedure task set O finish The method comprises the steps of carrying out a first treatment on the surface of the Judging the working procedure task set O task Preset process O in (3) ij+1 Whether all the preceding processes of (1) are in the initial process task set O finish Is a kind of medium. In the judging process task set O task Preset process O in (3) ij+1 All the pre-processes of (a) are in the initial process task set O finish In the case of (3), the step (O) is preset ij+1 From process task set O task Move to initial procedure task set O finish . Judging the working procedure task set O task Whether it is an empty set; in the working procedure task set O task In the case of empty sets, the heuristic-based scheduling algorithm is ended to obtain a scheduling scheme.
Specifically, in the determination of the preset step O ii+1 Not all of the pre-process tasks in the initial process task set O finish In the case of (3), description is made in the preset step O ij+1 There were previous processes that were not scheduled. Then the process task is assembled into O task Re-ordering according to order ordering rules to obtain updated target process O ij2 And according to the updated target procedure O ij2 The scheduling is continued. In a preset process O ij+1 The pre-process of (a) is not all in the initial process task set O finish In the case of the process, the process task set O is continued task The nth order is ordered according to order ordering rules until the updated target working procedure O ijn Preset process O of (2) ij+1 All the pre-processes of (a) are in the initial process task set O finish Is a kind of medium. In the working procedure task set O task If the set is not empty, the process task set O is described task If not all the processes in the process are scheduled, collecting the process tasks O task And re-ordering according to the order ordering rule to obtain an updated target procedure, and continuing to schedule according to the updated target procedure. In the working procedure task set O task If the set is still not empty, continuing to collect the process task set O task Re-ordering according to order ordering rule until the updated target process O ijm Meet the working procedure task set O task Is an empty set. And calling a scheduling algorithm to solve so as to obtain a scheduling result, and improving the executable performance and applicability of the process scheduling.
According to the technical scheme, various interference information in the service process is considered, the service influence is eliminated, the accuracy and timeliness of the scheduling scheme are ensured, manual operation is greatly reduced through an automatic algorithm, meanwhile, the algorithm can be manually interfered, and flexible coping under the condition of extreme service is ensured. The mode combines the advantages of different production modes, avoids disadvantages such as shortening the period of delivery, reduces the inventory stock and the inventory quantity, and can flexibly cope with the change of the sales plan.
FIG. 1 is a flow diagram of a method for determining a scheduling scheme in one embodiment. It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
Fig. 3 schematically illustrates a block diagram of a structure for determining a scheduling scheme according to an embodiment of the present application. As shown in fig. 3, an embodiment of the present application provides a controller, which may include:
a memory 310 configured to store instructions; and
processor 320 is configured to invoke instructions from memory 310 and when executing the instructions, to implement the method for determining a scheduling scheme described above.
Specifically, in embodiments of the present application, processor 320 may be configured to:
determining target sales data of a target product at a target time point;
determining demand fluctuation data and supply fluctuation data of a target product in a product delivery period corresponding to a target time point;
determining a safety stock of the target product at a target time point according to the demand fluctuation data and the supply fluctuation data;
predicting yield requirements of the target product at a target time point according to the target sales data, the safety stock and the plurality of influencing parameters;
determining a predicted supply of the target product;
and determining a scheduling scheme corresponding to the target time point according to the production and marketing requirements and the predicted supply quantity.
In one embodiment, the processor 320 may be further configured to:
determining a safety stock of the target product at the target point in time based on the demand fluctuation data and the supply fluctuation data comprises: acquiring an expected service level of a target product; the safety stock is determined based on the demand fluctuation data, the supply fluctuation data, and the desired service level.
In one embodiment, the processor 320 may be further configured to:
the influencing parameters include non-stock inventory, strategic placement, and developing a new plan, and predicting the yield requirement of the target product at the target point in time based on the target sales data, the safety stock, and the plurality of influencing parameters includes calculating the yield requirement according to the following equation (1):
D＝a 1 O+a 2 X+a 3 C+a 4 S-a 5 F+a 6 P (1)
Wherein D refers to the yield requirement of the target product at the target time point, O refers to the ascertained business machine of the target product at the target time point, X refers to the target sales data of the target product at the target time point, C refers to the non-standard inventory of the target product, S refers to the strategic placement of the target product, F refers to the safe inventory of the target product, P refers to the development new machine plan of the target product, a 1 Refers to the business machine realization coefficient, a 2 Refers to a duration prediction distribution coefficient, a 3 Is a specified system demand coefficient, a 4 Refers to strategic goods laying coefficients, a 5 Refers to the safety stock proportion, a 6 Refers to the innovation investment coefficient.
In one embodiment, the processor 320 may be further configured to:
determining target sales data for a target product at a target point in time includes: acquiring a plurality of historical sales data of a target product; and after the plurality of historical sales data are subjected to differential processing, inputting the processed plurality of historical sales data into a preset regression model so as to output target sales data through the preset regression model.
In one embodiment, the processor 320 may be further configured to:
inputting the processed plurality of historical sales data into a preset regression model, wherein the outputting of the target sales data through the preset regression model comprises the following steps of:
X t ＝α 1 X t-1 +α 2 X t-2 +…+α p X t-p +ε t +β 1 ε t-1 +…+β q ε t-q (2)
Wherein X is t Refers to target sales data of target products at a target time point t, X t-1 、X t-2 ...X t-p The historical sales data and epsilon corresponding to the target product at the historical time points t-1 and t-2 t 、ε t-1 ...ε t-q All refer to the white noise of the preset regression model, p refers to the quantity of historical sales data, q refers to the quantity of white noise, alpha 1 ...α q Refers to autoregressive coefficients that describe the relationship between the current observations and the top p historical sales data. Beta 1 ...β q Refers to a moving average coefficient describing the relationship between the current observation and the first q noise terms.
In one embodiment, the processor 320 may be further configured to:
determining the predicted supply of the target product includes: determining a supply chain of the target product, wherein the supply chain comprises a plurality of supply nodes and demand nodes corresponding to each supply node; for each supply node, determining an in-transit inventory currently being transported from the supply node to the corresponding demand node; determining the current stock sum of the target product according to the in-transit stock of each supply node; acquiring the upper limit of the capacity of a target product and the planned capacity; and determining the predicted supply quantity of the target product according to the upper limit of the capacity, the planned capacity and the current stock sum.
In one embodiment, the processor 320 may be further configured to:
The method for determining the scheduling scheme corresponding to the target time point according to the yield requirement and the predicted supply quantity comprises the following steps: determining a production task set for producing the target product according to the production and marketing requirements and the predicted supply quantity; initializing a production task set to obtain an initial production task set; sequencing the production task set according to order sequencing rules to determine target production; determining target equipment according to the priority of the optional equipment produced by the target; distributing the target production to the target equipment and updating the load capacity of the target equipment; under the condition that the load capacity updating of the target equipment is completed, moving the target production from the production task set to the initial production task set; judging whether all the pre-production of the preset production in the production task set is in the initial production task set; moving the preset production from the production task set to the initial production task set under the condition that all the pre-production of the preset production in the production task set is judged to be in the initial production task set; judging whether the production task set is an empty set or not; and ending the scheduling algorithm based on the heuristic rule under the condition that the production task set is an empty set so as to obtain the production scheduling scheme.
In one embodiment, as shown in FIG. 4, a flow chart of a production and marketing planning algorithm of one embodiment is provided. The system comprises an MBP system, an APS system, an SAP system, a PLM system and an MES system. Specifically, the scheduling regime is determined by a production and marketing planning algorithm model. Specifically, a heuristic-based scheduling algorithm may be used. Market sales forecast, market analysis, non-standard inventory, strategic placement data are obtained from the MBP system and rolled daily to the APS system. The business sales prediction comprises target sales data, and the MBP system analyzes the historical sales data through an autoregressive comprehensive moving average model to predict daily target sales data. And acquiring a new development plan from the PLM system, and sending the new development plan to the APS system in a month. The current stock and the complete machine safety stock are obtained from an SAP system library, and the current stock and the complete machine safety stock are sent to an APS system in a rolling mode every day. The output requirement is obtained through analysis and calculation of a time series prediction mixed model (including but not limited to an autoregressive comprehensive moving average model and a seasonal decomposition method) according to business machine sales prediction, market analysis, non-standard inventory, strategic placement, complete machine safety stock and new machine plan development. The related heavy part resources and annual outline information are marked through the APS system, and the annual outline information comprises the planned productivity. The upper capacity limit (maximum capacity planning and minimum capacity requirement) is obtained from the MES system and sent to the APS system according to the requirement. And according to the related heavy part resources, the upper limit of the productivity, the annual outline and the current inventory condition, the supply capacity (predicted supply quantity) is obtained through analysis and calculation of a supply chain network optimization model, a productivity planning model and a multi-objective optimization model. The APS system carries out simulation modeling on the whole process of enterprise production and marketing according to the output requirement and the supply capability, and obtains the final output result of the production scheduling scheme by combining an interactive solving algorithm and a scheduling algorithm based on heuristic rules to guide the production plan to schedule production. After the planning result of the production scheduling scheme is obtained, manual adjustment can be performed through review, and the result can be recalculated through adjustment of part of parameters to guide production scheduling and raw material purchase. The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel may be provided with one or more kernel parameters to implement the method for determining the scheduling scheme.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
The embodiment of the application provides a storage medium, on which a program is stored, which when executed by a processor, implements the above-described method for determining a scheduling scheme.
The embodiment of the application provides a processor for running a program, wherein the program runs to execute the method for determining the scheduling scheme.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor a01, a network interface a02, a memory (not shown) and a database (not shown) connected by a system bus. Wherein the processor a01 of the computer device is adapted to provide computing and control capabilities. The memory of the computer device includes internal memory a03 and nonvolatile storage medium a04. The nonvolatile storage medium a04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory a03 provides an environment for the operation of the operating system B01 and the computer program B02 in the nonvolatile storage medium a04. The database of the computer device is for storing data for determining a method of scheduling a production scheme. The network interface a02 of the computer device is used for communication with an external terminal through a network connection. The computer program B02 is executed by the processor a01 to implement a method for determining a scheduling scheme.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
The embodiment of the application provides equipment, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the method for determining the scheduling scheme.
The present application also provides a computer program product adapted to perform a program initialized with the above-mentioned method steps for determining a scheduling scheme when executed on a data processing apparatus.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may 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, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method for determining a scheduling regimen, the method comprising:
determining target sales data of a target product at a target time point;
determining demand fluctuation data and supply fluctuation data of the target product in a product delivery period corresponding to the target time point;
Determining a safety stock of the target product at the target time point according to the demand fluctuation data and the supply fluctuation data;
predicting a yield requirement of the target product at the target time point according to the target sales data, the safety stock and a plurality of influencing parameters;
determining a predicted supply of the target product;
and determining a scheduling scheme corresponding to the target time point according to the production and marketing requirements and the predicted supply quantity.
2. The method for determining a production schedule of claim 1, wherein said determining a safety stock for the target product at the target point in time based on the demand fluctuation data and the supply fluctuation data comprises:
acquiring an expected service level of the target product;
the safety stock is determined according to the demand fluctuation data, the supply fluctuation data and the expected service level.
3. The method for determining a production schedule of claim 1, wherein the influencing parameters include a non-stock inventory, strategic placement, and development of a new machine plan, and wherein predicting a production demand for the target product at the target point in time based on the target sales data, the safety stock, and a plurality of influencing parameters includes calculating the production demand according to the following equation (1):
D＝a 1 O+a 2 X+a 3 C+a 4 S-a 5 F+a 6 P (1)
Wherein D refers to the yield requirement of the target product at the target time point, O refers to the ascertained business machine of the target product at the target time point, X refers to the target sales data of the target product at the target time point, C refers to the non-standard inventory of the target product, S refers to the strategic placement of the target product, F refers to the safety stock of the target product, P refers to the new machine development plan of the target product, a 1 Refers to the business machine realization coefficient, a 2 Refers to a duration prediction distribution coefficient, a 3 Is a specified system demand coefficient, a 4 Refers to strategic goods laying coefficients, a 5 Refers to the safety stock proportion, a 6 Refers to the innovation investment coefficient.
4. The method for determining a production schedule of claim 1, wherein the determining target sales data for a target product at a target point in time comprises:
acquiring a plurality of historical sales data of the target product;
and after the plurality of historical sales data are subjected to differential processing, inputting the processed plurality of historical sales data into a preset regression model so as to output the target sales data through the preset regression model.
5. The method for determining a production schedule of claim 4, wherein the inputting the processed plurality of historical sales data into a preset regression model to output the target sales data through the preset regression model includes a functional expression of the preset regression model as shown in formula (2):
X t ＝α 1 X t-1 +α 2 X t-2 +…+α p X t-p +ε t +β 1 ε t-1 +…+β q ε t-q (2)
Wherein X is t Means the target sales data of the target product at a target time point t, X t-1 、X t-2 …X t-p Respectively refers to the historical sales data epsilon corresponding to the target product at the historical time points t-1 and t-2 … t-p t 、ε t-1 …ε t-q All refer to the white noise of the preset regression model, p refers to the quantity of the historical sales data, q refers to the quantity of the white noise, alpha 1 …α q Refers to autoregressive coefficients describing the relationship between the current observations and the top p historical sales data, beta 1 …β q Refers to a moving average coefficient describing the relationship between the current observation and the first q noise terms.
6. The method for determining a production schedule of claim 1, wherein the determining the predicted supply of the target product comprises:
determining a supply chain of the target product, wherein the supply chain comprises a plurality of supply nodes and demand nodes corresponding to each supply node;
for each supply node, determining an in-transit inventory currently being transported from the supply node to a corresponding demand node;
determining a current inventory sum of the target product according to the in-transit inventory of each supply node;
acquiring the upper limit of the capacity of the target product and the planned capacity;
and determining the predicted supply quantity of the target product according to the upper limit of the capacity, the planned capacity and the current stock sum.
7. The method for determining a scheduling scheme of claim 1, wherein said determining a scheduling scheme corresponding to the target point in time based on the yield requirement and the predicted supply amount comprises:
determining a production task set for producing the target product according to the production and marketing requirements and the predicted supply quantity;
initializing the production task set to obtain an initial production task set;
sorting the production task sets according to order sorting rules to determine target production;
determining target equipment according to the optional equipment priority of the target production;
distributing the target production to the target equipment and updating the load capacity of the target equipment;
moving the target production from the production task set to the initial production task set upon completion of the load capacity update of the target device;
judging whether all the pre-production of the preset production in the production task set is in the initial production task set;
moving the preset production from the production task set to the initial production task set under the condition that all the pre-production of the preset production in the production task set is judged to be in the initial production task set;
Judging whether the production task set is an empty set or not;
and ending a scheduling algorithm based on heuristic rules under the condition that the production task set is an empty set so as to obtain the production scheduling scheme.
8. A processor configured to perform the method for determining a production schedule according to any one of claims 1 to 7.
9. An apparatus for determining a scheduling plan, comprising:
a memory configured to store instructions; and
the processor of claim 8.
10. A machine-readable storage medium having instructions stored thereon, which when executed by a processor cause the processor to be configured to perform the method for determining a scheduling scheme according to any one of claims 1 to 7.
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