CN111369283A - Simulation-based engineering material purchase price adjustment strategy selection method and system - Google Patents
Simulation-based engineering material purchase price adjustment strategy selection method and system Download PDFInfo
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Abstract
The invention discloses a simulation-based engineering material purchasing price adjustment strategy selection method, which comprises the following steps of: selecting different parameter combinations based on various data related to the engineering material purchasing price adjustment strategy to generate various price adjustment strategies; analyzing price influence factors and historical data, and selecting price trends; generating a simulation case set under the trend of the selected price; and calculating the simulation case set under various different price adjustment strategies, and analyzing the statistical result. The invention determines the trend by analyzing historical price data and combining price influence factors, uses a simulation optimization algorithm to generate a simulation case close to reality, and uses a statistical method to analyze the result, aiming at selecting a price adjustment strategy meeting the requirement, visualizing the analysis result and assisting a decision maker to make a decision.
Description
Technical Field
The invention belongs to the technical field of simulation analysis, and particularly relates to a simulation-based engineering material purchasing price adjustment strategy selection method and system.
Background
In a long-term engineering project, the price change of the material is difficult to predict under the influence of various factors, and due to huge consumption, the slight price change can cause the settlement amount to change by hundreds of thousands yuan in the contract period, so that the reasonable setting of the price adjustment strategy of the material has important significance. In the engineering material purchasing bidding, the method can ensure that bidders do not need to consider the risk cost of price rising during bidding, ensure that the bidding degree is more reasonable, and is more beneficial to obtaining bidders with lower prices for the bidders. In the construction of the engineering project, the price adjustment terms in the contract can prevent the contractor from generating fund loss in the project construction process, and the proprietor does not need to worry about the project delay or low-quality delivery caused by the fund loss of the contractor. In a word, the reasonable design of the material price adjusting strategy is not only beneficial to effectively controlling the cost and guaranteeing the stability of material supply of owners in the engineering implementation, but also beneficial to reasonably adjusting the bidding price of bidders (material suppliers), thereby improving the competitive power of bidding.
The establishment of the project material price adjustment strategy is an important content in material purchasing bidding and is also a difficult problem. There are some researches on the theoretical framework of the material price adjustment strategy and the risk sharing of the price adjustment difference in the material price adjustment strategy, but no intensive research is currently carried out on the hierarchical processing of price fluctuation and the selection and determination of specific parameters in the price adjustment strategy. In the field of system simulation and optimization, corresponding theories or algorithms have been researched, and the theories and application methods can provide reference for the research of the material price adjustment strategy.
Therefore, at present, it is needed to provide a method and a system for selecting a procurement price adjustment strategy of engineering materials based on simulation to overcome the above-mentioned drawbacks.
Disclosure of Invention
The invention aims to provide a simulation-based engineering material purchasing price adjustment strategy selection method and system, which are used for solving one of the technical problems in the prior art, such as: the establishment of the project material price adjustment strategy is an important content in material purchasing bidding and is also a difficult problem. There are some researches on the theoretical framework of the material price adjustment strategy and the risk sharing of the price adjustment difference in the material price adjustment strategy, but no intensive research is currently carried out on the hierarchical processing of price fluctuation and the selection and determination of specific parameters in the price adjustment strategy. In the field of system simulation and optimization, corresponding theories or algorithms have been researched, and the theories and application methods can provide reference for the research of the material price adjustment strategy.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a simulation-based engineering material purchase price adjustment strategy selection method comprises the following steps:
s1, selecting different parameter combinations based on various data related to the project material purchasing price adjustment strategy to generate a plurality of price adjustment strategies;
s2, analyzing price influence factors and historical data, and selecting price trends;
s3, generating a simulation case set under the trend of the selected price on the basis of the step S2;
and S4, combining the steps S1 and S3, calculating the simulation case set under various different price adjustment strategies, and analyzing the statistical result.
The parameters selected in step S1 include information price, number of price fluctuation intervals, price fluctuation interval boundary, risk sharing ratio of fluctuation intervals, and different price adjustment strategies generated by different parameter combinations.
The historical data analysis of the step S2 is to divide the price fluctuation category according to the distribution of the material historical price fluctuation rate, the distribution is described by a frequency distribution table, the fluctuation interval is refined and layered, the frequency of the historical fluctuation rate appearing in each cell is counted, and the price trend comprises the number of sections of the fluctuation curve and the fluctuation category of each section of the curve.
The simulation case of step S3 is composed of a series of discrete points representing monthly price volatility, and the specific generation includes the following steps:
analyzing historical data to obtain the monthly cycle rate fluctuation distribution of the material price;
generating an inner curve according to the selected curve segment number and the fluctuation category, wherein a first point of the inner curve is generated according to the historical price fluctuation rate distribution, and subsequent points are generated according to the monthly cycle ratio fluctuation rate;
and connecting the generated curve segments, wherein the numerical difference of the front discrete point and the rear discrete point at the connecting position cannot be too large, and forming a plurality of complete simulation cases.
And optimizing by using an OCBA algorithm when the cases are generated, generating the simulation cases in stages, and adjusting the number of the cases generated in each fluctuation interval of the next stage by each stage according to the generated case data distribution and distribution metering rule.
In the step metering distribution principle, a plurality of small intervals into which a large fluctuation interval is subdivided are regarded as different schemes, and the frequency of the small intervals appearing in the large fluctuation interval is regarded as a performance value of the scheme.
The statistical analysis of step S4 is characterized by four indexes, i.e., the deviation ratio of the settlement difference, the goodness of fit between the adjusted price fluctuation ratio and the price without change, the goodness of fit between the adjusted price fluctuation ratio curve and the original fluctuation ratio curve, and the distribution of monthly settlement prices.
The monthly settlement price is shown in a box type graph, the box type graph comprises the highest settlement unit price, the lowest settlement unit price, a quartile value, a median and a mean value, the whole height of the box body represents the height of the price adjustment difference generated by the price adjustment strategy, the length of the box body represents the fluctuation range of the price adjustment, the shorter the box body is, the better the stability is represented, and the shorter the box body is, the better the marketability is represented.
Selecting different parameter combinations to generate a plurality of price adjustment strategies;
specifically, the calculation model is:
price fluctuation rate in month j:
wherein j is the month number in the contract period, j is 1,2 … M, MjInformation price index, M, in month j of materials published by base-term price authority signed in contract0A base price for the contract;
to account for risk sharing, the price volatility in month j:
wherein k is the undulation layer number, k is 1,2 … n, αkUpper bound of the k-th layer fluctuation interval, βkThe value of the share proportion of the owner to the price-exchanging difference risk in the k-th layer fluctuation interval is [0,1 ]]。
Settlement price of material price in month j:
Pj＝P0*(1+Uj)#(3)
wherein, P0And (5) making the contract of the materials at the factory price.
Total settlement in m months:
the parameter to be selected comprises the information price MjThe number n of sub-price fluctuation intervals, and a boundary α between price fluctuation intervalskRisk sharing ratio between fluctuation regions βkDifferent parameter combinations result in different price adjustment strategies.
Analyzing price influence factors and historical data, and selecting price trends;
the price influencing factors comprise cost factors, market environment competition factors and supply and demand relation change; the historical data analysis is to divide price fluctuation categories according to the distribution of the historical price fluctuation rate of the material, the distribution is described by a frequency distribution table, fluctuation intervals are refined and layered, the frequency of the historical fluctuation rate appearing in each cell is counted, and the price trend is selected to comprise the number of sections of a fluctuation curve and the fluctuation category of each section of the curve.
Generating a simulation case set under the selected trend;
the simulation case consists of a series of discrete points representing monthly price fluctuation rate, and specifically comprises the following steps:
analyzing historical data to obtain the monthly cycle rate fluctuation distribution of the material price;
generating an inner curve according to the selected curve segment number and the fluctuation category, wherein the first point of the inner curve is generated by the historical price fluctuation rate distribution in the step (2), and the subsequent points are generated by the monthly cycle ratio fluctuation rate;
connecting the generated curve segments to form a complete case, wherein the numerical difference of the front discrete point and the rear discrete point at the connection position cannot be too large;
and when the cases are generated, the OCBA algorithm is used for optimizing, the simulation cases are generated in stages, and the number of the cases generated in each fluctuation interval of the next stage is adjusted in each stage according to the generated case data distribution and distribution metering rule.
In the metering allocation principle, a plurality of small intervals into which a large fluctuation interval is subdivided are regarded as different schemes, and the frequency of the small intervals appearing in the large fluctuation interval is regarded as a performance value of the scheme.
And calculating the case set under different price adjustment strategies, and analyzing the statistical result.
The statistical analysis is characterized by four indexes of deviation rate of settlement difference, fitting goodness of the adjusted price fluctuation rate and unchanged price, fitting goodness of the adjusted price fluctuation rate curve and the original fluctuation rate curve and distribution of monthly settlement price.
Deviation ratio μ of settlement difference:
mu represents the difference degree between the adjusted and the non-adjusted settlement total, and the smaller the difference degree, the better the stability of the price adjustment strategy.
Adjusted price volatility (U)j) Goodness of fit R to 0(0 indicates no change in price)1 2：
U is UjMean value of R1 2Is taken to be [0,1 ]]The closer the value is to 1, the closer the generated settlement price is to the factory price, i.e. the better stability.
Adjusted price volatility (U)j) Curve and primary fluctuation ratio (V)j) Goodness of fit R of curve2 2：
R2 2Is taken to be [0,1 ]]The closer the value is to 1, the closer the generated settlement price is to the actual market, i.e. the better marketability.
Distribution of monthly settlement prices:
monthly settlement prices are presented in box plots, with each box in the horizontal direction representing a statistical distribution of the calculated results of a pricing strategy: respectively calculating monthly settlement price P generated by the generated simulation case under the price adjustment strategyj(the total of settlement may also be considered in connection with consumptionQuota) and will settle the monthly payment PjThe upper end and the lower end of a drawn thin line respectively represent the highest monthly settlement unit price and the lowest monthly settlement unit price of a generated case set under the price adjusting strategy, the upper end and the lower end of a box type entity represent the monthly settlement unit prices at 75% of quartiles and 25% of quartiles, the horizontal line in the middle of the box body represents a median, × represents the price adjusting average value, the height of the whole position of the box body represents the height of the price adjusting difference generated by the price adjusting strategy, the length of the box body represents the fluctuation range of the price adjusting amount, the shorter the box type diagram is, the better the stability is represented, and the shorter the box type diagram is, the better the marketability is represented.
In addition, the invention also provides a simulation-based engineering material purchasing price adjustment simulation system, which comprises an input module, a case generation module, a simulation calculation module and a result display module.
The input module is used for collecting parameters required by simulation, and comprises price adjusting strategies, price trends and data related to a calculation model, wherein the price adjusting strategies are different parameter combinations, and the parameters comprise fluctuation interval number n and fluctuation interval boundary αkRisk sharing ratio β between fluctuation zoneskAnd information price MjA source; the price trend comprises the number of sections of the fluctuation curve and the fluctuation category of the curve in the section; the related data of the calculation model comprises contract factory-leaving price P0Information reference price M0Consumption QjAnd historical price, number of simulation cases N.
The case generation module obtains historical price fluctuation distribution and monthly ring ratio price fluctuation distribution according to price trends and historical data, obtains a first point in a section according to the historical price fluctuation distribution, generates a subsequent point according to the monthly ring ratio price fluctuation distribution, uses OCBA algorithm optimization to generate simulation cases in stages, and adjusts the number of cases generated in each fluctuation interval of the next stage according to the generated case data distribution and distribution metering rules in each stage until a case set with the designated number is generated.
And the simulation calculation module brings the case set of the case generation module into model calculation under different price adjustment strategies input by the input module.
The result display module displays calculation results of a large number of cases under different price adjustment strategies in a box type graph form, the calculation results comprise monthly settlement unit price distribution adjusted by different price adjustment strategies and settlement total amount distribution adjusted by different price adjustment strategies, each transverse box body represents calculation result statistical distribution of one price adjustment strategy, the box type graph comprises the highest, the lowest, the quartile, the median and the mean of the settlement prices (or settlement amounts) of the different price adjustment strategies, the height of the whole box body represents the height of the price adjustment difference generated by the price adjustment strategies, the length of the box body represents the fluctuation range of the price adjustment, the shorter the box body is, the better the stability is represented, and the shorter the box body is, the better the marketability is represented.
The beneficial technical effects of the invention are as follows: the trend is determined by analyzing historical price data and combining price influence factors, a simulation case close to reality is generated by using a simulation optimization algorithm, and a result is analyzed by using a statistical method, so that a price adjusting strategy meeting requirements is selected, the analysis result is visualized, and a decision maker is assisted in making a decision.
Drawings
FIG. 1 is a flow chart of a simulation method for procurement and price adjustment of engineering materials according to the present invention.
Fig. 2 is a flow chart of generating an optimized case by using an OCBA method in the engineering material procurement price adjustment simulation method provided by the invention.
FIG. 3 is a box diagram of an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 3 of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
a method and a system for selecting a project material purchasing price adjustment strategy based on simulation comprise the following steps:
firstly, selecting different parameter combinations to generate a plurality of price adjustment strategies (including the 1 st and the 2 nd steps)
Step 1: selecting an information price:
there are 4 ways to select the combination of information prices: the method 1 only selects the cement information price index of Sichuan province published on the China Cement Net; the mode 2 only selects the cement information price index published by the Chinese price information network; the information price 1 and the information price 2 are selected in the mode 3; and the mode 4 selects the information price 1 and a Sichuan coal-saving and electricity-saving information price index published on a Chinese price information network.
Step 2: selecting the level parameters:
after the information price is determined, 3 layer parameters are required to be selected: the number of fluctuation layers of the information price, the upper and lower boundary lines of the fluctuation layers and the risk sharing proportion of each fluctuation layer. When the number of the layers is 2, the interval of each layer is two cases of [0,0.2], (0.2,0.5] and [0,0.3], (0.3,0.5], the sharing rate is two cases of (0, 0.5) and (0, 0.4), when the number of the layers is 3, the interval of each layer is two cases of [0,0.05], (0.05,0.2], (0.2,0.5] and [0,0.05], (0.05, 0.3) and (0.3,0.5], the sharing rate is two cases of (0, 1, 0.5) and (0, 1, 0.4), and when the fluctuation rate of the information price exceeds 0.5, the specific sharing rate is determined by the negotiation of both parties.
The combination of different information prices and tiers can form several price adjustment strategies.
Analyzing price influence factors and historical data, and selecting price trends (including steps 3 and 4);
and 3, step 3: analyzing the price influence factors and historical data:
the price influencing factors comprise cost factors, market environment competition factors and supply and demand relation change; the historical data analysis is to divide price fluctuation categories according to the distribution of the historical price fluctuation rate of the material, the distribution is described by a frequency distribution table, fluctuation intervals are refined and layered, the frequency of the historical fluctuation rate appearing in each cell is counted, and the price trend is selected to comprise the number of sections of a fluctuation curve and the fluctuation category of each section of the curve.
And 4, step 4: selecting a price trend:
and (4) selecting the number of the segments of the fluctuation curve and the fluctuation category of each segment of the curve according to the analysis result in the step 3.
Thirdly, generating a simulation case set under the selected trend (comprising the steps of 5 th, 6 th and 7 th)
And data processing including historical data analysis and simulation case optimization generation is realized by using JDK1.8 programming under Windows 10.
And 5, step 5: and (3) historical data analysis:
analyzing historical data to obtain the monthly cycle rate fluctuation rate distribution of the material price;
and 6, step 6: generating an inner segment curve:
and (4) generating an inner-segment curve according to the number of the curve segments selected in the step (4) and the fluctuation category, wherein the first point of the inner-segment curve is generated according to the historical price fluctuation rate distribution in the step (3), and the subsequent points are generated according to the lunar cycle ratio fluctuation rate in the step (5).
And 7, step 7: forming a complete simulation case:
the generated curves in the sections are connected to form a complete case, and the numerical difference of the discrete points before and after the connection cannot be too large.
And 8, step 8: and (3) simulation case generation optimization:
and simultaneously, optimizing by using an OCBA algorithm, generating simulation cases in stages by using a flow chart as shown in fig. 2, and adjusting the number of the cases generated in each fluctuation interval of the next stage by each stage according to the generated case data distribution and distribution metering rule until the cases with the specified number are generated. In the metering allocation principle, a plurality of small intervals into which a large fluctuation interval is subdivided are regarded as different schemes, and the frequency of the small intervals appearing in the large fluctuation interval is regarded as a performance value of the scheme. The optimal solution satisfies:
wherein, JiSetting m schemes for the performance index of the ith scheme, namely the historical occurrence frequency of the scheme; n is a radical ofiThe number of cases to be next assigned for the ith scenario; sigmaiFor the ith scheme in the assigned case setAbsolute error between history frequencies; let J1≥J2≥…≥Jm。
From the formula (8), the performance index J of the ith schemeiThe larger, NiThe larger the value is, namely the higher the historical occurrence frequency of the fluctuation interval is, the interval can be allocated to more cases in the next case allocation; absolute error sigmaiThe larger, NiThe smaller the value, i.e. the more frequently the fluctuation interval occurs than its historical frequency, the fewer cases will be allocated in this interval at the next case allocation.
Tc is the total case for the next assignment. Then:
Tc＝N1+N2+…+Nm#(2)
in combination with formula (8) and formula (9), further Tc may be expressed with respect to N1By means of the method of obtaining the most value, N in the optimal scheme can be obtained1Value, and further all N are obtainediI-1, 2, …, m, is the optimal allocation scheme.
Fourthly, calculating case sets under different price adjustment strategies, and analyzing statistical results (including the 9 th step and the 10 th step)
Calculating and analyzing by using an Excel tool, and visually displaying the result.
Step 9: acquiring input data:
the simulation input data comprises the information price of the step 1, the level parameters of the step 2, the price trend of the step 4, the information reference price input by the user, the contract factory price, the consumption and the number of simulation cases.
Step 10: running output, analysis result:
and (4) substituting the case generated in the 8 th step of the parameters acquired in the 9 th step into the models according to the formulas (3) to (6) for calculation, analyzing and outputting the obtained result.
Pj＝P0*(1+Uj)#(5)
Where j is the month number in the contract period, j is 1,2 … M, the price fluctuation rate of the j-th month, MjInformation price index, M, in month j of materials published by base-term price authority signed in contract0A base price for the contract; u shapejIn order to consider the price fluctuation rate of the j th month after the risk sharing, wherein k is the fluctuation layer number, and k is 1,2 … n, αkUpper bound of the k-th layer fluctuation interval, βkThe value of the share proportion of the owner to the price-exchanging difference risk in the k-th layer fluctuation interval is [0,1 ]]；PjSettle price for material price in month j, P0The contract of the materials is released for the factory price; s is the total settlement of m months, QjIs the material consumption of the j month.
The result analysis is characterized by statistical analysis by four indexes of deviation rate of settlement difference, goodness of fit of adjusted price fluctuation rate and unchanged price, goodness of fit of adjusted price fluctuation rate curve and original fluctuation rate curve, and distribution of monthly settlement price.
The deviation rate mu of the settlement difference represents the difference degree between the settlement total after price adjustment and the settlement total without price adjustment, and the smaller the deviation rate mu represents the better stability of the price adjustment strategy; price fluctuation rate (U) after completionj) And 0(0 means price is not changed)Goodness of fit R1 2U is UjMean value of R1 2Is taken to be [0,1 ]]The closer the value is to 1, the closer the generated settlement price is to the factory price, namely the stability is better; adjusted price volatility (U)j) Curve and primary fluctuation ratio (V)j) Goodness of fit R of curve2 2Is taken to be [0,1 ]]The closer the value is to 1, the closer the generated settlement price is to the actual market, i.e. the better marketability.
Monthly settlement prices are presented in a boxed graph as shown in FIG. 3, with each box in the horizontal direction representing a statistical distribution of the calculated results of a price adjustment strategy: respectively calculating monthly settlement price P generated by the generated simulation case under the price adjustment strategyj(or settle the total), and settle the monthly settlement price PjThe upper end and the lower end of a drawn thin line respectively represent the highest monthly settlement unit price (or settlement total) generated by the generated case set under the price adjusting strategy, the upper end and the lower end of a box-type entity represent the monthly settlement unit price (or settlement total) at 75% of quartiles and 25% of quartiles, the horizontal line in the middle of the box body represents a median, the × is the price adjusting mean, the height of the whole position of the box body represents the height of the price adjusting difference generated by the price adjusting strategy, the length of the box body represents the fluctuation range of the price adjusting amount, the shorter the box-type diagram is, the better the stability is represented, and the market is represented by the shorter the box-type diagram is.
Example 2:
a simulation-based engineering material purchasing price-adjusting simulation system comprises an input module, a case generation module, a simulation calculation module and a result display module.
The input module is used for collecting parameters required by simulation, and comprises price adjusting strategies, price trends and data related to a calculation model, wherein the price adjusting strategies are different parameter combinations, and the parameters comprise fluctuation interval number n and fluctuation interval boundary αkRisk sharing ratio β between fluctuation zoneskAnd information price MjA source; the price trend comprises the number of sections of the fluctuation curve and the fluctuation category of the curve in the section; the related data of the calculation model comprises contract factory-leaving price P0Information reference price M0Consumption QjAnd historical price, number of simulation cases N.
The case generation module obtains historical price fluctuation distribution and monthly ring ratio price fluctuation distribution according to price trends and historical data, obtains a first point in a section according to the historical price fluctuation distribution, generates a subsequent point according to the monthly ring ratio price fluctuation distribution, uses OCBA algorithm optimization to generate simulation cases in stages, and adjusts the number of cases generated in each fluctuation interval of the next stage according to the generated case data distribution and distribution metering rules in each stage until a case set with the designated number is generated.
And the simulation calculation module brings the case set of the case generation module into model calculation under different price adjustment strategies input by the input module.
The result display module displays calculation results of a large number of cases under different price adjustment strategies in a box type graph form, the calculation results comprise monthly settlement unit price distribution adjusted by different price adjustment strategies and settlement total amount distribution adjusted by different price adjustment strategies, each transverse box body represents calculation result statistical distribution of one price adjustment strategy, the box type graph comprises the highest, the lowest, the quartile, the median and the mean of the settlement prices (or settlement amounts) of the different price adjustment strategies, the height of the whole box body represents the height of the price adjustment difference generated by the price adjustment strategies, the length of the box body represents the fluctuation range of the price adjustment, the shorter the box body is, the better the stability is represented, and the shorter the box body is, the better the marketability is represented.
In the description of the present invention, it is to be understood that the terms "counterclockwise", "clockwise", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate orientations or positional relationships based on those shown in the drawings, and are used for convenience of description only, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be considered as limiting.
Claims (9)
1. A simulation-based engineering material purchase price adjustment strategy selection method is characterized by comprising the following steps:
s1, selecting different parameter combinations based on various data related to the project material purchasing price adjustment strategy to generate a plurality of price adjustment strategies;
s2, analyzing price influence factors and historical data, and selecting price trends;
s3, generating a simulation case set under the trend of the selected price on the basis of the step S2;
and S4, combining the steps S1 and S3, calculating the simulation case set under various different price adjustment strategies, and analyzing the statistical result.
2. The method as claimed in claim 1, wherein the parameters selected in step S1 include information price, number of price fluctuation intervals, boundary line of price fluctuation intervals, risk sharing ratio of fluctuation intervals, and different parameter combinations generate different price adjustment strategies.
3. The method as claimed in claim 1, wherein the historical data analysis in step S2 is to classify the price fluctuation categories according to the distribution of the historical price fluctuation rate of the material, the distribution is described in a frequency distribution table, the fluctuation intervals are subdivided and layered, and the frequency of the historical fluctuation rate occurring in each cell is counted, and the price trend is selected from the group consisting of the number of segments of the fluctuation curve and the fluctuation category of each segment of the curve.
4. The method as claimed in claim 3, wherein the simulation case of step S3 is composed of a series of discrete points representing monthly price fluctuation rate, and the specific generation comprises the following steps:
analyzing historical data to obtain the monthly cycle rate fluctuation distribution of the material price;
generating an inner curve according to the selected curve segment number and the fluctuation category, wherein a first point of the inner curve is generated according to the historical price fluctuation rate distribution, and subsequent points are generated according to the monthly cycle ratio fluctuation rate;
and connecting the generated curve segments, wherein the numerical difference of the front discrete point and the rear discrete point at the connecting position cannot be too large, and forming a plurality of complete simulation cases.
5. The method as claimed in claim 4, wherein the OCBA algorithm is used for optimization during case generation, the simulation cases are generated in stages, and the number of cases generated in each fluctuation interval of the next stage is adjusted in each stage according to the generated case data distribution and distribution metering rules.
6. The method as claimed in claim 5, wherein in the step of measuring and allocating principle, the small intervals into which the large fluctuation interval is subdivided are regarded as different schemes, and the frequency of the small intervals appearing in the large fluctuation interval is regarded as the performance value of the scheme.
7. The method as claimed in claim 1, wherein the statistical analysis of step S4 is characterized by including four indexes, i.e. deviation rate of settlement difference, goodness of fit between adjusted price fluctuation rate and unchanged price, goodness of fit between adjusted price fluctuation rate curve and original fluctuation rate curve, and distribution of monthly settlement price.
8. The method as claimed in claim 7, wherein the monthly settlement price is displayed in a box type chart, the box type chart includes the highest and lowest settlement price, the quartile value, the median and the mean value, the height of the box body represents the height of the price adjustment difference generated by the price adjustment strategy, the length of the box body represents the fluctuation range of the price adjustment, the shorter the box body represents the better stability, and the shorter the box body represents the better marketability.
9. A simulation-based engineering material purchasing price adjustment strategy selection system is characterized by comprising an input module, a case generation module, a simulation calculation module and a result display module;
the input module is used for collecting parameters required by simulation, and the parameters comprise price adjustment strategies, price trends and calculation model related data;
the price adjustment strategy is different parameter combinations, and the parameters comprise fluctuation interval number, fluctuation interval boundary lines, risk sharing proportion in fluctuation intervals and information price sources;
the price trend comprises the number of sections of the fluctuation curve and the fluctuation category of the curve in the section;
calculating relevant data of the model, wherein the relevant data comprises contract factory-leaving price, information reference price, consumption, historical price and simulation case number;
the case generation module obtains historical price fluctuation distribution and monthly cycle price fluctuation distribution according to the price trends and the historical data, obtains a first point in a section according to the historical price fluctuation distribution, generates a subsequent point according to the monthly cycle price fluctuation distribution, uses OCBA algorithm optimization to generate simulation cases in stages, and adjusts the number of cases generated in each fluctuation interval of the next stage according to the generated case data distribution and distribution metering rule in each stage until a case set of the fluctuation interval number is generated;
the simulation calculation module brings the case set into model calculation under the different price adjustment strategies;
the result module is displayed in a box type graph, the monthly settlement unit price distribution and the settlement total amount distribution are adjusted by different price adjustment strategies, the box type graph contains the settlement price or the highest, lowest, quartile, median and mean value of the settlement amount of the different price adjustment strategies, the height of the whole box body represents the height of the price adjustment difference generated by the price adjustment strategies, the length of the box body represents the fluctuation range of the price adjustment, the shorter the box body is, the better the stability is represented, and the shorter the box body is, the market performance is better.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111861387A (en) * | 2020-07-13 | 2020-10-30 | 泰康保险集团股份有限公司 | Contract batch change method and device |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110443431A (en) * | 2019-08-14 | 2019-11-12 | 上海德拓信息技术股份有限公司 | Stock market's deep bid advance-decline forecasting method based on big data |
-
2020
- 2020-02-28 CN CN202010131578.6A patent/CN111369283A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110443431A (en) * | 2019-08-14 | 2019-11-12 | 上海德拓信息技术股份有限公司 | Stock market's deep bid advance-decline forecasting method based on big data |
Non-Patent Citations (2)
| Title |
|---|
| 刘晓佳: "基于复杂适应系统的中国燃料油期货市场交易策略研究", 《中国博士学位论文全文数据库 经济与管理科学辑》 * |
| 宁晓玲等: "2011年8月三七文山市场走势分析", 《中国现代中药》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111861387A (en) * | 2020-07-13 | 2020-10-30 | 泰康保险集团股份有限公司 | Contract batch change method and device |
| CN111861387B (en) * | 2020-07-13 | 2023-12-19 | 泰康保险集团股份有限公司 | Contract batch changing method and device |
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