CN108108994A - For the plan optimization method of chemical enterprise supply chain - Google Patents
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Abstract
The present invention provides the plan optimization methods for chemical enterprise supply chain, belong to optimization method field, including：Supply chain initial parameter is obtained, supply chain initial parameter is stored to data warehouse；With reference to chemical enterprise sales data over the years, determine that the market prospective demand opposite with chemical enterprise builds Supply Chain Planner Optimized model by Self Matching requirement forecasting, planning optimization object function and constraints are equipped in Supply Chain Planner Optimized model, the optimal solution of planned target function is acquired under constraints；Setup parameter step-length builds different planning optimization scheduling schemes automatically, calculates the corresponding profit of each plans, chooses profit highest corresponding plans and the relevant parameter of enterprise supply chain is optimized.The plans solved by that while supply and demand is balanced, can provide an economic optimization to realize that the dynamic of product structure adjusts, improve the accuracy, enforceability and the efficiency of planning work of plan, information support are provided for production management.
Description
Technical Field
The invention belongs to the field of optimization methods, and particularly relates to a supply chain plan optimization method for a chemical enterprise.
Background
At present, the supply chain planning of most domestic chemical enterprises is carried out by adopting manual experience. For a large and medium-sized chemical enterprise, the product specifications are numerous, the business process changes frequently, and the manual planning of the supply chain is very complicated.
On one hand, demand planning personnel at the front end of a supply chain plan need to perform market prediction based on historical data, the common prediction mode is manual judgment and summary, the workload is huge, corresponding mathematical models and system assistance are not needed, the prediction deviation is large, and the supply plan is easy to mislead; on the other hand, in practice, many information (such as inventory, in-transit, whether raw materials can be delivered on time, raw material supply, logistics transportation information and the like) needed by a supply chain plan of a chemical enterprise is opaque and serious in asymmetry, the supply and demand balance of chemical raw materials and products is difficult, due to the lack of auxiliary optimization of a mathematical model, the manual plan is difficult to ensure the global optimum, the phenomena of uncoordinated raw material supply, plan, overhaul and inventory are frequent, and the completion rate of the whole plan is unsatisfactory.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention provides a plan optimization method for a chemical industry enterprise supply chain, which is used for determining an optimal optimization scheme for the supply chain in a mode of constructing an optimization function and then solving the optimization function for the optimal solution.
In order to achieve the technical purpose, the invention provides a plan optimization method for a chemical industry enterprise supply chain, which comprises the following steps:
acquiring initial parameters of a supply chain, and storing the initial parameters of the supply chain to a data warehouse;
determining expected market demands relative to the chemical enterprises according to a self-matching demand prediction algorithm by combining the annual sales data of the chemical enterprises, and correcting the expected time demands based on an optimized statistical prediction algorithm to obtain the corrected market demand quantity;
constructing a supply chain plan optimization model according to the corrected market demand quantity and supply chain initial parameters stored in a data warehouse, setting a plan optimization objective function and constraint conditions in the supply chain plan optimization model, and solving an optimal solution of the plan objective function under the constraint conditions;
different plan optimization scheduling schemes are built, initial parameters of a supply chain are obtained from a data warehouse, at least two sets of plan schemes are obtained by adjusting input values of a plan optimization model of the supply chain according to preset step lengths, the profit corresponding to each plan scheme is calculated, and the plan scheme corresponding to the highest profit is selected to optimize related parameters of the enterprise supply chain.
Optionally, the supply chain initial parameters include:
supply chain initial parameters and initialization parameters;
the supply chain initial parameters include: a prediction period, a prediction level;
the initialization parameters include: the unit raw material purchase price, the unit raw material inventory price, the unit product inventory price, the unit supplier raw material transportation price, the unit customer required product transportation price, the unit device produced product operation price, the minimum and maximum raw material purchase, the minimum and maximum product sales meeting the requirements, the minimum and maximum inventory limits of various required products and raw materials, the unit consumption of each raw material corresponding to each product, the minimum processing capacity and the maximum processing capacity of each set of production device, the initial storage and the in-transit storage of various required products and raw materials, and the weight of an objective function.
Optionally, the determining, by combining the historical sales data of the chemical enterprises, the expected market demand relative to the chemical enterprises according to the self-matching demand prediction algorithm, and modifying the demand for the expected duration based on the optimized statistical prediction algorithm to obtain the modified market demand quantity includes:
extracting historical sales data of chemical enterprises from supply chain parameters, determining a demand mode according to the historical sales data, and recombining prediction samples according to prediction levels corresponding to different products in specific contents in the demand mode;
based on the recombined prediction samples, determining an expected duration demand model relative to the chemical industry enterprises according to a self-matching demand prediction algorithm,
wherein X is X1,x2,x3… are predicted samples of different products at selected levels,for the demand prediction results of different products under different prediction algorithm models, i is 1,2,3 …, k is 1,2,3 … are the ith product, the kth prediction algorithm model and Fi,k(Xi) A kth predictive algorithm model for the ith product;
determining an expected duration requirement relative to the chemical industry enterprise according to the expected duration requirement model;
comparing output errors of prediction models of different requirements of various products, and matching to obtain a model with the best prediction precision as a final prediction model of the product;
ydemand,i＝Fi,best(Xi)
wherein, Yi,kFor practical requirements, Fi,bestFor the final demand prediction model, y, obtained from the matching correspondencesdemand,iCalculating a product demand result for the self-matching;
the revised market demand quantity is determined based on a final prediction model of the product.
Optionally, the constructing a supply chain plan optimization model according to the obtained corrected market demand quantity and the supply chain initial parameters stored in the data warehouse, where a plan optimization objective function and constraint conditions are set in the supply chain plan optimization model, includes:
acquiring a data warehouse including the device operating rate, a chemical device material list, a raw material purchase amount, a stock amount and a delivery amount from the data warehouse, and constructing a supply chain plan optimization model by combining the corrected duration demand amount;
the supply chain plan optimization model is provided with a plan optimization objective function for maximizing the overall economic profit of the supply chain on the premise of meeting the demand plan of the chemical products
Wherein,
wherein, in the planning optimization objective function, OBJ is a minimization objective function,
the first item of the objective function is the reciprocal of the maximum economic profit objective calculation, and the second item is a demand meeting objective; COSTbuy,COSTinv,COSTtran,COSTopr,COSTfixed,allRespectively the purchase cost of the raw materials,Raw material/product inventory costs, logistics distribution costs, plant operating costs, fixed costs; pdemand,iSales price for unit demand product;
Pbuy,j,Pinv,i,Pinv,j,Ptran,js,Ptran,ic,Popr,imthe unit raw material purchase price, the unit raw material inventory price, the unit product inventory price, the unit supplier raw material transportation price, the unit customer required product transportation price and the unit device production product operation price are obtained;
the quantity of various products required, the quantity of various products required actually met, the quantity of various raw materials purchased, the quantity of various product stocks, the quantity of various raw material stocks, the quantity of each product delivered to each customer, the quantity of each raw material provided by each supplier, and the quantity of each product actually produced by each set of equipment, which are respectively predicted for the demand plan;
1,2,3 …, 1,2,3 …, 1,2,3 …, 1,2,3 …, 1,2,3 …, wherein m is 1,2,3 … are respectively the ith product, the jth raw material, the s th supplier, the c th customer and the m th set of production device; w is a1,wiThe weights of the two terms of the objective function are respectively.
Optionally, the constraint condition includes:
the raw material purchasing constraint is
ybuy,j,min≤ybuy,j≤ybuy,j,max，
Wherein, ybuy,j,min,ybuy,j,maxRespectively purchasing minimum quantity and maximum quantity for each raw material;
the product sales demand is constrained to
Wherein, ydemand,i,min,ydemand,i,maxRespectively selling a minimum amount and a maximum amount for each product meeting the demand;
the device production capacity is constrained to
ycap,m,min≤ycap,m≤ycap,m,max，
Wherein, ycap,m,ycap,m,min,ycap,m,maxRespectively the normal processing capacity, the minimum processing capacity and the maximum processing capacity of each set of production device, yprod,iThe production quantity of each product;
the material consumption of the device is restricted as
Wherein, ycons,ij,aijConsumption and unit consumption of each raw material for producing each product, aijCan be obtained from a chemical product bill of material (BOM) in ERP; y iscons,jThe total amount consumed to produce each product for each raw material;
the raw materials/products inventory constraint is
yfinal,j＝yini,j+yin-transit,j+ybuy,j-ycons,j
yinv,j＝yfinal,j+ycons,j
yinv,i,min≤yinv,i≤yinv,i,max
yinv,j,min≤yinv,j≤yinv,j,max，
Wherein, yini,i,yfinal,i,yin-transit,iRespectively the initial inventory, the end-of-term inventory, the in-transit inventory and the y of various required productsini,j,yfinal,j,yin-transit,j,ycons,jRespectively the initial inventory, the end inventory, the in-transit inventory, the production consumption and the y of various raw materialsinv,i,min,yinv,i,max,yinv,j,min,yinv,j,maxMinimum inventory limits, maximum inventory limits for various desired products and raw materials; the consumption and the unit consumption of each raw material corresponding to each product are produced, the normal processing capacity of each set of production device, the minimum processing capacity of the device, the maximum processing capacity of the device, the minimum sales amount of each product meeting the demand and the initial inventory amount, the final inventory amount and the in-transit inventory amount of each product meeting the demand are respectively obtained.
The technical scheme provided by the invention has the beneficial effects that:
by balancing supply and demand, a plan scheme of economic optimization solution can be provided to realize dynamic adjustment of product structure, the accuracy, the performability and the efficiency of plan work can be improved, and information support is provided for production management.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a plan optimization method for a chemical industry enterprise supply chain according to the present invention.
Detailed Description
To make the structure and advantages of the present invention clearer, the structure of the present invention will be further described with reference to the accompanying drawings.
Example one
The invention provides a plan optimization method for a chemical industry enterprise supply chain, as shown in fig. 1, the plan optimization method includes:
11. acquiring initial parameters of a supply chain, and storing the initial parameters of the supply chain to a data warehouse;
12. determining expected market demands relative to the chemical enterprises according to a self-matching demand prediction algorithm by combining the annual sales data of the chemical enterprises, and correcting the expected time demands based on an optimized statistical prediction algorithm to obtain the corrected market demand quantity;
13. constructing a supply chain plan optimization model according to the corrected market demand quantity and supply chain initial parameters stored in a data warehouse, setting a plan optimization objective function and constraint conditions in the supply chain plan optimization model, and solving an optimal solution of the plan objective function under the constraint conditions;
14. different plan optimization scheduling schemes are built, initial parameters of a supply chain are obtained from a data warehouse, at least two sets of plan schemes are obtained by adjusting input values of a plan optimization model of the supply chain according to preset step lengths, the profit corresponding to each plan scheme is calculated, and the plan scheme corresponding to the highest profit is selected to optimize related parameters of the enterprise supply chain.
In implementation, the invention discloses a method for optimizing a supply chain plan of a chemical industry enterprise. The method comprises the steps of establishing self-matching demand prediction algorithm models of different chemical products according to factors such as sales history data, seasonal variation conditions and the like, and assisting planning personnel to form a demand plan by combining hierarchical prediction and sales experience cooperation, so that the demand prediction accuracy is improved. On the basis of obtaining the quantity of market demands, constraints such as purchase, production, inventory and delivery of chemical enterprises are considered, a chemical supply chain plan optimization plan model is constructed, and a plan scheme of linear optimization solution can be provided while supply and demand are balanced, so that dynamic adjustment of a product structure is realized, and the optimal comprehensive cost and the maximized productivity are realized. Based on the optimal solution obtained by the supply chain optimization model under the constraint condition, and in combination with price prediction data and production cost data, a multi-scheme profitability automatic measuring and calculating strategy is constructed, various enterprise economic schemes are measured and calculated by automatically setting input step length, planning personnel and financial personnel are assisted to select a planning scheme with the maximum profitability, and high-level rapid decision making can be assisted. In the subsequent process, the optimized and formulated plan is also subjected to auditing, examining, approving, tracking, adjusting, analyzing and the like, so that the system realizes the visualization and the tracking of the planning process, can improve the accuracy and the performability of the plan and the efficiency of the planning work, and provides information support for production management.
And generating various plan optimization production scheduling schemes by utilizing a multi-scheme profitability automatic measuring and calculating strategy. The method comprises the steps of obtaining data such as production cost, product price and the like from a data warehouse for storing initial parameters of a supply chain, automatically generating a series of different plan schemes by setting fixed or variable step length adjustment model input or various constraints based on a plan optimization model, and calculating different profit levels, so that planning personnel are assisted to select the most appropriate scheme from a plurality of plan scheduling optimization schemes for production scheduling, profit capacity financial analysis and company high-level decision.
Optionally, the supply chain initial parameters include:
supply chain initial parameters and initialization parameters;
the supply chain initial parameters include: a prediction period, a prediction level;
the initialization parameters include: the unit raw material purchase price, the unit raw material inventory price, the unit product inventory price, the unit supplier raw material transportation price, the unit customer required product transportation price, the unit device produced product operation price, the minimum and maximum raw material purchase, the minimum and maximum product sales meeting the requirements, the minimum and maximum inventory limits of various required products and raw materials, the unit consumption of each raw material corresponding to each product, the minimum processing capacity and the maximum processing capacity of each set of production device, the initial storage and the in-transit storage of various required products and raw materials, and the weight of an objective function.
In implementation, the acquisition mode of the initial parameters of the supply chain is to acquire and store the initial parameters to a data warehouse through a data integration engine in modes of ERP, an Oracle database, a report form, manual presetting and the like.
The initialization parameters comprise: prediction period, prediction level. The initialization parameters required by the plan optimization module include: the unit raw material purchase price, the unit raw material inventory price, the unit product inventory price, the unit supplier raw material transportation price, the unit customer required product transportation price, the unit device produced product operation price, the minimum and maximum raw material purchase, the minimum and maximum product sales meeting the requirements, the minimum and maximum inventory limits of various required products and raw materials, the unit consumption of each raw material corresponding to each product, the minimum processing capacity and the maximum processing capacity of each set of production device, the initial storage and the in-transit storage of various required products and raw materials, and the weight of an objective function.
Optionally, the determining, by combining the historical sales data of the chemical enterprises, the expected market demand relative to the chemical enterprises according to the self-matching demand prediction algorithm, and modifying the demand for the expected duration based on the optimized statistical prediction algorithm to obtain the modified market demand quantity includes:
extracting historical sales data of chemical enterprises from supply chain parameters, determining a demand mode according to the historical sales data, and recombining prediction samples according to prediction levels corresponding to different products in specific contents in the demand mode;
based on the recombined prediction samples, determining an expected duration demand model relative to the chemical industry enterprises according to a self-matching demand prediction algorithm,
wherein X is X1,x2,x3… are predicted samples of different products at selected levels,for the demand prediction results of different products under different prediction algorithm models, i is 1,2,3 …, k is 1,2,3 … are the ith product, the kth prediction algorithm model and Fi,k(Xi) A kth predictive algorithm model for the ith product;
determining an expected duration requirement relative to the chemical industry enterprise according to the expected duration requirement model;
comparing output errors of prediction models of different requirements of various products, and obtaining a model with the best prediction precision by self-matching as a final prediction model of the product;
ydemand,i＝Fi,best(Xi)
wherein, Yi,kFor practical requirements, Fi,bestFor the final demand prediction model, y, obtained from the matching correspondencesdemand,iCalculating a product demand result for the self-matching;
the revised market demand quantity is determined based on a final prediction model of the product.
In the implementation, the specific step of determining the market demand quantity of various products of the chemical industry in different future periods is to acquire sales data of various chemical products in the past years from a data warehouse as a sample of selected hierarchical prediction modeling, the generation process of the self-matching demand prediction result is a process of automatically predicting the future demand by using an optimized statistical prediction algorithm after identifying different demand patterns contained in the past data sample, and the market demand quantity in different future periods is determined by combining sales and collaborative prediction correction according to market information.
The different demand patterns comprise seasonal, smooth, periodic fluctuation, event influence and price influence factors.
The statistical prediction algorithm comprises a primary exponential smoothing prediction algorithm, a grey prediction algorithm, a seasonal smoothing algorithm, a decomposition prediction algorithm, a regression prediction algorithm and a neural network prediction algorithm.
The selection level prediction is that different levels of prediction granularity are defined for different products, such as specifications, sales areas, users and the like.
Optionally, the constructing a supply chain plan optimization model according to the obtained corrected market demand quantity and the supply chain initial parameters stored in the data warehouse, where a plan optimization objective function and constraint conditions are set in the supply chain plan optimization model, includes:
acquiring a data warehouse including the device operating rate, a chemical device material list, a raw material purchase amount, a stock amount and a delivery amount from the data warehouse, and constructing a supply chain plan optimization model by combining the corrected duration demand amount;
the supply chain plan optimization model is provided with a plan optimization objective function for maximizing the overall economic profit of the supply chain on the premise of meeting the demand plan of the chemical products
Wherein,
wherein, in the planning optimization objective function, OBJ is a minimization objective function,
the first item of the objective function is the reciprocal of the maximum economic profit objective calculation, and the second item is a demand meeting objective;
COSTbuy,COSTinv,COSTtran,COSTopr,COSTfixed,allrespectively raw material purchase cost, raw material/product inventory cost, logistics distribution cost, device operation cost and fixed cost; pdemand,iSales price for unit demand product;
Pbuy,j,Pinv,i,Pinv,j,Ptran,js,Ptran,ic,Popr,ima is unit raw material purchase price, unit raw material inventory price, unit product inventory price, unit supplier raw material transportation price, unit customer demand product transportation price, unit device production product operation price;
the quantity of various products required, the quantity of various products required actually met, the quantity of various raw materials purchased, the quantity of various product stocks, the quantity of various raw material stocks, the quantity of each product delivered to each customer, the quantity of each raw material provided by each supplier, and the quantity of each product actually produced by each set of equipment, which are respectively predicted for the demand plan;
1,2,3 …, 1,2,3 …, 1,2,3 …, 1,2,3 …, 1,2,3 …, wherein m is 1,2,3 … are respectively the ith product, the jth raw material, the s th supplier, the c th customer and the m th set of production device; w is a1,wiThe weights of the two terms of the objective function are respectively.
In implementation, the supply chain plan optimization model is constructed by considering the product demand generated by the demand prediction module unit and acquiring element information such as the device operating rate, a chemical device bill of materials (BOM), the raw material purchase quantity, the inventory quantity, the delivery quantity and the like from the data warehouse of the data integration module unit, the global cost of the supply chain plan system is minimized, and the economic benefit is maximized. Given a plan optimization objective function and constraint conditions, the generation process of the supply chain plan scheduling optimization scheme is a process of solving an optimal solution meeting the objective function by using a linear programming solver in a feasible domain determined by the constraint conditions.
Optionally, the constraint condition includes:
the raw material purchasing constraint is
ybuy,j,min≤ybuy,j≤ybuy,j,max，
Wherein, ybuy,j,min,ybuy,j,maxRespectively purchasing minimum quantity and maximum quantity for each raw material;
the product sales demand is constrained to
Wherein, ydemand,i,min,ydemand,i,maxRespectively selling a minimum amount and a maximum amount for each product meeting the demand;
the device production capacity is constrained to
ycap,m,min≤ycap,m≤ycap,m,max，
Wherein, ycap,m,ycap,m,min,ycap,m,maxRespectively the normal processing capacity, the minimum processing capacity and the maximum processing capacity of each set of production device, yprod,iThe production quantity of each product;
the material consumption of the device is restricted as
Wherein, ycons,ij,aijConsumption and unit consumption of each raw material for producing each product, aijCan be made in ERPAcquiring a chemical product bill of material (BOM); y iscons,jThe total amount consumed to produce each product for each raw material;
the raw materials/products inventory constraint is
yfinal,j＝yini,j+yin-transit,j+ybuy,j-ycons,j
yinv,j＝yfinal,j+ycons,j
yinv,i,min≤yinv,i≤yinv,i,max
yinv,j,min≤yinv,j≤yinv,j,max，
Wherein, yini,i,yfinal,i,yin-transit,iRespectively the initial inventory, the end-of-term inventory, the in-transit inventory and the y of various required productsini,j,yfinal,j,yin-transit,j,ycons,jRespectively the initial inventory, the end inventory, the in-transit inventory, the production consumption and the y of various raw materialsinv,i,min,yinv,i,max,yinv,j,min,yinv,j,maxMinimum inventory limits, maximum inventory limits for various desired products and raw materials; the consumption and the unit consumption of each raw material corresponding to each product are produced, the normal processing capacity of each set of production device, the minimum processing capacity of the device, the maximum processing capacity of the device, the minimum sales amount of each product meeting the demand and the initial inventory amount, the final inventory amount and the in-transit inventory amount of each product meeting the demand are respectively obtained.
It should be noted that, in addition to the foregoing steps, the steps of periodically tracking and feeding back the production completion condition, order execution condition, raw material product inventory, product quality, equipment maintenance, and key process information after scheduling and issuing of the supply chain plan can be performed.
The steps are executed, except for a mode of solving an optimal solution of the objective function under a constraint condition according to the content, the method aims to establish functions of planning, auditing, publishing, tracking, analyzing and planning report forms, so that various information such as production completion conditions, order execution conditions, raw material product inventory, product quality, equipment maintenance, key process flows and the like after plan optimization scheduling of the supply chain is regularly tracked and fed back, the completion rate of the supply chain plan is ensured, and the enterprise is helped to improve the plan management capability of the supply chain.
In addition, after step 12 is executed, the following steps may also be executed:
and extracting the obtained corrected market demand quantity, the optimal solution of the planning objective function under the constraint condition, the profit corresponding to each set of planning scheme, the production completion condition after the supply chain planning optimization scheduling is issued, the order execution condition, the raw material product inventory, the product quality, the equipment maintenance and the key process flow information from a data warehouse of the data integration module unit, and performing multi-directional visual centralized comprehensive display in a webpage integration mode.
In the above steps, the product demand prediction information generated by the self-matching demand prediction module unit, the purchase, production, inventory and logistics information generated by the plan optimization calculation module unit in an optimized manner, the multiple scheme comparison information measured and calculated by the multi-scheme profitability automatic measurement and calculation module unit, and the plan tracking, plan analysis and report information are extracted from the data warehouse of the data integration module unit and are displayed in a multi-aspect visual centralized and comprehensive manner in a web page integration manner, so that all levels of staff in an enterprise can better master the comprehensive information of the supply chain plan.
The invention provides a plan optimization method for a chemical industry enterprise supply chain, which comprises the following steps: acquiring initial parameters of a supply chain, and storing the initial parameters of the supply chain to a data warehouse; determining expected market demands relative to the chemical enterprises according to a self-matching demand prediction algorithm by combining the annual sales data of the chemical enterprises, and correcting the expected time demands based on an optimized statistical prediction algorithm to obtain the corrected market demand quantity; constructing a supply chain plan optimization model according to the corrected market demand quantity and supply chain initial parameters stored in a data warehouse, setting a plan optimization objective function and constraint conditions in the supply chain plan optimization model, and solving an optimal solution of the plan objective function under the constraint conditions; different plan optimization scheduling schemes are built, initial parameters of a supply chain are obtained from a data warehouse, at least two sets of plan schemes are automatically obtained by adjusting input values of a plan optimization model of the supply chain according to preset step lengths, the profit corresponding to each plan scheme is calculated, and the plan scheme corresponding to the highest profit is selected to optimize related parameters of the enterprise supply chain. By predicting future demands of chemical products and combining hierarchy prediction and sales experience cooperation, planning personnel can be assisted to form a demand plan, and the accuracy of market demand prediction is improved; by balancing supply and demand, a plan scheme of economic optimization solution can be provided, so that the dynamic adjustment of the product structure is realized, the comprehensive cost is optimized, and the productivity is maximized. By utilizing multi-scheme analysis, planning personnel and financial personnel are assisted to select the planning scheme with the maximum profit capacity, and high-level quick decision-making of an enterprise can be assisted. The system can realize visualization and traceability of the supply chain planning process, and can solidify the experience accumulated by experts into the system, thereby improving the accuracy and the performability of the plan and the efficiency of the planning work and providing information support for production management.
The sequence numbers in the above embodiments are merely for description, and do not represent the sequence of the assembly or the use of the components.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A plan optimization method for a chemical industry enterprise supply chain is characterized by comprising the following steps:
acquiring initial parameters of a supply chain, and storing the initial parameters of the supply chain to a data warehouse;
determining expected market demands relative to the chemical enterprises according to a self-matching demand prediction algorithm by combining the annual sales data of the chemical enterprises, and correcting the expected time demands based on an optimized statistical prediction algorithm to obtain the corrected market demand quantity;
constructing a supply chain plan optimization model according to the corrected market demand quantity and supply chain initial parameters stored in a data warehouse, setting a plan optimization objective function and constraint conditions in the supply chain plan optimization model, and solving an optimal solution of the plan objective function under the constraint conditions;
different plan optimization scheduling schemes are built, initial parameters of a supply chain are obtained from a data warehouse, at least two sets of plan schemes are obtained by adjusting input values of a plan optimization model of the supply chain according to preset step lengths, the profit corresponding to each plan scheme is calculated, and the plan scheme corresponding to the highest profit is selected to optimize related parameters of the enterprise supply chain.
2. The method of claim 1, wherein the supply chain initial parameters comprise:
supply chain initial parameters and initialization parameters;
the supply chain initial parameters include: a prediction period, a prediction level;
the initialization parameters include: the unit raw material purchase price, the unit raw material inventory price, the unit product inventory price, the unit supplier raw material transportation price, the unit customer required product transportation price, the unit device produced product operation price, the minimum and maximum raw material purchase, the minimum and maximum product sales meeting the requirements, the minimum and maximum inventory limits of various required products and raw materials, the unit consumption of each raw material corresponding to each product, the minimum processing capacity and the maximum processing capacity of each set of production device, the initial storage and the in-transit storage of various required products and raw materials, and the weight of an objective function.
3. The plan optimization method for the supply chain of the chemical industry enterprise according to claim 1, wherein the step of determining the expected market demand relative to the chemical industry enterprise according to a self-matching demand forecasting algorithm by combining the historical sales data of the chemical industry enterprise, and correcting the demand for the expected duration based on the optimized statistical forecasting algorithm to obtain the corrected market demand quantity comprises the following steps:
extracting historical sales data of chemical enterprises from supply chain parameters, determining a demand mode according to the historical sales data, and recombining prediction samples according to prediction levels corresponding to different products in specific contents in the demand mode;
based on the recombined prediction samples, determining an expected duration demand model relative to the chemical industry enterprises according to a self-matching demand prediction algorithm,
<mrow> <msub> <mover> <mi>Y</mi> <mo>~</mo> </mover> <mrow> <mi>i</mi> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>F</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mi>X</mi> <mo>=</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>x</mi> <mn>3</mn> </msub> <mo>...</mo> </mrow>
wherein X is X1,x2,x3… are predicted samples of different products at selected levels,for the demand prediction results of different products under different prediction algorithm models, i is 1,2,3 …, k is 1,2,3 … are the ith product, the kth prediction algorithm model and Fi,k(Xi) A kth predictive algorithm model for the ith product;
determining an expected duration requirement relative to the chemical industry enterprise according to the expected duration requirement model;
comparing output errors of prediction models of different requirements of various products, and obtaining a model with the best prediction precision by self-matching as a final prediction model of the product;
<mrow> <msub> <mi>F</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>b</mi> <mi>e</mi> <mi>s</mi> <mi>t</mi> </mrow> </msub> <mo>&LeftArrow;</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <mrow> <mo>|</mo> <mrow> <msub> <mover> <mi>Y</mi> <mo>~</mo> </mover> <mrow> <mi>i</mi> <mo>,</mo> <mi>k</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>Y</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>k</mi> </mrow> </msub> </mrow> <mo>|</mo> </mrow> </mrow>
ydemand,i＝Fi,best(Xi)
wherein, Yi,kFor practical requirements, Fi,bestFor the final demand prediction model, y, obtained from the matching correspondencesdemand,iCalculating a product demand result for the self-matching;
the revised market demand quantity is determined based on a final prediction model of the product.
4. The plan optimization method for the supply chain of the chemical industry enterprise according to claim 1, wherein the constructing a supply chain plan optimization model according to the obtained modified market demand quantity and the supply chain initial parameters stored in the data warehouse, and a plan optimization objective function and constraint conditions are set in the supply chain plan optimization model, and the method comprises the following steps:
acquiring a data warehouse including the device operating rate, a chemical device material list, a raw material purchase amount, a stock amount and a delivery amount from the data warehouse, and constructing a supply chain plan optimization model by combining the corrected duration demand amount;
the supply chain plan optimization model is provided with a plan optimization objective function for maximizing the overall economic profit of the supply chain on the premise of meeting the demand plan of the chemical products
Wherein,
<mrow> <msub> <mi>COST</mi> <mrow> <mi>b</mi> <mi>u</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <munder> <mi>&Sigma;</mi> <mi>j</mi> </munder> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>u</mi> <mi>y</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <msub> <mi>y</mi> <mrow> <mi>b</mi> <mi>u</mi> <mi>y</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>COST</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>v</mi> </mrow> </msub> <mo>=</mo> <munder> <mi>&Sigma;</mi> <mi>i</mi> </munder> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>v</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <msub> <mi>y</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>v</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>+</mo> <munder> <mi>&Sigma;</mi> <mi>j</mi> </munder> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>v</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <msub> <mi>y</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>v</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> </mrow>
<mrow> <msub> <mi>COST</mi> <mrow> <mi>t</mi> <mi>r</mi> <mi>a</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <munder> <mo>&Sigma;</mo> <mi>i</mi> </munder> <munder> <mo>&Sigma;</mo> <mi>c</mi> </munder> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mrow> <mi>t</mi> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mo>,</mo> <mi>i</mi> <mi>c</mi> </mrow> </msub> <msub> <mi>y</mi> <mrow> <mi>t</mi> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mo>,</mo> <mi>i</mi> <mi>c</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>+</mo> <munder> <mo>&Sigma;</mo> <mi>j</mi> </munder> <munder> <mo>&Sigma;</mo> <mi>s</mi> </munder> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mrow> <mi>t</mi> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mo>,</mo> <mi>j</mi> <mi>s</mi> </mrow> </msub> <msub> <mi>y</mi> <mrow> <mi>t</mi> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mo>,</mo> <mi>j</mi> <mi>s</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow>
<mrow> <msub> <mi>COST</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <munder> <mo>&Sigma;</mo> <mi>m</mi> </munder> <munder> <mo>&Sigma;</mo> <mi>i</mi> </munder> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mrow> <mi>o</mi> <mi>p</mi> <mi>r</mi> <mo>,</mo> <mi>i</mi> <mi>m</mi> </mrow> </msub> <msub> <mi>y</mi> <mrow> <mi>p</mi> <mi>r</mi> <mi>o</mi> <mi>d</mi> <mo>,</mo> <mi>i</mi> <mi>m</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow>
wherein, in the planning optimization objective function, OBJ is a minimization objective function,
the first item of the objective function is the reciprocal of the maximum economic profit objective calculation, and the second item is a demand meeting objective; COSTbuy,COSTinv,COSTtran,COSTopr,COSTfixed,allRespectively raw material purchase cost, raw material/product inventory cost, logistics distribution cost, device operation cost and fixed cost; pdemand,iSales price for unit demand product;
Pbuy,j,Pinv,i,Pinv,j,Ptran,js,Ptran,ic,Popr,imthe unit raw material purchase price, the unit raw material inventory price, the unit product inventory price, the unit supplier raw material transportation price, the unit customer required product transportation price and the unit device production product operation price are obtained;
ydemand,i,ybuy,j,yinv,i,yinv,j,ytran,ic,ytran,js,yprod,imthe quantity of various products required, the quantity of various products required actually met, the quantity of various raw materials purchased, the quantity of various product stocks, the quantity of various raw material stocks, the quantity of each product delivered to each customer, the quantity of each raw material provided by each supplier, and the quantity of each product actually produced by each set of equipment, which are respectively predicted for the demand plan;
i-1, 2,3 …, j-1, 2,3 …, s-1, 2,3 …, c-1, 2,3 …, m-1, 2,3 … are respectively the ith product, the jth raw material, the sth supplier,the c customer and the m set of production devices; w is a1,wiThe weights of the two terms of the objective function are respectively.
5. The method of claim 4, wherein the constraints comprise:
the raw material purchasing constraint is
ybuy,j,min≤ybuy,j≤ybuy,j,max，
Wherein, ybuy,j,min,ybuy,j,maxRespectively purchasing minimum quantity and maximum quantity for each raw material;
the product sales demand is constrained to
Wherein, ydemand,i,min,ydemand,i,maxRespectively selling a minimum amount and a maximum amount for each product meeting the demand;
the device production capacity is constrained to
<mrow> <msub> <mi>y</mi> <mrow> <mi>c</mi> <mi>a</mi> <mi>p</mi> <mo>,</mo> <mi>m</mi> </mrow> </msub> <mo>=</mo> <munder> <mo>&Sigma;</mo> <mi>i</mi> </munder> <msub> <mi>y</mi> <mrow> <mi>p</mi> <mi>r</mi> <mi>o</mi> <mi>d</mi> <mo>,</mo> <mi>i</mi> <mi>m</mi> </mrow> </msub> </mrow>
<mrow> <msub> <mi>y</mi> <mrow> <mi>p</mi> <mi>r</mi> <mi>o</mi> <mi>d</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>=</mo> <munder> <mo>&Sigma;</mo> <mi>m</mi> </munder> <msub> <mi>y</mi> <mrow> <mi>p</mi> <mi>r</mi> <mi>o</mi> <mi>d</mi> <mo>,</mo> <mi>i</mi> <mi>m</mi> </mrow> </msub> <mo>,</mo> </mrow>
ycap,m,min≤ycap,m≤ycap,m,max，
Wherein, ycap,m,ycap,m,min,ycap,m,maxRespectively the normal processing capacity, the minimum processing capacity and the maximum processing capacity of each set of production device, yprod,iThe production quantity of each product;
the material consumption of the device is restricted as
<mrow> <msub> <mi>y</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>n</mi> <mi>s</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>=</mo> <munder> <mo>&Sigma;</mo> <mi>i</mi> </munder> <msub> <mi>y</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>n</mi> <mi>s</mi> <mo>,</mo> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>,</mo> </mrow>
Wherein, ycons,ij,aijConsumption and unit consumption of each raw material for producing each product, aijCan be obtained from a chemical product bill of material (BOM) in ERP; y iscons,jThe total amount consumed to produce each product for each raw material;
the raw materials/products inventory constraint is
yfinal,j＝yini,j+yin-transit,j+ybuy,j-ycons,j
yinv,j＝yfinal,j+ycons,j
yinv,i,min≤yinv,i≤yinv,i,max
yinv,j,min≤yinv,j≤yinv,j,max，
Wherein, yini,i,yfinal,i,yin-transit,iRespectively the initial inventory, the end-of-term inventory, the in-transit inventory and the y of various required productsini,j,yfinal,j,yin-transit,j,ycons,jRespectively the initial inventory, the end inventory, the in-transit inventory, the production consumption and the y of various raw materialsinv,i,min,yinv,i,max,yinv,j,min,yinv,j,maxMinimum inventory limits, maximum inventory limits for various desired products and raw materials; the consumption and the unit consumption of each raw material corresponding to each product are produced, the normal processing capacity of each set of production device, the minimum processing capacity of the device, the maximum processing capacity of the device, the minimum sales amount of each product meeting the demand and the initial inventory amount, the final inventory amount and the in-transit inventory amount of each product meeting the demand are respectively obtained.
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