CN113536046B - Supply chain planning service optimization method, system, electronic equipment and storage medium - Google Patents
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Abstract
The application relates to a supply chain plan optimization method, a supply chain plan optimization system, electronic equipment and a storage medium based on graphical configuration, wherein the supply chain plan optimization method comprises the following steps: a configuration module is established by using the supply chain information; building a graphical supply chain flow model based on the actual business flow and combining a configuration module, and generating flow direction information which represents business flow in the actual business flow; constructing a multi-period supply chain planning model according to the flow direction information, wherein the supply chain planning model comprises a plurality of different planning schemes; one of the planning schemes is selected for execution and the supply chain planning model is optimized in real time during execution. According to the application, the graphical supply chain planning model is constructed, constraint conditions such as the production capacity of a processing device are comprehensively considered, the planning model is dynamically optimized, the planning of enterprise supply chain planning in one or more periods in the future is realized, and the benefit is effectively improved while the efficient operation of enterprise operation is ensured.
Description
Technical Field
The present application relates to the field of computer software technologies, and in particular, to a supply chain plan optimization method, system, electronic device, and storage medium based on a graphical configuration.
Background
The supply chain planning of the enterprise is not only one of important links of enterprise management, but also can further guide production, is an effective basis for production decision, and the advantages and disadvantages of the planning scheme can often determine the economic benefit of the enterprise.
Currently, supply chain planning for most domestic enterprises relies on human experience. However, for large and medium enterprises, the product specifications are numerous, the business process is frequently changed, and negotiation and resource adjustment among links of each department are required in the planning process. The manual planning of the supply chain is very complex and labor-consuming, the optimization result is difficult to obtain quickly according to the manual coordination among departments, and the accuracy of the finally-made planning scheme cannot be quantified. In addition, the planning schemes of the supply chains of many enterprises often use single-period optimization, take one month as an optimization interval or take a day as a unit, optimize a scheme with maximized benefits, multiply the scheme with coefficients, and do not dynamically update and roll according to actual planning conditions, so that the planning is more accurate in the early stage, the deviation of the planning from the actual conditions is larger and larger along with the time, the planning accuracy is lower and lower, and the disjointing of the supply chain month planning from the actual conditions is unavoidable.
The above drawbacks are to be overcome by those skilled in the art.
Disclosure of Invention
First, the technical problem to be solved
In order to solve the problems in the prior art, the application provides a supply chain plan optimization method, a supply chain plan optimization system, an electronic device and a storage medium based on graphical configuration, which aim to solve the problem that a supply chain plan scheme cannot be changed and optimized along with changes in the prior art.
(II) technical scheme
To solve the above problems, in a first aspect, the present application provides a supply chain plan optimization method based on a graphical configuration, the method comprising:
a configuration module is established by using the supply chain information;
building a graphical supply chain flow model based on the actual business flow and combining the configuration module, and generating flow direction information, wherein the flow direction information represents business flow in the actual business flow;
constructing a multi-period supply chain planning model according to the flow direction information, wherein the supply chain planning model comprises a plurality of different planning schemes;
one of the plurality of planning schemes is selected for execution and the supply chain planning model is optimized in real-time during execution.
In an exemplary embodiment of the present application, the building a graphical supply chain process model based on the actual business process and the process configuration module includes:
establishing a supply chain flow chart according to the actual business flow and flow direction of an enterprise, wherein the supply chain flow chart comprises a plurality of graphic elements and a plurality of flows, the graphic elements are arranged corresponding to the configuration modules, and the flows are arranged between the configuration modules;
encoding the plurality of primitives and the plurality of streams separately in encoded form;
and setting constraint conditions and configuration information for each link of purchasing, processing, feeding and discharging in the supply chain flow chart.
In an exemplary embodiment of the present application, the generating stream flow information includes:
acquiring flow configuration information from the supply chain flow chart, selecting intersection from the flow configuration information, and extracting graphic primitives;
classifying and defining different primitives, and acquiring a coding set of each type of primitives according to coding rules;
sequencing the primitives to obtain inflow and outflow corresponding to each primitive, and constructing a corresponding inflow matrix and outflow matrix for each primitive;
determining unit consumption and yield of the device according to the configuration information and the acquired bill of materials;
and (3) corresponding the purchased feeding flow variable to the raw material, and corresponding the sales discharging flow to the finished product to form a raw material two-dimensional matrix corresponding to the raw material and the variable and a product two-dimensional matrix corresponding to the product and the variable.
In one exemplary embodiment of the application, constructing a multi-cycle supply chain planning model from the flow direction information includes:
obtaining an input quantity of the supply chain planning model, wherein the input quantity at least comprises: any one of an upper limit and a lower limit of a raw material warehouse, an upper limit and a lower limit of a material warehouse, an upper limit and a lower limit of production capacity of a workshop, a product sales plan, an upper limit and a lower limit of a purchasing amount, a raw material warehouse inventory cost, a raw material warehouse initial inventory, a product warehouse initial inventory, a market price of a product, a purchasing price of raw materials, flow information of a flow, a planning period, a current case name and a case group;
obtaining an output of the supply chain planning model, wherein the output at least comprises: any one of purchasing plans, raw material warehouse stock, allocating warehouse stock, finished product warehouse stock, material feeding and discharging amount, workshop processing yield, optimized product sales amount and optimal objective function value of the current case in each period in the planning period;
and combining the input quantity and the output quantity, maximizing economic benefit into an optimal objective function, and carrying out linear programming solving by taking the input and output material balance, the purchase quantity, the raw material inventory quantity, the allocation warehouse inventory, the finished product warehouse inventory, the sales quantity and the flow limit as constraint conditions to obtain a multi-period supply chain planning model.
In an exemplary embodiment of the present application, the optimal objective function is:
economic benefit = sales amount × sales price-stock cost of each warehouse-raw material purchase amount × raw material price;
the balance constraint conditions of the feeding and the discharging are as follows:
delta is less than or equal to the feeding amount- [ consumption coefficient ] × [ discharging amount ] < delta;
wherein delta is a relaxation variable;
the purchasing quantity constraint conditions are as follows:
the purchasing lower limit is less than or equal to the purchasing quantity and less than or equal to the purchasing upper limit;
the stock quantity constraint conditions are as follows:
the lower stock limit is less than or equal to the purchasing quantity + the initial stock-discharging quantity is less than or equal to the upper stock limit;
the warehouse inventory constraint condition is that:
the lower stock limit is less than or equal to the feeding amount plus the initial stock-sigma discharging amount is less than or equal to the upper stock limit;
the inventory constraint conditions of the finished product warehouse are as follows:
the stock lower limit is less than or equal to the feeding amount + the original stock-sales amount is less than or equal to the stock upper limit;
the sales constraints are:
the lower limit of sales is less than or equal to the lower limit of sales and less than or equal to the upper limit of sales;
the workshop processing capacity limit constraint conditions are:
the lower workshop processing limit is more than or equal to Sigma workshop discharge amount is less than or equal to the upper workshop processing limit;
the flow restriction constraints are:
the lower flow limit is less than or equal to the lower flow limit and less than or equal to the upper flow limit.
In an exemplary embodiment of the present application, after a planning scheme is obtained in the constructing a multi-period supply chain planning model according to the flow direction information, the method further includes:
a plurality of planning schemes are established by changing configuration information and constraints, forming a multi-cycle supply chain planning model containing different ones of the plurality of planning schemes.
In an exemplary embodiment of the application, said optimizing said supply chain planning model in real time during execution comprises:
predicting actual benefits in the execution process;
if the predicted result cannot meet the preset requirement, comprehensively comparing the current advantages and the future advantages of different planning schemes by combining the flow direction information in the latest acquired actual business flow;
and providing an optimization scheme according to the comprehensive comparison result.
In a second aspect, the present application also provides a supply chain plan optimization system based on a graphical configuration, the system comprising:
the configuration establishing unit is used for establishing a configuration module by using the supply chain information;
the flow establishing unit is used for establishing a graphical supply chain flow model based on the actual business flow and combining the configuration module, and generating flow direction information, wherein the flow direction information represents business flows in the actual business flow;
the plan establishing unit is used for establishing a multi-period supply chain plan model according to the flow direction information, wherein the supply chain plan model comprises a plurality of different plan schemes;
and the execution optimization unit is used for selecting one of a plurality of planning schemes to execute and optimizing the supply chain planning model in real time in the execution process.
In a third aspect, the present application also provides an electronic device, including:
a processor;
a memory storing method steps as described above for the processor control.
In a fourth aspect, the application also provides a storage medium having stored thereon computer executable instructions, characterized in that the executable instructions when executed by a processor implement the method steps as described above.
(III) beneficial effects
The beneficial effects of the application are as follows: according to the supply chain plan optimizing method, system, electronic equipment and storage medium based on the graphical configuration, provided by the embodiment of the application, the graphical supply chain plan model is constructed, the constraint conditions such as the production capacity of a processing device are comprehensively considered, the plan model is dynamically optimized, the planning of enterprise supply chain plans of one period or even a plurality of periods in the future is realized, and the benefit is effectively improved while the efficient operation of enterprise operation is ensured.
Drawings
FIG. 1 is a flowchart of a supply chain plan optimization method based on a graphical configuration according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a supply chain flow diagram in accordance with one embodiment of the present application;
FIG. 3 is a schematic diagram of aspects of an embodiment of the present application;
FIG. 4 is a schematic diagram of a configuration model of a supply chain of an agronomic manufacturing enterprise in accordance with one embodiment of the present application;
FIG. 5 is a schematic diagram of a supply chain plan optimization system based on a graphical configuration according to another embodiment of the present application;
fig. 6 is a schematic diagram of an internal structure of a computer system of an electronic device according to still another embodiment of the present application.
Detailed Description
The application will be better explained by the following detailed description of the embodiments with reference to the drawings.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present application are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
Furthermore, descriptions such as those referred to as "first," "second," and the like, are provided for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implying an order of magnitude of the indicated technical features in the present disclosure. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the related embodiment of the application, only one optimization result is provided, and the diversification of the scheme execution process is not considered, but often, the comparison analysis of multiple schemes can further assist the higher layer of the enterprise to make decisions. In addition, the traditional model construction is too fixed, and when the conditions of production line adjustment, product class increase, warehouse new construction, warehouse withdrawal and the like occur, an optimization model needs to be reconstructed, so that the system maintenance workload is huge.
Therefore, in order to ensure the efficiency, accuracy and scheme execution diversification of the enterprise supply chain plan and the automation of the plan flow, the application provides a supply chain plan optimizing method based on graphical configuration, which provides comprehensive and effective support for the enterprise supply chain plan, can assist the enterprise to reasonably allocate resources, complete customer delivery on time, effectively control inventory and realize the maximization of enterprise profit.
Fig. 1 is a flowchart of a supply chain plan optimization method based on a graphical configuration according to an embodiment of the present application, as shown in fig. 1, specifically including the following steps:
as shown in fig. 1, in step S110, a configuration module is built according to supply chain information;
as shown in fig. 1, in step S120, a graphical supply chain flow model is built based on the actual service flow in combination with the configuration module, and flow information is generated, where the flow information represents the service flow in the actual service flow;
as shown in fig. 1, in step S130, a multi-period supply chain plan model is constructed according to the flow direction information, wherein the supply chain plan model includes a plurality of different plan schemes;
as shown in fig. 1, in step S140, one of a plurality of planning schemes is selected to be executed, and the supply chain planning model is optimized in real time during the execution.
Based on the method, a graphical supply chain plan model is constructed, constraint conditions such as the production capacity of a processing device are comprehensively considered, a multi-period processing plan is constructed, the plan model is dynamically optimized, the planning of enterprise supply chain plans of one period or even a plurality of periods in the future is realized, and the benefit is effectively improved while the efficient operation of enterprise operation is ensured.
The method shown in fig. 1 is described in detail below:
in step S110, a configuration module is built according to the supply chain information.
In an exemplary embodiment of the application, this step is performed by creating a basic data configuration, e.g., encoding the supply chain information (e.g., process capability, inventory capacity, demand planning, materials, etc. of the process plant) object, and retrieving initial inventory, plant process capability, price system, etc. data from the third party system interface, and creating a configuration module based on these basic data. The configuration module obtained in the step is used as a basic constitution of a subsequent establishment flow model, and for a processing device, the processing capability, the feeding condition of single processing, the product outlet condition, the part abrasion and maintenance condition and the like are included; the warehouse includes the number of warehouse-in and warehouse-out, the category, the occupied space, the storage environment and the like.
In step S120, a patterned supply chain flow model is built based on the actual service flow in combination with the configuration module, and flow direction information is generated, where the flow direction information represents the service flow in the actual service flow.
In step S120, a patterned supply chain process model is formed by processing the basic data of the configuration module in combination with the process flow.
In an exemplary embodiment of the present application, the building a graphical supply chain process model based on the actual business process and the process configuration module in this step includes:
s121: a supply chain flow chart is established according to the actual business flow and flow direction of the enterprise, wherein the supply chain flow chart comprises a plurality of graphic elements and a plurality of flows.
In this step, a supply chain flow chart is established according to the actual business flow and flow direction of the supply chain provided by the enterprise, and the supply chain flow chart is shown in a graphical manner, and is simple and clear. Wherein the primitives shown in the supply chain flow diagram are arranged corresponding to configuration modules, and the streams are arranged between the configuration modules. FIG. 2 is a schematic diagram of a supply chain flow chart according to an embodiment of the present application, and is shown in FIG. 2, where N1, N2, etc. represent raw materials or products, R1, R2, etc. represent material warehouse, P1, P2, etc. represent product warehouse, l1 represents product production plant, stream1, stream2, etc. represent flow in production process, indicating the transfer relationship of materials or intermediate products from warehouse to plant to warehouse.
S122: the plurality of primitives and the plurality of streams are encoded separately in encoded form to ensure uniqueness.
S123: and setting constraint conditions and configuration information for each link of purchasing, processing, feeding and discharging in the supply chain flow chart. The supply chain planning and optimizing is carried out under the conditions of taking the capacity upper limit and lower limit of a raw material warehouse, the capacity upper line and lower line of a finished product warehouse, purchasing upper line and lower line, the workshop processing capacity upper line and lower line, the workshop yield constraint, the feeding and discharging balance and the sales planning of products into constraint by comprehensively considering the factors such as the capacity of the warehouse, the workshop processing, the product selling price and the raw material purchasing price.
S124: stream flow information is generated.
In an exemplary embodiment of the present application, the generating stream flow information includes:
firstly, flow configuration information is obtained from the supply chain flow chart, intersections are selected from the flow configuration information, and primitives are extracted. Table 1 is an example of flow configuration information, where T1, T2, etc. represent raw material warehouse primitives, M1, M2, etc. represent production plants, U1, U2, etc. represent finished product warehouses, and S1, S2, etc. represent assorted warehouses.
TABLE 1
And secondly, classifying and defining different primitives, and acquiring a coding set of each type of primitive according to coding rules.
Thirdly, sorting the primitives to obtain inflow and outflow corresponding to each primitive, and constructing a corresponding inflow matrix and outflow matrix for each primitive; the matrix is represented by 0 or 1, the dimension of the matrix is the number of primitives x total flow, as shown in table 2, table 2 shows an example of an ingress/egress flow matrix table.
TABLE 2
| stream1 | Stream2 | Stream3 | Stream4 | ... | |
| S1 | 0 | 1 | 0 | 0 | |
| N1 | 1 | 0 | 1 | 1 | |
| M3 | 0 | 0 | 1 | 0 | |
| N8 | 1 | 1 | 1 | 1 | |
| ... |
And then, determining the unit consumption and the yield of the device according to the configuration information and the acquired bill of materials. Specific: and acquiring relevant configuration information, and corresponding the uploaded bom table to stream data. If no bom table information is provided, training a model of historical device processing data according to a big data artificial intelligence algorithm to obtain a current device unit consumption and yield model, determining the device unit consumption and yield, and table 3 shows a device yield example.
TABLE 3 Table 3
| stream1 | Stream2 | Stream3 | stream1 | |
| Stream7 | 0.3 | 0 | 0.1 | 0.9 |
| Stream9 | 0 | 1.2 | 0 | 0 |
| Stream22 | 0.7 | 0.4 | 0 | 0.1 |
Finally, the procurement feed stream variable is correlated with the raw material, the sales discharge stream is correlated with the finished product, a raw material two-dimensional matrix corresponding to the raw material and variable and a product two-dimensional matrix corresponding to the product and variable are formed, and the stream flow examples are shown in Table 4.
TABLE 4 Table 4
| Stream name | Initial primitive name | Termination primitive name |
| stream1 | N1 | U1 |
| stream2 | N2 | U1 |
| stream3 | N3 | U1 |
| stream4 | U1 | T1 |
| stream5 | U1 | T2 |
| stream6 | T1 | N5 |
| stream7 | T2 | N6 |
The plan model obtained through the steps is dynamically generated based on the visualized supply chain business flow direction information, so that the maintenance is convenient, the plan optimization system supports business flow configuration construction, the model is not immobilized any more, and customization of the plan optimization model can be realized through simple constraint condition configuration and modification.
In step S130, a multi-cycle supply chain planning model is constructed according to the flow direction information, wherein the supply chain planning model includes a plurality of different planning schemes.
In an exemplary embodiment of the application, constructing a multi-cycle supply chain planning model from the flow direction information in this step includes:
obtaining input quantity of the supply chain planning model, wherein the input quantity at least comprises: any one of an upper limit and a lower limit of a raw material warehouse, an upper limit and a lower limit of a material warehouse, an upper limit and a lower limit of production capacity of a workshop, a product sales plan, an upper limit and a lower limit of a purchasing amount, a raw material warehouse inventory cost, a raw material warehouse initial inventory, a product warehouse initial inventory, a market price of a product, a purchasing price of raw materials, flow information of a flow, a planning period, a current case name and a case group;
obtaining an output of the supply chain planning model, the output at least comprising: at least one of purchasing plans, raw material warehouse stock, allocating warehouse stock, finished product warehouse stock, material feeding and discharging amount, workshop processing yield, optimized product sales amount and optimal objective function value of the current case in each period in the planning period;
and combining the input quantity and the output quantity, maximizing economic benefit into an optimal objective function, and carrying out linear programming solving by taking the input and output material balance, the purchase quantity, the raw material inventory quantity, the allocation warehouse inventory, the finished product warehouse inventory, the sales quantity and the flow limit as constraint conditions to obtain a multi-period supply chain planning model.
In an exemplary embodiment of the present application, the optimal objective function is:
economic benefit = sales amount × sales price-stock cost of each warehouse-raw material purchase amount × raw material price;
the balance constraint conditions of the feeding and the discharging are as follows:
delta is less than or equal to the feeding quantity- [ consumption coefficient ] × [ discharging quantity is less than or equal to delta;
wherein delta is a relaxation variable;
the purchasing quantity constraint conditions are as follows:
the purchasing lower limit is less than or equal to the purchasing quantity and less than or equal to the purchasing upper limit;
the stock quantity constraint conditions are as follows:
the lower stock limit is less than or equal to the purchasing quantity + the initial stock-discharging quantity is less than or equal to the upper stock limit;
the warehouse inventory constraint condition is that:
the lower stock limit is less than or equal to the feeding amount plus the initial stock-sigma discharging amount is less than or equal to the upper stock limit;
the inventory constraint conditions of the finished product warehouse are as follows:
the stock lower limit is less than or equal to the feeding amount + the original stock-sales amount is less than or equal to the stock upper limit;
the sales constraints are:
the lower limit of sales is less than or equal to the lower limit of sales and less than or equal to the upper limit of sales;
the workshop processing capacity limit constraint conditions are:
the lower workshop processing limit is more than or equal to Sigma workshop discharge amount is less than or equal to the upper workshop processing limit;
the flow restriction constraints are:
the lower flow limit is less than or equal to the lower flow limit and less than or equal to the upper flow limit.
Based on the steps, the multi-period production scheme optimization calculation is carried out by the linear programming solver when the economic benefit is maximized.
In an exemplary embodiment of the present application, after obtaining a planning scheme, the method further includes:
a plurality of planning schemes are established by changing configuration information and constraints, forming a multi-cycle supply chain planning model containing different ones of the plurality of planning schemes.
Based on the steps, a plurality of supply chain planning schemes can be established or expanded by changing different configurations or constraint conditions, input and constraint of a model can be regulated by setting fixed or variable step sizes, a series of different supply chain planning schemes are formed, then multi-dimensional comparison analysis is carried out on the different schemes, and a more comprehensive and changeable scheme can be provided for a user by combining multi-period dimensions. Fig. 3 is a schematic diagram of multiple schemes in an embodiment of the present application, as shown in fig. 4, the initial scheme 1 may be used as a reference, and the above-mentioned method is adopted to expand the schemes gradually to obtain 9 schemes, and the schemes are obtained from different configurations, constraint conditions or input variables, when parameters of a certain dimension change in the processing process, the schemes can be directly found to implement the schemes immediately, so as to save the time of re-planning.
Therefore, in the operation of the plan optimization model, the unit consumption and yield model of the production device can be trained in a data driving mode by utilizing the mode of big data and artificial intelligence and through the historical data of the consumption and the output condition of materials in the production process of an enterprise, so that the situation that the enterprise cannot give a fixed bom table is made up.
In step S140, one of the plurality of planning schemes is selected for execution, and the supply chain planning model is optimized in real time during execution.
In an exemplary embodiment of the application, said optimizing said supply chain planning model in real time during execution comprises:
firstly, the actual benefit is predicted in the execution process, and enterprises often take the benefit as the most main target, so that the actual benefit generated when the method is executed according to the current technology is predicted in the execution process, the deviation between a plan and the actual is fully considered, the feedback of the actual plan scheme data is supported, and the multi-period plan optimization scheme can be calculated in a rolling mode.
And secondly, if the predicted result cannot meet the preset requirement, comprehensively comparing the current advantages and the future advantages of different planning schemes by combining the flow direction information in the latest acquired actual business process. Because the program is designed for multiple cycles, the present and future advantages of each program can be calculated and compared as a consideration in selecting alternative solutions.
And finally, providing an optimization scheme according to the comprehensive comparison result.
The step description not only carries out auditing, publishing and tracking on the plan, but also regularly tracks and feeds back various information such as raw material purchasing condition, production completion condition, order execution condition, raw material product inventory, finished product sales condition and the like after the supply chain plan is issued, the information is used as flow direction information in the latest acquired and actual business flow, the plan deviation is subjected to multi-period rolling correction, and the instantaneity, the accuracy and the completion rate of the supply chain plan are ensured.
The following is an example of an implementation of the method:
some agrochemicals manufacturing enterprises take glyphosate raw medicines as a main material and phosphorus chemical industry as a matching material, and various raw medicines and dosage forms synchronously develop agrochemicals. The enterprise supply chain planning is established by taking material balance as a main material, and simultaneously taking the stock capacity, the device processing capacity, the demand planning and the like into consideration, so as to construct a supply chain configuration model. Fig. 4 is a schematic diagram of a supply chain configuration model of an agrochemical manufacturing enterprise according to an embodiment of the present application, as shown in fig. 4, in which plan data reports of purchase, inventory, production, etc. can be obtained by optimizing the supply chain by setting constraints of supply chain devices, price systems, etc., and table 5 is a purchasing plan example of the present month, and table 6 is a production plan example of the present month.
TABLE 5
TABLE 6
From the above tables 5 and 6, it is clear that a planned value of a planned production month, as well as a history value, can be seen, and actual production and scheduling can be guided by the release of the planned value.
In summary, the supply chain plan optimization process modeling is realized by constructing a graphical supply chain plan model and utilizing a graphical configuration, the linear programming method is based on flexible multi-objective methods such as benefit maximization and full load of the device, all units (including raw material warehouse, workshop, transfer warehouse, finished product warehouse and the like) are taken as variables, and a multi-period plan optimization model is dynamically established under the constraint conditions of comprehensive consideration of the device material inlet and outlet balance, workshop processing, inventory constraint, product yield, purchasing and the like, so that enterprise supply chain plan (purchasing plan, inventory plan, production plan and sales plan) arrangement of one period or even multiple periods in the future is realized, and the efficient operation of enterprise operation is ensured while the benefit is effectively improved. In addition, the method supports establishment of multiple periods and multiple schemes, and the multiple scheme profit capability measuring and calculating module is utilized to conduct multiple scheme analysis, so that economic benefits of different schemes can be compared and analyzed from different dimensions, planning personnel and financial personnel can be assisted in selecting a planning scheme with the largest profit capability, and high-level rapid decision making of an enterprise can be assisted.
In accordance with the above method, fig. 5 is a schematic diagram of a supply chain plan optimization system based on a graphical configuration according to another embodiment of the present application, and as shown in fig. 5, the system 200 includes: the configuration setting up unit 210, the flow setting up unit 220, the plan setting up unit 230 and the execution optimizing unit 240.
The configuration establishing unit 210 is configured to establish a configuration module according to the supply chain information; the flow establishing unit 220 is configured to establish a patterned supply chain flow model based on the actual service flow in combination with the configuration module, and generate flow direction information, where the flow direction information represents a service flow in the actual service flow; the plan building unit 230 is configured to build a multi-period supply chain plan model according to the flow direction information, where the supply chain plan model includes a plurality of different plan schemes; the execution optimization unit 240 is configured to select one of a plurality of planning schemes to execute, and optimize the supply chain planning model in real time during execution.
The functions of the units in the system are referred to in the above description of the method embodiments, and are not repeated here.
In another aspect, the present disclosure also provides an electronic device, including a processor and a memory, the memory storing operation instructions for the processor to control:
a configuration module is established by using the supply chain information;
building a graphical supply chain flow model based on the actual business flow and combining the configuration module, and generating flow direction information, wherein the flow direction information represents business flow in the actual business flow;
constructing a multi-period supply chain planning model according to the flow direction information, wherein the supply chain planning model comprises a plurality of different planning schemes;
one of the plurality of planning schemes is selected for execution and the supply chain planning model is optimized in real-time during execution.
Referring now to FIG. 6, a schematic diagram of a computer system 400 suitable for use in implementing an electronic device of an embodiment of the present application is shown. The electronic device shown in fig. 6 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the application.
As shown in fig. 5, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage portion 407 into a Random Access Memory (RAM) 403. In RAM 403, various programs and data required for the operation of system 400 are also stored. The CPU401, ROM 402, and RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output portion 407 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage section 408 including a hard disk or the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. The drive 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 409 and/or installed from the removable medium 411. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 401.
The storage medium shown in the present application may be a computer readable signal medium or a computer readable medium, or any combination of the two. The computer readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented in software or in hardware. The described units may also be provided in a processor, wherein the names of the units do not in some cases constitute a limitation of the unit itself.
In another aspect, the present disclosure also provides a storage medium that may be included in the electronic device described in the above embodiments; or may exist alone without being incorporated into the electronic device. The storage medium carries one or more programs which, when executed by one of the electronic devices, cause the electronic device to include the method steps of:
a configuration module is established by using the supply chain information;
building a graphical supply chain flow model based on the actual business flow and combining the configuration module, and generating flow direction information, wherein the flow direction information represents business flow in the actual business flow;
constructing a multi-period supply chain planning model according to the flow direction information, wherein the supply chain planning model comprises a plurality of different planning schemes;
one of the plurality of planning schemes is selected for execution and the supply chain planning model is optimized in real-time during execution.
It should be understood that the above description of the specific embodiments of the present application is only for illustrating the technical route and features of the present application, and is for enabling those skilled in the art to understand the present application and implement it accordingly, but the present application is not limited to the above-described specific embodiments. All changes or modifications that come within the scope of the appended claims are intended to be embraced therein.
Claims (7)
1. A supply chain plan optimization method based on a graphical configuration, the method comprising:
a configuration module is established by using the supply chain information;
building a graphical supply chain flow model based on the actual business flow and combining the configuration module, and generating flow direction information, wherein the flow direction information represents business flow in the actual business flow;
the building of the graphical supply chain flow model based on the actual business flow and the configuration module comprises the following steps:
establishing a supply chain flow chart according to the actual business flow and flow direction of an enterprise, wherein the supply chain flow chart comprises a plurality of graphic elements and a plurality of flows, the graphic elements are arranged corresponding to the configuration modules, and the flows are arranged between the configuration modules;
encoding the plurality of primitives and the plurality of streams separately in encoded form;
setting constraint conditions and configuration information for each link of purchasing, processing, feeding and discharging in the supply chain flow chart;
the generating stream flow information includes:
acquiring flow configuration information from the supply chain flow chart, selecting intersection from the flow configuration information, and extracting graphic primitives;
classifying and defining different primitives, and acquiring a coding set of each type of primitives according to coding rules;
sequencing the primitives to obtain inflow and outflow corresponding to each primitive, and constructing a corresponding inflow matrix and outflow matrix for each primitive;
determining unit consumption and yield of the device according to the configuration information and the acquired bill of materials;
corresponding the purchased feeding flow variable to the raw material, and corresponding the sales discharging flow to the finished product to form a raw material two-dimensional matrix corresponding to the raw material and the variable and a product two-dimensional matrix corresponding to the product and the variable;
constructing a multi-period supply chain planning model according to the flow direction information, wherein the supply chain planning model comprises a plurality of different planning schemes and comprises the following steps:
obtaining input quantity of a supply chain planning model, wherein the input quantity at least comprises: any one of an upper limit and a lower limit of a raw material warehouse, an upper limit and a lower limit of a material warehouse, an upper limit and a lower limit of production capacity of a workshop, a product sales plan, an upper limit and a lower limit of a purchasing amount, a raw material warehouse inventory cost, a raw material warehouse initial inventory, a product warehouse initial inventory, a market price of a product, a purchasing price of raw materials, flow information of a flow, a planning period, a current case name and a case group;
obtaining an output of the supply chain planning model, the output comprising at least: at least one of purchasing plans, raw material warehouse stock, allocating warehouse stock, finished product warehouse stock, material feeding and discharging amount, workshop processing yield, optimized product sales amount and optimal objective function value of the current case in each period in the planning period;
combining the input quantity and the output quantity, maximizing economic benefit into an optimal objective function, and carrying out linear programming solving by taking the input and output material balance, the purchase quantity, the raw material inventory quantity, the allocation warehouse inventory, the finished product warehouse inventory, the sales quantity and the flow limit as constraint conditions to obtain a multi-period supply chain planning model;
one of the plurality of planning schemes is selected for execution and the supply chain planning model is optimized in real-time during execution.
2. The supply chain plan optimization method based on a graphical configuration of claim 1, wherein the optimal objective function is:
economic benefit = sales amount × sales price-stock cost of each warehouse-raw material purchase amount × raw material price;
the balance constraint conditions of the feeding and the discharging are as follows:
delta is less than or equal to the feeding amount- [ consumption coefficient ] × [ discharging amount ] < delta;
wherein delta is a relaxation variable;
the purchasing quantity constraint conditions are as follows:
the purchasing lower limit is less than or equal to the purchasing quantity and less than or equal to the purchasing upper limit;
the stock quantity constraint conditions are as follows:
the lower stock limit is less than or equal to the purchasing quantity + the initial stock-discharging quantity is less than or equal to the upper stock limit;
the warehouse inventory constraint condition is that:
the lower stock limit is less than or equal to the feeding amount plus the initial stock-sigma discharging amount is less than or equal to the upper stock limit;
the inventory constraint conditions of the finished product warehouse are as follows:
the stock lower limit is less than or equal to the feeding amount + the original stock-sales amount is less than or equal to the stock upper limit;
the sales constraints are:
the lower limit of sales is less than or equal to the lower limit of sales and less than or equal to the upper limit of sales;
the workshop processing capacity limit constraint conditions are:
the lower workshop processing limit is more than or equal to Sigma workshop discharge amount is less than or equal to the upper workshop processing limit;
the flow restriction constraints are:
the lower flow limit is less than or equal to the lower flow limit and less than or equal to the upper flow limit.
3. The supply chain plan optimization method based on the graphical configuration according to claim 2, wherein after a plan scheme is obtained in the multi-period supply chain plan model constructed according to the flow direction information, the method further comprises:
a plurality of planning schemes are established by changing configuration information and constraints, forming a multi-cycle supply chain planning model containing different ones of the plurality of planning schemes.
4. The supply chain plan optimization method based on a graphical configuration of claim 3, wherein optimizing the supply chain plan model in real time during execution comprises:
predicting actual benefits in the execution process;
if the predicted result cannot meet the preset requirement, comprehensively comparing the current advantages and the future advantages of different planning schemes by combining the flow direction information in the latest acquired actual business flow;
and providing an optimization scheme according to the comprehensive comparison result.
5. A supply chain plan optimization system based on a graphical configuration, the system comprising:
the configuration establishing unit is used for establishing a configuration module by using the supply chain information;
the flow establishing unit is used for establishing a graphical supply chain flow model based on the actual business flow and combining the configuration module, and generating flow direction information, wherein the flow direction information represents business flows in the actual business flow;
the building of the graphical supply chain flow model based on the actual business flow and the configuration module comprises the following steps:
establishing a supply chain flow chart according to the actual business flow and flow direction of an enterprise, wherein the supply chain flow chart comprises a plurality of graphic elements and a plurality of flows, the graphic elements are arranged corresponding to the configuration modules, and the flows are arranged between the configuration modules;
encoding the plurality of primitives and the plurality of streams separately in encoded form;
setting constraint conditions and configuration information for each link of purchasing, processing, feeding and discharging in the supply chain flow chart;
the generating stream flow information includes:
acquiring flow configuration information from the supply chain flow chart, selecting intersection from the flow configuration information, and extracting graphic primitives;
classifying and defining different primitives, and acquiring a coding set of each type of primitives according to coding rules;
sequencing the primitives to obtain inflow and outflow corresponding to each primitive, and constructing a corresponding inflow matrix and outflow matrix for each primitive;
determining unit consumption and yield of the device according to the configuration information and the acquired bill of materials;
corresponding the purchased feeding flow variable to the raw material, and corresponding the sales discharging flow to the finished product to form a raw material two-dimensional matrix corresponding to the raw material and the variable and a product two-dimensional matrix corresponding to the product and the variable;
the plan establishing unit is used for establishing a multi-period supply chain plan model according to the flow direction information, wherein the supply chain plan model comprises a plurality of different plan schemes;
the plan creation unit is specifically configured to:
obtaining input quantity of a supply chain planning model, wherein the input quantity at least comprises: any one of an upper limit and a lower limit of a raw material warehouse, an upper limit and a lower limit of a material warehouse, an upper limit and a lower limit of production capacity of a workshop, a product sales plan, an upper limit and a lower limit of a purchasing amount, a raw material warehouse inventory cost, a raw material warehouse initial inventory, a product warehouse initial inventory, a market price of a product, a purchasing price of raw materials, flow information of a flow, a planning period, a current case name and a case group;
obtaining an output of the supply chain planning model, the output comprising at least: at least one of purchasing plans, raw material warehouse stock, allocating warehouse stock, finished product warehouse stock, material feeding and discharging amount, workshop processing yield, optimized product sales amount and optimal objective function value of the current case in each period in the planning period;
combining the input quantity and the output quantity, maximizing economic benefit into an optimal objective function, and carrying out linear programming solving by taking the input and output material balance, the purchase quantity, the raw material inventory quantity, the allocation warehouse inventory, the finished product warehouse inventory, the sales quantity and the flow limit as constraint conditions to obtain a multi-period supply chain planning model;
and the execution optimization unit is used for selecting one of a plurality of planning schemes to execute and optimizing the supply chain planning model in real time in the execution process.
6. An electronic device, comprising:
a processor;
memory storing method steps for the processor to control according to any one of claims 1-4.
7. A storage medium having stored thereon computer executable instructions, which when executed by a processor, implement the method steps according to any of claims 1-4.
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