CN108108994A - For the plan optimization method of chemical enterprise supply chain - Google Patents

For the plan optimization method of chemical enterprise supply chain Download PDF

Info

Publication number
CN108108994A
CN108108994A CN201711107937.9A CN201711107937A CN108108994A CN 108108994 A CN108108994 A CN 108108994A CN 201711107937 A CN201711107937 A CN 201711107937A CN 108108994 A CN108108994 A CN 108108994A
Authority
CN
China
Prior art keywords
mrow
msub
supply chain
demand
product
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201711107937.9A
Other languages
Chinese (zh)
Inventor
娄海川
苏宏业
吴学华
侯卫锋
马晓华
吴玉成
古勇
荣冈
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang Supcon Software Co ltd
Zhejiang University ZJU
Original Assignee
ZHEJIANG SUPCON SOFTWARE CO Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by ZHEJIANG SUPCON SOFTWARE CO Ltd filed Critical ZHEJIANG SUPCON SOFTWARE CO Ltd
Priority to CN201711107937.9A priority Critical patent/CN108108994A/en
Publication of CN108108994A publication Critical patent/CN108108994A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders

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

For the plan optimization method of chemical enterprise supply chain
Technical field
The invention belongs to optimization method field, more particularly to for chemical enterprise Supply Chain Planner optimization method.
Background technology
The Supply Chain Planner establishment of most domestic chemical enterprise is carried out using artificial experience at present.It is and large and medium-sized to one Chemical enterprise for, product specification is numerous, and operation flow variation is frequent, and it is very multiple manually to work out supply chain planning solution It is miscellaneous.
On the one hand, Supply Chain Planner front end plan of needs personnel need to carry out market prediction based on historical data, usually pre- Survey mode is artificial judgement and summarizes that workload is huge, and no corresponding mathematics model and system auxiliary, prediction deviation is big, easily by mistake Lead supply plan；On the other hand, chemical enterprise Supply Chain Planner needs in practice many information (such as stock, on way, raw material is It is no can arrival on time, raw material supply, logistics transportation information etc.) opaque, asymmetric serious, industrial chemicals, the product equilibrium of supply and demand Difficulty, due to lacking the auxiliary optimization of mathematical model, manually plan is difficult to ensure that global optimum, causes raw material supply, plan, inspection Repair with stock it is uncoordinated the phenomenon that take place frequently, corporate plan completion rate is not fully up to expectations.
The content of the invention
In order to solve shortcoming and defect in the prior art, the present invention provides for pass through build majorized function into And the mode of optimal solution is asked majorized function to determine to the optimum optimization scheme of supply chain based on chemical enterprise supply chain Draw optimization method.
In order to reach above-mentioned technical purpose, the present invention provides the plan optimization method for chemical enterprise supply chain, institutes Plan optimization method is stated, 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, determined according to Self Matching requirement forecasting algorithm opposite with chemical enterprise pre- The phase market demand is modified expected duration demand based on the statistical forecast algorithm after optimization, and obtaining revised market needs Seek quantity；
According to obtained revised market demand quantity and the supply chain initial parameter structure stored into data warehouse Supply Chain Planner Optimized model is built, 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；
Different planning optimization scheduling schemes is built, supply chain initial parameter is obtained from data warehouse, by supply The input value of chain planning optimization model is adjusted by default step-length, is drawn at least two sets of plans, is calculated each plan side The corresponding profit of case chooses profit highest corresponding plans and the relevant parameter of enterprise supply chain is optimized.
Optionally, the supply chain initial parameter includes：
Supply chain initial parameter and initiation parameter；
The supply chain initial parameter includes：Predetermined period, prediction level；
The initiation parameter includes：Unit purchasing of raw materials price, unit material inventory price, unit product stock's valency Lattice, unit supplier raw material transport price, unit customer demand Product transport price, unit device production product operation price, Each materials procurement minimum and maximum, the production marketing minimum and maximum of each meet demand, various requirement products and The minimum inventories limitation of raw material, maximum stock's limitation, produce the unit consumption that each product corresponds to each raw material, often cover process units most Opening inventory amount, the transportation inventory amount of small working ability, device maximum working ability, various requirement products and raw material, target letter Several weights.
Optionally, the combination chemical enterprise sales data over the years, determines and chemical industry according to Self Matching requirement forecasting algorithm The opposite market prospective demand of enterprise is modified expected duration demand based on the statistical forecast algorithm after optimization, is repaiied Market demand quantity after just, including：
The sales data over the years of chemical enterprise is extracted from supply chain parameter, demand mould is determined according to sales data over the years Formula corresponds to the prediction level of different product, forecast sample is recombinated according to demand in the particular content in pattern；
Based on the forecast sample after restructuring, when determining the expection opposite with chemical enterprise according to Self Matching requirement forecasting algorithm Long demand model,
Wherein, X=x1,x2,x3... it is forecast sample of the different product in selected level,It is that different product is pre- in difference Requirement forecasting under method of determining and calculating model is as a result, i=1,2,3 ..., k=1,2,3 ... respectively i-th kind of product, kth kind prediction algorithm Model, Fi,k(Xi) for the kth kind prediction algorithm model of i-th kind of product；
The expection duration demand opposite with chemical enterprise is determined according to expected duration demand model；
Various products different demands prediction model output error is compared, matching obtains the best model of precision of prediction The prediction model final as the product；
ydemand,i=Fi,best(Xi)
Wherein, Yi,kFor actual demand, Fi,bestFor the final Demand Forecast Model that Self Matching corresponds to, ydemand,i The product demand result being calculated for Self Matching；
Revised market demand quantity is determined based on the final prediction model of product.
Optionally, the revised market demand quantity and store the supply chain into data warehouse that the basis obtains Initial parameter builds Supply Chain Planner Optimized model, and planning optimization object function peace treaty is equipped in Supply Chain Planner Optimized model Beam condition, including：
Being obtained from data warehouse includes the device utilization of capacity, chemical plant installations store list, raw material purchase volume, quantity in stock, dispatching Data warehouse including amount combines revised duration quantity required and builds Supply Chain Planner Optimized model；
It is whole that maximization supply chain on the premise of meeting chemical products plan of needs is equipped in Supply Chain Planner Optimized model The planning optimization object function of body economic profit
Wherein,
Wherein, in planning optimization object function, OBJ is minimum object function,
Object function Section 1 is the inverse that maximum economic profit target calculates, and Section 2 is need satisfaction target； COSTbuy,COSTinv,COSTtran,COSTopr,COSTfixed,allRespectively purchasing of raw materials cost, raw material/product inventory cost, Logistics distribution cost, device operating cost, fixed cost；Pdemand,iFor unit requirement product selling price；
Pbuy,j,Pinv,i,Pinv,j,Ptran,js,Ptran,ic,Popr,imFor unit purchasing of raw materials price, unit material inventory valency Lattice, unit product inventory prices, unit supplier raw material transport price, unit customer demand Product transport price, unit device Produce product operation price；
Respectively demand The various products quantity required of plan forecast, the various products quantity required actually met, the various raw material quantity, various of purchase Product inventory quantity, various material inventory quantity, the quantity of each product delivery to each client, each raw material are by each supply The quantity of the quantity that business provides, each product per covering device actual production；
I=1,2,3 ..., j=1,2,3 ..., s=1,2,3 ..., c=1,2,3 ..., m=1,2,3 ... are respectively i-th kind of production Product, jth kind raw material, s-th of supplier, c-th of client, m set process units；w1,wiThe respectively power of object function two Weight.
Optionally, the constraints includes：
The purchasing of raw materials is constrained to
ybuy,j,min≤ybuy,j≤ybuy,j,max,
Wherein, ybuy,j,min,ybuy,j,maxRespectively each materials procurement minimum and maximum；
Production marketing constraint of demand is
Wherein, ydemand,i,min,ydemand,i,maxThe production marketing minimum and maximum of respectively each meet demand；
Device produces capacity constraints
ycap,m,min≤ycap,m≤yCap, m, max,
Wherein, ycap,m,ycap,m,min,ycap,m,maxRespectively often cover process units normal process ability, device minimum process Ability, device maximum working ability, yprod,iFor the production quantity of each product；
The consumption of device production material is constrained to
Wherein, ycons,ij,aijRespectively produce consumption and unit consumption that each product corresponds to each raw material, aijIt can be by ERP Middle chemical products bill of materials (BOM) obtains；ycons,jThe total amount of each product consumption is produced for each raw material；
Raw material/product inventory is constrained to
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,iThe opening inventory amounts of respectively various requirement products, closing inventory, Transportation inventory amount, yini,j,yfinal,j,yin-transit,j,ycons,jThe opening inventory amounts of respectively various raw materials, closing inventory, Transportation inventory amount, production consumption, yinv,i,min,yinv,i,max,yinv,j,min,yinv,j,maxFor various requirement products and raw material most Small stock's limitation, maximum stock's limitation；Consumption and unit consumption that each product corresponds to each raw material are produced, is often covering process units just Normal working ability, device minimum process ability, device maximum working ability, the production marketing minimum and maximum of each meet demand Measure the opening inventory amount, closing inventory, transportation inventory amount of various requirement products.
The advantageous effect that technical solution provided by the invention is brought is：
The plans solved by the way that an economic optimization while supply and demand is balanced, can be provided, to realize production The dynamic adjustment of product structure, can improve the accuracy, enforceability and the efficiency of planning work of plan, be provided for production management Information support.
Description of the drawings
It, below will be to attached drawing needed in embodiment description in order to illustrate more clearly of technical scheme It is briefly described, it should be apparent that, the accompanying drawings in the following description is only some embodiments of the present invention, general for this field For logical technical staff, without creative efforts, other attached drawings are can also be obtained according to these attached drawings.
Fig. 1 has been provided by the invention for the flow diagram of the plan optimization method of chemical enterprise supply chain.
Specific embodiment
To make the structure of the present invention and advantage clearer, the structure of the present invention is made further below in conjunction with attached drawing Description.
Embodiment one
The present invention provides in order to reach above-mentioned technical purpose, the present invention provides the plans for chemical enterprise supply chain Optimization method, as shown in Figure 1, the plan optimization method, including：
11st, supply chain initial parameter is obtained, supply chain initial parameter is stored to data warehouse；
12nd, with reference to chemical enterprise sales data over the years, determined according to Self Matching requirement forecasting algorithm opposite with chemical enterprise Market prospective demand, expected duration demand is modified based on the statistical forecast algorithm after optimization, obtains revised city Field quantity required；
13rd, according to obtained revised market demand quantity and the supply chain initial parameter stored into data warehouse Supply Chain Planner Optimized model is built, 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；
14th, different planning optimization scheduling schemes is built, supply chain initial parameter is obtained from data warehouse, by supplying The input value of chain planning optimization model is answered to be adjusted by default step-length, at least two sets of plans is drawn, calculates each plan The corresponding profit of scheme chooses profit highest corresponding plans and the relevant parameter of enterprise supply chain is optimized.
In force, the invention discloses a kind of chemical enterprise Supply Chain Planner optimization methods.The present invention is included according to pin The factors such as historical data, seasonal variations situation are sold, the Self Matching requirement forecasting algorithm model of different chemical products is established, ties simultaneously Level prediction and marketing experience collaboration are closed, auxiliary staff planners form plan of needs, improve requirement forecasting accuracy.Obtaining city On the basis of the quantity required of field, the constraints such as buying, production, stock and the dispatching of chemical enterprise are considered, build chemical supply chain meter Optimal planning model is drawn, while supply and demand is balanced, the plans that a linear optimization solves can be provided, to realize The dynamic adjustment of product structure, maximizes comprehensive Optimum cost and production capacity.Based on supply chain optimization model under constraints Obtained optimal solution, and price prediction data, production cost data are combined, structure multi-scheme profitability automatic measurement ＆ calculation strategy leads to It crosses automatic set and inputs the various business economic schemes of step-length measuring and calculating, staff planners and financial staff is assisted to select profitability maximum Plans, and high-rise high-speed decision can be aided in.In the follow-up process also to optimization formulate plan carry out examination ＆ verification examination ＆ approval, with Track, adjustment and analysis etc. make system realize a plan the visualization of process, traceable, can improve the accuracy, executable of plan Property and the efficiency of planning work, information support is provided for production management.
Utilize a variety of planning optimization scheduling schemes of multi-scheme profitability automatic measurement ＆ calculation strategy generating.At the beginning of storage supply chain Production cost, the data such as product price are obtained in the data warehouse of beginning parameter, based on planning optimization model, by set it is fixed or The step-length of variation adjusts mode input or various constraints to automatically generate a series of different plans, calculates different be full of It is sharp horizontal, so as to assist staff planners selected in numerous plan production optimization schemes a most suitable sets of plan for produce row Production, profitability financial analysis and company executives decision-making.
Optionally, the supply chain initial parameter includes：
Supply chain initial parameter and initiation parameter；
The supply chain initial parameter includes：Predetermined period, prediction level；
The initiation parameter includes：Unit purchasing of raw materials price, unit material inventory price, unit product stock's valency Lattice, unit supplier raw material transport price, unit customer demand Product transport price, unit device production product operation price, Each materials procurement minimum and maximum, the production marketing minimum and maximum of each meet demand, various requirement products and The minimum inventories limitation of raw material, maximum stock's limitation, produce the unit consumption that each product corresponds to each raw material, often cover process units most Opening inventory amount, the transportation inventory amount of small working ability, device maximum working ability, various requirement products and raw material, target letter Several weights.
In force, the acquisition modes of supply chain initial parameter are with ERP, Oracle data by data integration engine The modes such as storehouse, report, manual presetting are obtained and stored to data warehouse.
Wherein initiation parameter includes：Predetermined period, prediction level.The initiation parameter bag that plan optimization module needs It includes：Unit purchasing of raw materials price, unit material inventory price, unit product inventory prices, unit supplier raw material transport price, Unit customer demand Product transport price, unit device production product operation price, each materials procurement minimum and maximum, The minimum inventories limitation of the production marketing minimum and maximum of each meet demand, various requirement products and raw material, maximum stock Limitation, produces the unit consumption that each product corresponds to each raw material, often covers the maximum processing energy of process units minimum process ability, device Opening inventory amount, the transportation inventory amount of power, various requirement products and raw material, the weight of object function.
Optionally, the combination chemical enterprise sales data over the years, determines and chemical industry according to Self Matching requirement forecasting algorithm The opposite market prospective demand of enterprise is modified expected duration demand based on the statistical forecast algorithm after optimization, is repaiied Market demand quantity after just, including：
The sales data over the years of chemical enterprise is extracted from supply chain parameter, demand mould is determined according to sales data over the years Formula corresponds to the prediction level of different product, forecast sample is recombinated according to demand in the particular content in pattern；
Based on the forecast sample after restructuring, when determining the expection opposite with chemical enterprise according to Self Matching requirement forecasting algorithm Long demand model,
Wherein, X=x1,x2,x3... it is forecast sample of the different product in selected level,It is that different product is pre- in difference Requirement forecasting under method of determining and calculating model is as a result, i=1,2,3 ..., k=1,2,3 ... respectively i-th kind of product, kth kind prediction algorithm Model, Fi,k(Xi) for the kth kind prediction algorithm model of i-th kind of product；
The expection duration demand opposite with chemical enterprise is determined according to expected duration demand model；
Various products different demands prediction model output error is compared, Self Matching obtains the best mould of precision of prediction The type prediction model final as the product；
ydemand,i=Fi,best(Xi)
Wherein, Yi,kFor actual demand, Fi,bestFor the final Demand Forecast Model that Self Matching corresponds to, ydemand,i The product demand result being calculated for Self Matching；
Revised market demand quantity is determined based on the final prediction model of product.
In force, determine that chemical enterprise various products are concretely comprised the following steps in the market demand quantity of following different cycles Various chemical products are obtained from data warehouse and go over the sample of sales data over the years as the prediction modeling of selected level, from Generating process with requirement forecasting result is after by recognizing the different demands pattern included in past data sample, certainly The process of the dynamic statistical forecast algorithm prediction tomorrow requirement using optimization, is repaiied in combination with sale according to market information collaborative forecasting Just, the market demand quantity of following different cycles is determined.
The different demands pattern, including seasonality, flatness, cyclic fluctuation, event influence, price because Element.
The statistical forecast algorithm, is smoothly calculated including single exponential smoothing prediction algorithm, Grey Prediction Algorithm, season Method decomposes prediction algorithm, regression forecasting algorithm, neural network prediction algorithm.
The selection level prediction defines the prediction granularity of different levels, such as specification, sale for different product Region, user etc..
Optionally, the revised market demand quantity and store the supply chain into data warehouse that the basis obtains Initial parameter builds Supply Chain Planner Optimized model, and planning optimization object function peace treaty is equipped in Supply Chain Planner Optimized model Beam condition, including：
Being obtained from data warehouse includes the device utilization of capacity, chemical plant installations store list, raw material purchase volume, quantity in stock, dispatching Data warehouse including amount combines revised duration quantity required and builds Supply Chain Planner Optimized model；
It is whole that maximization supply chain on the premise of meeting chemical products plan of needs is equipped in Supply Chain Planner Optimized model The planning optimization object function of body economic profit
Wherein,
Wherein, in planning optimization object function, OBJ is minimum object function,
Object function Section 1 is the inverse that maximum economic profit target calculates, and Section 2 is need satisfaction target；
COSTbuy,COSTinv,COSTtran,COSTopr,COSTfixed,allRespectively purchasing of raw materials cost, raw material/product library It is saved as sheet, logistics distribution cost, device operating cost, fixed cost；Pdemand,iFor unit requirement product selling price；
Pbuy,j,Pinv,i,Pinv,j,Ptran,js,Ptran,ic,Popr,imA is unit purchasing of raw materials price, unit material inventory valency Lattice, unit product inventory prices, unit supplier raw material transport price, unit customer demand Product transport price, unit device Produce product operation price；
Respectively demand The various products quantity required of plan forecast, the various products quantity required actually met, the various raw material quantity, various of purchase Product inventory quantity, various material inventory quantity, the quantity of each product delivery to each client, each raw material are by each supply The quantity of the quantity that business provides, each product per covering device actual production；
I=1,2,3 ..., j=1,2,3 ..., s=1,2,3 ..., c=1,2,3 ..., m=1,2,3 ... are respectively i-th kind of production Product, jth kind raw material, s-th of supplier, c-th of client, m set process units；w1,wiThe respectively power of object function two Weight.
In force, each product consumption of requirement forecasting modular unit generation is considered and from data integration module The acquisition device utilization of capacity, chemical plant installations store list (BOM tables), raw material purchase volume, quantity in stock, dispensed amounts in the data warehouse of unit Element informations is waited to build Supply Chain Planner Optimized model, and minimize Supply Chain Planner system overall situation cost, are maximized economical Benefit.Given planning optimization object function and constraints, the generating process of Supply Chain Planner production optimization scheme is logical The process for the optimal solution for meeting object function is obtained in the feasible zone that overconstrained condition determines with linear programming for solution device.
Optionally, the constraints includes：
The purchasing of raw materials is constrained to
ybuy,j,min≤ybuy,j≤ybuy,j,max,
Wherein, ybuy,j,min,ybuy,j,maxRespectively each materials procurement minimum and maximum；
Production marketing constraint of demand is
Wherein, ydemand,i,min,ydemand,i,maxThe production marketing minimum and maximum of respectively each meet demand；
Device produces capacity constraints
ycap,m,min≤ycap,m≤ycap,m,max,
Wherein, ycap,m,ycap,m,min,ycap,m,maxRespectively often cover process units normal process ability, device minimum process Ability, device maximum working ability, yprod,iFor the production quantity of each product；
The consumption of device production material is constrained to
Wherein, ycons,ij,aijRespectively produce consumption and unit consumption that each product corresponds to each raw material, aijIt can be by ERP Middle chemical products bill of materials (BOM) obtains；ycons,jThe total amount of each product consumption is produced for each raw material；
Raw material/product inventory is constrained to
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,iThe opening inventory amounts of respectively various requirement products, closing inventory, Transportation inventory amount, yini,j,yfinal,j,yin-transit,j,ycons,jThe opening inventory amounts of respectively various raw materials, closing inventory, Transportation inventory amount, production consumption, yinv,i,min,yinv,i,max,yinv,j,min,yinv,j,maxFor various requirement products and raw material most Small stock's limitation, maximum stock's limitation；Consumption and unit consumption that each product corresponds to each raw material are produced, is often covering process units just Normal working ability, device minimum process ability, device maximum working ability, the production marketing minimum and maximum of each meet demand Measure the opening inventory amount, closing inventory, transportation inventory amount of various requirement products.
It is worth noting that, in addition to abovementioned steps, periodically tracking feedback Supply Chain Planner Optimal Scheduling can also carry out Prodction situation, order implementation status, raw produce stock, product quality, overhaul of the equipments, critical process flow after assigning The step of information.
Why above-mentioned steps are performed, except seeking optimal solution under constraints to object function according to foregoing teachings Mode, it is therefore intended that formulate the function of establishment, examination ＆ verification, issue, tracking, analysis and plan sheet of realizing a plan, so as to periodically with Track feeds back the prodction situation after Supply Chain Planner Optimal Scheduling is assigned, order implementation status, raw produce stock, product matter Many information such as amount, overhaul of the equipments, critical process flow ensure the completion rate of Supply Chain Planner, and enterprise is helped to improve and is supplied Answer chain planning management ability.
In addition, after step 12 is performed, following steps are can also carry out：
By obtained revised market demand quantity, acquire under constraints the optimal solution, every of planned target function Set plans it is corresponding profit, Supply Chain Planner Optimal Scheduling assign after prodction situation, order implementation status, raw material Product inventory, product quality, overhaul of the equipments, critical process procedure information extract simultaneously from the data warehouse of data integration modular unit Multi-faceted visualization is carried out by webpage integration mode and concentrates comprehensive displaying.
In above-mentioned steps, by the product demand predictive information of Self Matching requirement forecasting modular unit generation, planning optimization The buying of computing module unit optimization generation, production, stock, logistics information, multi-scheme profitability automatic measurement ＆ calculation modular unit The kinds of schemes comparative information of measuring and calculating and plan tracking, planning analysis and report messages are from the number of data integration modular unit It is extracted according to warehouse and passes through webpage integration mode and carry out the multi-faceted comprehensive displaying of visualization concentration, so that enterprise personnel at different levels Preferably grasp the integrated information of Supply Chain Planner.
The present invention provides the present invention provides the plan optimization method for chemical enterprise supply chain, including：It obtains and supplies Chain initial parameter is answered, supply chain initial parameter is stored to data warehouse；With reference to chemical enterprise sales data over the years, according to from The market prospective demand opposite with chemical enterprise is determined with requirement forecasting algorithm, based on the statistical forecast algorithm after optimization to expection Duration demand is modified, and obtains revised market demand quantity；According to obtained revised market demand quantity and The supply chain initial parameter structure Supply Chain Planner Optimized model into data warehouse is stored, in Supply Chain Planner Optimized model Equipped with planning optimization object function and constraints, the optimal solution of planned target function is acquired under constraints；Structure is different Planning optimization scheduling scheme, from data warehouse obtain supply chain initial parameter, by Supply Chain Planner Optimized model Input value is adjusted by default step-length, and automatically derived at least two sets of plans calculate the corresponding profit of each plans, The corresponding plans of profit highest are chosen to optimize the relevant parameter of enterprise supply chain.By predicting chemical products future Demand cooperates in combination with level prediction and marketing experience, staff planners can be aided in form plan of needs, improves the market demand The accuracy of prediction；The plans solved by the way that an economic optimization while supply and demand is balanced, can be provided, with reality The dynamic adjustment of existing product structure, maximizes comprehensive Optimum cost and production capacity.Analyzed using multi-scheme, assist staff planners and Financial staff selects the plans of profitability maximum, and can auxiliary enterprises high level high-speed decision.It can also realize system Supply Chain Planner process visualizes, is traceable, while can the experience that expert accumulates be cured to system, can improve plan Accuracy, enforceability and the efficiency of planning work, information support is provided for production management.
Each sequence number in above-described embodiment is for illustration only, does not represent the elder generation during the assembling or use of each component Order afterwards.
The foregoing is merely the embodiment of the present invention, are not intended to limit the invention, all in the spirit and principles in the present invention Within, any modifications, equivalent replacements and improvements are made should all be included in the protection scope of the present invention.

Claims (5)

1. for the plan optimization method of chemical enterprise supply chain, which is characterized in that the plan optimization method, 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, the pre- forward market opposite with chemical enterprise is determined according to Self Matching requirement forecasting algorithm Field demand is modified expected duration demand based on the statistical forecast algorithm after optimization, obtains revised market demand number Amount；
It is supplied according to obtained revised market demand quantity and the supply chain initial parameter stored into data warehouse structure Chain planning optimization model is answered, planning optimization object function and constraints are equipped in Supply Chain Planner Optimized model, is being constrained Under the conditions of acquire the optimal solution of planned target function；
Different planning optimization scheduling schemes is built, supply chain initial parameter is obtained from data warehouse, by supply chain meter The input value for drawing Optimized model is adjusted by default step-length, is drawn at least two sets of plans, is calculated each plans pair The profit answered chooses profit highest corresponding plans and the relevant parameter of enterprise supply chain is optimized.
2. the plan optimization method according to claim 1 for chemical enterprise supply chain, which is characterized in that the supply Chain initial parameter includes：
Supply chain initial parameter and initiation parameter；
The supply chain initial parameter includes：Predetermined period, prediction level；
The initiation parameter includes：Unit purchasing of raw materials price, unit material inventory price, unit product inventory prices, list Position supplier raw material transport price, unit customer demand Product transport price, unit device production product operation price, each former material Material buying minimum and maximum, the production marketing minimum and maximum of each meet demand, various requirement products and raw material Minimum inventories limitation, maximum stock's limitation, produce the unit consumption that each product corresponds to each raw material, often cover process units minimum process Opening inventory amount, the transportation inventory amount of ability, device maximum working ability, various requirement products and raw material, the power of object function Weight.
3. the plan optimization method according to claim 1 for chemical enterprise supply chain, which is characterized in that the combination Chemical enterprise sales data over the years determines the market prospective demand opposite with chemical enterprise according to Self Matching requirement forecasting algorithm, Expected duration demand is modified based on the statistical forecast algorithm after optimization, obtains revised market demand quantity, including：
The sales data over the years of chemical enterprise is extracted from supply chain parameter, demand model, root are determined according to sales data over the years According to the prediction level that different product is corresponded in the particular content in demand model, forecast sample is recombinated；
Based on the forecast sample after restructuring, the expection duration need opposite with chemical enterprise are determined according to Self Matching requirement forecasting algorithm Modulus type,
<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=x1,x2,x3... it is forecast sample of the different product in selected level,It is that different product is calculated in advance different Requirement forecasting under method model is as a result, i=1,2,3 ..., k=1,2,3 ... respectively i-th kind of product, kth kind prediction algorithm mould Type, Fi,k(Xi) for the kth kind prediction algorithm model of i-th kind of product；
The expection duration demand opposite with chemical enterprise is determined according to expected duration demand model；
Various products different demands prediction model output error is compared, Self Matching obtains the best model of precision of prediction and makees For the prediction model that the product is final；
<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>&amp;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 actual demand, Fi,bestFor the final Demand Forecast Model that Self Matching corresponds to, ydemand,iFor certainly The product demand result that matching primitives obtain；
Revised market demand quantity is determined based on the final prediction model of product.
4. the plan optimization method according to claim 1 for chemical enterprise supply chain, which is characterized in that the basis Obtained revised market demand quantity and the supply chain initial parameter stored into data warehouse structure Supply Chain Planner Optimized model is equipped with planning optimization object function and constraints in Supply Chain Planner Optimized model, including：
Being obtained from data warehouse, which includes the device utilization of capacity, chemical plant installations store list, raw material purchase volume, quantity in stock, dispensed amounts, exists Interior data warehouse combines revised duration quantity required and builds Supply Chain Planner Optimized model；
Maximization supply chain on the premise of meeting chemical products plan of needs is equipped in Supply Chain Planner Optimized model integrally to pass through The planning optimization object function for profit of helping
Wherein,
<mrow> <msub> <mi>COST</mi> <mrow> <mi>b</mi> <mi>u</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <munder> <mi>&amp;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>&amp;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>&amp;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>&amp;Sigma;</mo> <mi>i</mi> </munder> <munder> <mo>&amp;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>&amp;Sigma;</mo> <mi>j</mi> </munder> <munder> <mo>&amp;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>&amp;Sigma;</mo> <mi>m</mi> </munder> <munder> <mo>&amp;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 planning optimization object function, OBJ is minimum object function,
Object function Section 1 is the inverse that maximum economic profit target calculates, and Section 2 is need satisfaction target；COSTbuy, COSTinv,COSTtran,COSTopr,COSTfixed,allRespectively purchasing of raw materials cost, raw material/product inventory cost, logistics distribution Cost, device operating cost, fixed cost；Pdemand,iFor unit requirement product selling price；
Pbuy,j,Pinv,i,Pinv,j,Ptran,js,Ptran,ic,Popr,imFor unit purchasing of raw materials price, unit material inventory price, list Position product inventory price, unit supplier raw material transport price, unit customer demand Product transport price, unit device production production Product operate price；
ydemand,i,ybuy,j,yinv,i,yinv,j,ytran,ic,ytran,js,yprod,imRespectively plan of needs prediction is each Kind product demand quantity, the various products quantity required actually met, various raw material quantity, the various products inventory of purchase The number that amount, various material inventory quantity, the quantity of each product delivery to each client, each raw material are provided by each supplier It measures, the quantity of each product of every covering device actual production；
I=1,2,3 ..., j=1,2,3 ..., s=1,2,3 ..., c=1,2,3 ..., m=1,2,3 ... respectively i-th kind of product, jth Kind raw material, s-th of supplier, c-th of client, m set process units；w1,wiThe respectively weight of object function two.
5. the plan optimization method according to claim 4 for chemical enterprise supply chain, which is characterized in that the constraint Condition includes：
The purchasing of raw materials is constrained to
ybuy,j,min≤ybuy,j≤ybuy,j,max,
Wherein, ybuy,j,min,ybuy,j,maxRespectively each materials procurement minimum and maximum；
Production marketing constraint of demand is
Wherein, ydemand,i,min,ydemand,i,maxThe production marketing minimum and maximum of respectively each meet demand；
Device produces capacity constraints
<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>&amp;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>&amp;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 often cover process units normal process ability, device minimum process ability, Device maximum working ability, yprod,iFor the production quantity of each product；
The consumption of device production material is constrained to
<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>&amp;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,aijRespectively produce consumption and unit consumption that each product corresponds to each raw material, aijIt can be by changing in ERP Chemical product bill of materials (BOM) obtains；ycons,jThe total amount of each product consumption is produced for each raw material；
Raw material/product inventory is constrained to
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,iThe opening inventory amounts of respectively various requirement products, closing inventory, on way Quantity in stock, yini,j,yfinal,j,yin-transit,j,ycons,jThe opening inventory amounts of respectively various raw materials, closing inventory, on way Quantity in stock, production consumption, yinv,i,min,yinv,i,max,yinv,j,min,yinv,j,maxFor the minimum storehouse of various requirement products and raw material Deposit limitation, maximum stock's limitation；Consumption and unit consumption that each product corresponds to each raw material are produced, process units is often covered and normally adds Work ability, device minimum process ability, device maximum working ability, the production marketing minimum and maximum of each meet demand are each Opening inventory amount, closing inventory, the transportation inventory amount of kind requirement product.
CN201711107937.9A 2017-11-10 2017-11-10 For the plan optimization method of chemical enterprise supply chain Pending CN108108994A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711107937.9A CN108108994A (en) 2017-11-10 2017-11-10 For the plan optimization method of chemical enterprise supply chain

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711107937.9A CN108108994A (en) 2017-11-10 2017-11-10 For the plan optimization method of chemical enterprise supply chain

Publications (1)

Publication Number Publication Date
CN108108994A true CN108108994A (en) 2018-06-01

Family

ID=62206831

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711107937.9A Pending CN108108994A (en) 2017-11-10 2017-11-10 For the plan optimization method of chemical enterprise supply chain

Country Status (1)

Country Link
CN (1) CN108108994A (en)

Cited By (29)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108985495A (en) * 2018-06-26 2018-12-11 广州视源电子科技股份有限公司 Material procurement method of planning and system
CN109086998A (en) * 2018-08-02 2018-12-25 东莞尊荣鞋业有限公司 A kind of scheduled production method based on cold-gluing shoes automated production
CN109740624A (en) * 2018-11-22 2019-05-10 重庆邮电大学 A kind of logistics supply chain needing forecasting method based on big data
CN109767032A (en) * 2018-12-24 2019-05-17 北京航天智造科技发展有限公司 A kind of business finance operation digital management optimization system based on data analysis
CN109886527A (en) * 2018-12-28 2019-06-14 国网天津市电力公司电力科学研究院 Electric energy metering verification production of intelligent scheduling method and intelligent program system based on branch and bound algorithms
CN110189040A (en) * 2019-06-04 2019-08-30 合肥博耀信息技术有限公司 A kind of wisdom production scheduled production algorithm
CN110222900A (en) * 2019-06-12 2019-09-10 行小膳科技(杭州)有限公司 A kind of quinoa polygamy side meal replacement powder raw materials inventory procurement management system
CN110490529A (en) * 2019-08-29 2019-11-22 浪潮软件集团有限公司 A kind of cigarette distribution management method and system
CN110659762A (en) * 2018-06-29 2020-01-07 天津宝钢钢材配送有限公司 Stamping scheduling planning method based on industrial 4.0 transverse integrated distribution mode
CN110827102A (en) * 2018-08-07 2020-02-21 北京京东尚科信息技术有限公司 Method and device for adjusting goods price ratio
CN110825045A (en) * 2019-11-05 2020-02-21 青岛大学 Method for improving switching efficiency of multi-device multi-product processing schemes
CN110826928A (en) * 2019-11-12 2020-02-21 山东怡之家智能科技有限公司 ERP inventory optimization analysis method and system based on big data
CN110866635A (en) * 2019-11-05 2020-03-06 青岛大学 Method for improving switching prediction precision of device processing scheme
CN111027895A (en) * 2019-05-16 2020-04-17 珠海随变科技有限公司 Stock prediction and behavior data collection method, apparatus, device and medium for commodity
CN111292044A (en) * 2020-02-28 2020-06-16 雅砻江流域水电开发有限公司 Multi-level inventory control method and system for engineering materials
CN111507767A (en) * 2020-04-17 2020-08-07 无锡雪浪数制科技有限公司 Iron and steel raw material purchasing and supplying optimization method
CN112396374A (en) * 2020-11-17 2021-02-23 山东财经大学 Inventory optimization management system and method for dairy product supply chain system under uncertain environment
CN112396322A (en) * 2020-11-18 2021-02-23 广东赛意信息科技有限公司 Production plan-based process technology unit productivity assessment method and system
CN112651561A (en) * 2020-12-28 2021-04-13 车主邦（北京）科技有限公司 Data analysis method and device for supply capacity
CN112712351A (en) * 2021-01-19 2021-04-27 南通大学 Chemical enterprise electrical equipment intermittent process operation management method and device
CN112766981A (en) * 2020-12-30 2021-05-07 国网英大国际控股集团有限公司 Business circle tree construction method and system based on machine learning
CN113505908A (en) * 2021-05-01 2021-10-15 合肥食里挑一网络科技有限公司 Dynamic inventory optimization method
CN113673885A (en) * 2021-08-25 2021-11-19 浙江中控技术股份有限公司 Method and system for determining scheduling plan
CN113724053A (en) * 2021-09-09 2021-11-30 内江师范学院 Agricultural product supply chain coordination method and system
WO2022047696A1 (en) * 2020-09-03 2022-03-10 Boe Technology Group Co., Ltd. Intelligent management system, intelligent management method, and computer-program product
CN116029759A (en) * 2023-01-31 2023-04-28 砼联数字科技有限公司 Dynamic intelligent optimization method and system for concrete production industry chain
WO2023179386A1 (en) * 2022-03-22 2023-09-28 阿里巴巴（中国）有限公司 Data processing method, device, storage medium, and program product
CN116934177A (en) * 2023-09-14 2023-10-24 中交二公局东萌工程有限公司 Enterprise production task management data tracking analysis processing system
CN117726245A (en) * 2024-02-09 2024-03-19 爱彼希科技(北京)有限公司 Supply chain planning method

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030158611A1 (en) * 2001-08-06 2003-08-21 Gideon Weiss Control of items in a complex system by using fluid models and solving continuous linear programs
CN101441466A (en) * 2008-12-25 2009-05-27 浙江工业大学 Control method of polypropylene multiple-grade production plan based on forecast control
CN101493906A (en) * 2007-12-19 2009-07-29 国际商业机器公司 Method and structure for risk-based resource planning for configurable products

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030158611A1 (en) * 2001-08-06 2003-08-21 Gideon Weiss Control of items in a complex system by using fluid models and solving continuous linear programs
CN101493906A (en) * 2007-12-19 2009-07-29 国际商业机器公司 Method and structure for risk-based resource planning for configurable products
CN101441466A (en) * 2008-12-25 2009-05-27 浙江工业大学 Control method of polypropylene multiple-grade production plan based on forecast control

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
王金旺: ""供应链环境下市场需求预测体系研究"", 《中国优秀硕士学位论文全文数据库经济与管理科学辑》 *
舒彤: ""供应链协同的供应商选择与销售预测"", 《中国优秀博士学位论文全文数据库经济与管理科学辑》 *

Cited By (39)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108985495A (en) * 2018-06-26 2018-12-11 广州视源电子科技股份有限公司 Material procurement method of planning and system
CN108985495B (en) * 2018-06-26 2021-04-13 广州视源电子科技股份有限公司 Material purchasing planning method and system
CN110659762A (en) * 2018-06-29 2020-01-07 天津宝钢钢材配送有限公司 Stamping scheduling planning method based on industrial 4.0 transverse integrated distribution mode
CN109086998A (en) * 2018-08-02 2018-12-25 东莞尊荣鞋业有限公司 A kind of scheduled production method based on cold-gluing shoes automated production
CN110827102A (en) * 2018-08-07 2020-02-21 北京京东尚科信息技术有限公司 Method and device for adjusting goods price ratio
CN109740624B (en) * 2018-11-22 2022-12-27 重庆邮电大学 Logistics supply chain demand prediction method based on big data
CN109740624A (en) * 2018-11-22 2019-05-10 重庆邮电大学 A kind of logistics supply chain needing forecasting method based on big data
CN109767032A (en) * 2018-12-24 2019-05-17 北京航天智造科技发展有限公司 A kind of business finance operation digital management optimization system based on data analysis
CN109886527B (en) * 2018-12-28 2023-07-21 国网天津市电力公司电力科学研究院 Intelligent scheduling method and intelligent scheduling system for electric energy metering verification production based on branch definition algorithm
CN109886527A (en) * 2018-12-28 2019-06-14 国网天津市电力公司电力科学研究院 Electric energy metering verification production of intelligent scheduling method and intelligent program system based on branch and bound algorithms
CN111027895A (en) * 2019-05-16 2020-04-17 珠海随变科技有限公司 Stock prediction and behavior data collection method, apparatus, device and medium for commodity
CN110189040A (en) * 2019-06-04 2019-08-30 合肥博耀信息技术有限公司 A kind of wisdom production scheduled production algorithm
CN110222900B (en) * 2019-06-12 2021-08-31 中食安泓(广东)健康产业有限公司 Chenopodium quinoa willd multi-formula meal replacement powder raw material stock purchase management system
CN110222900A (en) * 2019-06-12 2019-09-10 行小膳科技(杭州)有限公司 A kind of quinoa polygamy side meal replacement powder raw materials inventory procurement management system
CN110490529A (en) * 2019-08-29 2019-11-22 浪潮软件集团有限公司 A kind of cigarette distribution management method and system
CN110866635B (en) * 2019-11-05 2024-02-09 青岛大学 Method for improving switching prediction precision of device processing scheme
CN110866635A (en) * 2019-11-05 2020-03-06 青岛大学 Method for improving switching prediction precision of device processing scheme
CN110825045A (en) * 2019-11-05 2020-02-21 青岛大学 Method for improving switching efficiency of multi-device multi-product processing schemes
CN110826928A (en) * 2019-11-12 2020-02-21 山东怡之家智能科技有限公司 ERP inventory optimization analysis method and system based on big data
CN111292044B (en) * 2020-02-28 2023-07-21 雅砻江流域水电开发有限公司 Multistage inventory control method and system for engineering materials
CN111292044A (en) * 2020-02-28 2020-06-16 雅砻江流域水电开发有限公司 Multi-level inventory control method and system for engineering materials
CN111507767A (en) * 2020-04-17 2020-08-07 无锡雪浪数制科技有限公司 Iron and steel raw material purchasing and supplying optimization method
WO2022047696A1 (en) * 2020-09-03 2022-03-10 Boe Technology Group Co., Ltd. Intelligent management system, intelligent management method, and computer-program product
CN112396374A (en) * 2020-11-17 2021-02-23 山东财经大学 Inventory optimization management system and method for dairy product supply chain system under uncertain environment
CN112396322A (en) * 2020-11-18 2021-02-23 广东赛意信息科技有限公司 Production plan-based process technology unit productivity assessment method and system
CN112651561A (en) * 2020-12-28 2021-04-13 车主邦（北京）科技有限公司 Data analysis method and device for supply capacity
CN112766981A (en) * 2020-12-30 2021-05-07 国网英大国际控股集团有限公司 Business circle tree construction method and system based on machine learning
CN112712351A (en) * 2021-01-19 2021-04-27 南通大学 Chemical enterprise electrical equipment intermittent process operation management method and device
CN113505908A (en) * 2021-05-01 2021-10-15 合肥食里挑一网络科技有限公司 Dynamic inventory optimization method
CN113505908B (en) * 2021-05-01 2024-01-12 合肥食里挑一网络科技有限公司 Dynamic inventory optimization method
CN113673885A (en) * 2021-08-25 2021-11-19 浙江中控技术股份有限公司 Method and system for determining scheduling plan
CN113724053B (en) * 2021-09-09 2023-06-23 内江师范学院 Agricultural product supply chain coordination method and system
CN113724053A (en) * 2021-09-09 2021-11-30 内江师范学院 Agricultural product supply chain coordination method and system
WO2023179386A1 (en) * 2022-03-22 2023-09-28 阿里巴巴（中国）有限公司 Data processing method, device, storage medium, and program product
CN116029759A (en) * 2023-01-31 2023-04-28 砼联数字科技有限公司 Dynamic intelligent optimization method and system for concrete production industry chain
CN116029759B (en) * 2023-01-31 2024-03-19 砼联数字科技有限公司 Dynamic intelligent optimization method and system for concrete production industry chain
CN116934177B (en) * 2023-09-14 2023-12-08 中交二公局东萌工程有限公司 Enterprise production task management data tracking analysis processing system
CN116934177A (en) * 2023-09-14 2023-10-24 中交二公局东萌工程有限公司 Enterprise production task management data tracking analysis processing system
CN117726245A (en) * 2024-02-09 2024-03-19 爱彼希科技(北京)有限公司 Supply chain planning method

Similar Documents

Publication Publication Date Title
CN108108994A (en) For the plan optimization method of chemical enterprise supply chain
Arampantzi et al. A new model for designing sustainable supply chain networks and its application to a global manufacturer
Wagner et al. The game plan for aligning the organization
Ghahremani Nahr et al. A robust optimization approach for multi-objective, multi-product, multi-period, closed-loop green supply chain network designs under uncertainty and discount
CN102385724A (en) Spare part assembling demand forecasting information processing method applied to inventory management
PG A neuro-fuzzy hybrid model for assessing leanness of manufacturing systems
Solodovnik et al. Innovative development of the foreign economic activity of the enterprise
Safaei Investigating and extracting green marketing strategies for eco-friendly packaging in the food and pharmaceutical supply chain (case study of arian daru pharmaceutical company)
Sehgal et al. Role of CMMS for optimising performance of Indian manufacturing industries
Borjalilu et al. An integrated method for airline company supplier selection based on the entropy and vikor methods: a real case study
Tanjoyo et al. The Role of TQM and Organizational Culture on Operational Performance
Nyaoga et al. Does capacity utilization mediate the relationship between operations constraint management and value chain performance of tea processing firms? evidence from Kenya
Bahadir The role of management accounting systems in implementing lean business strategies
Smith Operational tactics and insights for deploying ERP-related software applications and technology: manufacturing case study
Kolinski et al. Impact of transposing the strategic objectives on supply efficiency
Ali Analysis Of Quality Management System (Qms) Applied On Manufacturing & Design Companies
Turisová et al. Monitoring of process performance by means of financial indicators
Sadowska et al. Logistics costs and balanced scorecard in business management
Singh et al. Sustainable Green Development and Manufacturing Performance Through Modern Production Techniques
KAMRA Sustainable development through digital transformation.
Jones et al. Supply Chain Performance Measurement at Louserindo Megah Permai Using SCOR Model
Lim et al. Ecomspaces: Warehouse Workflow Optimization
Azakir Simulation-Based Lean Framework to Improve Window-Manufacturing Production System
Hendrickx Techno-economic evaluation of a blow molding
Todeti et al. Reducing internal lead times in MTO & job-shop production environments: a case study

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right
TA01 Transfer of patent application right

Effective date of registration: 20190528

Address after: 310053 4th Floor, Zone D, No. 309 Liuhe Road, Binjiang District, Hangzhou City, Zhejiang Province

Applicant after: ZHEJIANG SUPCON SOFTWARE Co.,Ltd.

Applicant after: ZHEJIANG University

Address before: 310053 4th Floor, Zone D, No. 309 Liuhe Road, Binjiang District, Hangzhou City, Zhejiang Province

Applicant before: ZHEJIANG SUPCON SOFTWARE Co.,Ltd.

RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20180601