CN111369283A - A simulation-based method and system for selecting price adjustment strategy for engineering material procurement - Google Patents

Abstract

The invention discloses a method for selecting a price adjustment strategy for engineering material procurement based on simulation. Historical data, select the price trend; generate a simulation case set under the selected price trend; calculate the simulation case set under a variety of different price adjustment strategies, and analyze the statistical results. The invention determines the trend by analyzing the historical price data, combining the price influencing factors, using the simulation optimization algorithm to generate a simulation case close to the reality, and using the statistical method to analyze the results, aiming to select a price adjustment strategy that meets the requirements, and visualize the analysis results. Assist decision makers in making decisions.

Description

A simulation-based method and system for selecting price adjustment strategy for engineering material procurement

technical field

The invention belongs to the technical field of simulation analysis, and in particular relates to a method and system for selecting a price adjustment strategy for engineering material procurement based on simulation.

Background technique

In long-term engineering projects, due to various factors, it is difficult to predict changes in material prices, and due to huge consumption, subtle price changes may lead to changes in the settlement amount of up to several million yuan during the contract period. Reasonable setting of material price adjustment strategies has the advantages of important meaning. In engineering materials procurement bidding, it can make the bidders do not need to consider the risk cost of price increase when bidding, making the bidding system more reasonable, and for the tenderer, it is more beneficial to obtain bidders with lower prices. In the construction of engineering projects, the existence of price adjustment clauses in the contract can help the contractor to avoid capital losses during the project construction process, and the owner does not have to worry about the contractors delaying the project or delivering low-quality projects due to capital losses. In a word, the rational design of material price adjustment strategy not only helps the owner to effectively control the cost in the project implementation, ensure the stability of material supply, but also helps the bidder (material supplier) to adjust the bidding price reasonably, thereby improving the competitiveness of the bidding.

The formulation of project material price adjustment strategy is an important part of material procurement bidding, and it is also a difficult problem. There are some researches on the theoretical framework of material price adjustment strategy and the risk sharing of the price adjustment difference, but there is no in-depth research on the hierarchical processing of price fluctuations and the selection and determination of specific parameters in the price adjustment strategy. In the field of system simulation and optimization, there have been many researches on corresponding theories or algorithms, and these theories and application methods can provide reference for the study of material price adjustment strategies.

Therefore, at this stage, it is necessary to provide a method and system for selecting a price adjustment strategy for engineering material procurement based on simulation to overcome the above shortcomings.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a method and system for selecting a price adjustment strategy for engineering material procurement based on simulation, which is used to solve one of the technical problems existing in the above-mentioned prior art. important content, but also a difficult problem. There are some researches on the theoretical framework of material price adjustment strategy and the risk sharing of the price adjustment difference, but there is no in-depth research on the hierarchical processing of price fluctuations and the selection and determination of specific parameters in the price adjustment strategy. In the field of system simulation and optimization, there have been many researches on corresponding theories or algorithms, and these theories and application methods can provide reference for the study of material price adjustment strategies.

For achieving the above object, the technical scheme adopted in the present invention is:

A method for selecting a price adjustment strategy for engineering material procurement based on simulation, comprising the following steps:

S1. Based on the data involved in the price adjustment strategy of engineering material procurement, select different parameter combinations to generate a variety of price adjustment strategies;

S2. Analyze price influencing factors and historical data, and select price trends;

S3. On the basis of step S2, generate a set of simulation cases under the selected price trend;

S4, in combination with steps S1 and S3, the simulation case set is calculated under a variety of different price adjustment strategies, and the statistical results are analyzed.

The parameters selected in step S1 include the information price, the number of price fluctuation intervals, the boundary of the price fluctuation interval, and the risk sharing ratio of the fluctuation interval. Different parameter combinations generate different price adjustment strategies.

The historical data analysis in step S2 is to divide the price fluctuation categories according to the distribution of the historical price volatility of materials, and the distribution is described by a frequency distribution table. The selection includes the number of segments of the fluctuation curve and the fluctuation type of each segment.

The simulation case of step S3 consists of a series of discrete points representing monthly price volatility, and the specific generation includes the following steps:

Analyze historical data to obtain the monthly month-on-month volatility distribution of material prices;

The intra-segment curve is generated according to the selected number of curve segments and the volatility category. The first point of the intra-segment curve is generated according to the historical price volatility distribution, and the subsequent points are generated according to the monthly volatility;

The generated curve segments are connected, and the numerical difference between the discrete points before and after the connection should not be too large to form multiple complete simulation cases.

The OCBA algorithm is used for optimization in case generation, and simulation cases are generated in stages. In each stage, the number of cases generated in each fluctuation interval in the next stage is adjusted according to the distribution and distribution measurement rules of the generated case data.

In the principle of step metering and distribution, a large fluctuation interval is subdivided into several small intervals as different schemes, and the frequency of the small interval in the large fluctuation interval is regarded as the performance value of the scheme.

The statistical analysis in step S4 is based on the deviation rate of the settlement difference, the goodness of fit between the adjusted price volatility and the unchanged price, the goodness of fit between the adjusted price volatility curve and the original volatility curve, and the monthly settlement price. The distribution of these four indicators is described.

The monthly settlement price is displayed in a box chart. The box chart includes the highest, lowest, quartile, median and average of the settlement unit price. The overall level of the box represents the price adjustment difference generated by the price adjustment strategy. The length of the price adjustment fluctuation range, the shorter the better the stability, and vice versa, the better the marketability.

Among them, different parameter combinations are selected to generate a variety of price adjustment strategies;

Specifically, the calculation model is:

Price volatility in month j:

Among them, j is the month number in the contract period, j=1,2...m,M _j is the information price index of the materials in the jth month announced by the price authority of the base period signed in the contract, M ₀ is the base period signed in the contract price;

In order to take into account the risk sharing, the price volatility of the jth month:

Among them, k is the number of the volatility layer, k=1, 2...n, α _k is the upper bound of the fluctuation interval of the k-th layer, β _k is the owner's share of the risk of the price adjustment difference in the fluctuation interval of the k-th layer, and the value is [0,1].

Material price settlement price of the jth month:

P _j =P ₀ *(1+U _j )#(3)

Among them, P ₀ is the contract ex-factory price of materials.

The total settlement amount for m months:

The parameters to be selected include the information price M _j , the number of sub-price fluctuation intervals n, the price fluctuation interval boundary α _k , and the risk sharing ratio β _k of the fluctuation interval. Different parameter combinations produce different price adjustment strategies.

Analyze price influencing factors and historical data, and select price trends;

Price influencing factors include cost factors, market environment competition factors and changes in supply and demand; historical data analysis is to divide price fluctuation categories according to the distribution of historical price volatility of materials. The frequency of historical volatility in each cell, and the selection of price trend includes the number of segments of the volatility curve and the volatility category of each segment.

Generate a set of simulation cases under the selected trend;

The simulation case consists of a series of discrete points representing monthly price volatility, and includes the following steps:

Analyze historical data to obtain the monthly month-on-month volatility distribution of material prices;

The intra-segment curve is generated according to the selected number of curve segments and the volatility category. The first point of the intra-segment curve is generated from the historical price volatility distribution described in step (2), and the subsequent points are generated from the monthly volatility;

The generated curve segments are connected to form a complete case, and the numerical difference between the discrete points before and after the connection cannot be too large;

During case generation, the OCBA algorithm is used for optimization, and simulation cases are generated in stages. In each stage, the number of cases generated in each fluctuation interval of the next stage is adjusted according to the distribution and distribution measurement rules of the generated case data.

In the principle of metering and distribution, a large fluctuation interval is subdivided into several small intervals as different schemes, and the frequency of the small interval in the large fluctuation interval is regarded as the performance value of the scheme.

The case set is calculated under different price adjustment strategies, and the statistical results are analyzed.

Statistical analysis is based on the deviation rate of the settlement difference, the goodness of fit between the adjusted price volatility and the unchanged price, the goodness of fit between the adjusted price volatility curve and the original volatility curve, and the distribution of monthly settlement prices. Four indicators are described.

Deviation rate μ of settlement difference:

μ represents the difference between the total settlement amount after price adjustment and the total settlement amount without price adjustment. The smaller the value, the better the stability of the price adjustment strategy.

Goodness-of-fit R ₁ ² of the adjusted price volatility (U _j ) to 0 (0 means the price has not changed):

U is the mean value of U _j , and the value of R ₁ ² is between [0, 1]. The closer the value is to 1, the closer the settlement price is to the ex-factory price, that is, the better the stability.

The goodness of fit R ₂ ² of the adjusted price volatility (U _j ) curve and the original volatility (V _j ) curve:

The value of R ₂ ² is between [0, 1]. The closer the value is to 1, the closer the generated settlement price is to the actual market situation, that is, the better the marketability.

Distribution of monthly settlement prices:

The monthly settlement price is displayed in a box chart, and each horizontal box represents the statistical distribution of the calculation results of a price adjustment strategy: the monthly settlement price P _j generated by the generated simulation case under the price adjustment strategy (can also be combined with consumption) The monthly settlement price P _j is arranged in ascending order. The upper and lower ends of the leading thin line respectively represent the highest and lowest monthly settlement unit price generated by the generated case set under this price adjustment strategy; the upper and lower ends of the box-shaped entity represent the monthly settlement unit price at 75% and 25% of the quartile. ; The horizontal line in the middle of the box represents the median; the × symbol is the mean price adjustment. The overall position of the box represents the level of the price adjustment difference generated by this price adjustment strategy; the length of the box represents the fluctuation range of the price adjustment. The shorter the box chart, the better the stability, and vice versa, the better the marketability.

In addition, the present invention also provides a simulation-based engineering material procurement price adjustment simulation system, including an input module, a case generation module, a simulation calculation module, and a result display module.

The input module is used to collect parameters required for simulation, including price adjustment strategies, price trends and data related to calculation models. The price adjustment strategy is a combination of different parameters. The parameters include the number of fluctuation intervals n, the boundary of the fluctuation interval α _k , the risk sharing ratio of the fluctuation interval β _k and the source of the information price M _j ; the price trend includes the number of segments of the fluctuation curve and the fluctuation type of the curve within the segment; The relevant data of the calculation model include contract ex-factory price P ₀ , information base price M ₀ , consumption Q _j and historical price, and the number of simulation cases N.

The case generation module obtains the historical price fluctuation distribution and the monthly price fluctuation distribution according to the price trend and historical data, obtains the first point in the segment according to the historical price fluctuation distribution, generates subsequent points according to the monthly price fluctuation distribution, and uses the OCBA algorithm Optimization, generate simulation cases in stages, each stage adjusts the number of cases generated in each fluctuation interval of the next stage according to the distribution and distribution measurement rules of the generated case data, until a specified number of case sets are generated.

The simulation calculation module brings the case set of the case generation module into the model calculation under different price adjustment strategies input by the input module.

The result display module displays the calculation results of a large number of cases under different price adjustment strategies in the form of box plots, including the monthly settlement unit price distribution adjusted by different price adjustment strategies and the total settlement distribution adjusted by different price adjustment strategies. Each box represents the statistical distribution of the calculation results of a price adjustment strategy. The box plot contains the highest, lowest, quartile, median and mean of the settlement price (or settlement amount) of different price adjustment strategies. The level represents the price adjustment difference generated by the price adjustment strategy. The length of the box represents the price adjustment fluctuation range. The shorter the box, the better the stability, and vice versa, the better the marketability.

The beneficial technical effects of the present invention are: by analyzing historical price data, determining the trend in combination with price influencing factors, using a simulation optimization algorithm to generate a simulation case close to the reality, and using statistical methods to analyze the results, aiming to select a price adjustment that meets the requirements Strategies, visualization of analysis results, and assistance to decision makers in making decisions.

Description of drawings

FIG. 1 is a flowchart of a simulation method for price adjustment of engineering material procurement provided by the present invention.

FIG. 2 is a flow chart of the generation of an optimization case using the OCBA method in the simulation method for price adjustment of engineering material procurement provided by the present invention.

FIG. 3 is a schematic diagram of a box diagram according to an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings 1-3 of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Example 1:

A method and system for selecting a price adjustment strategy for engineering material procurement based on simulation, including the following:

1. Select different parameter combinations to generate a variety of price adjustment strategies (including the first and second steps)

Step 1: Information price selection:

There are 4 ways to select the combination of information prices: Method 1 only selects the Sichuan Cement Information Price Index published on China Cement Online; Method 2 only selects the cement information price index published by China Price Information Network; 2; Method 4 uses information price 1 and the Sichuan coal power information price index published on China Price Information Online.

Step 2: Layer parameter selection:

After the information price is determined, three levels of parameters need to be selected: the number of information price fluctuation layers, the upper and lower boundaries of the fluctuation layer, and the risk sharing ratio in each fluctuation layer. When there are 2 layers, the interval of each layer is [0, 0.2], (0.2, 0.5] and [0, 0.3], (0.3, 0.5], and the sharing rate is (0, 0.5) and (0, 0.4) two cases; when it is 3 layers, the interval of each layer is divided into [0,0.05], (0.05,0.2], (0.2,0.5] and [0,0.05], (0.05,0.3], (0.3 , 0.5] two cases. The sharing rate takes (0, 1, 0.5) and (0, 1, 0.4) two cases; when the volatility of the information price exceeds 0.5, the specific sharing ratio will be determined by both parties through negotiation.

The combination of different information prices and levels can form several price adjustment strategies.

2. Analyze price influencing factors and historical data, and select price trends (including steps 3 and 4);

Step 3: Analyze price influencing factors and historical data:

Price influencing factors include cost factors, market environment competition factors and changes in supply and demand; historical data analysis is to divide price fluctuation categories according to the distribution of historical price volatility of materials. The frequency of historical volatility in each cell, and the selection of price trend includes the number of segments of the volatility curve and the volatility category of each segment.

Step 4: Select the price trend:

According to the analysis results in step 3, select the number of segments of the fluctuation curve and the fluctuation type of each segment.

3. Generate a set of simulation cases under the selected trend (including the 5th, 6th, and 7th steps)

Using JDK1.8 programming under Windows 10 to realize data processing, including historical data analysis, simulation case optimization generation.

Step 5: Historical Data Analysis:

Analyze historical data to obtain the monthly month-on-month volatility distribution of material prices;

Step 6: Generate Intrasegment Curves:

The intra-segment curve is generated according to the number of curve segments and the volatility category selected in step 4. The first point of the intra-segment curve is generated according to the historical price volatility distribution in step 3, and the subsequent points are based on the monthly fluctuation in step 5. rate generation.

Step 7: Form a complete simulation case:

The generated intra-segment curves are connected to form a complete case, and the numerical difference between the discrete points before and after the connection should not be too large.

Step 8: Simulation case generation optimization:

At the same time, the OCBA algorithm is used for optimization. The flow chart is shown in Figure 2. The simulation cases are generated in stages. In each stage, the number of cases generated in each fluctuation range in the next stage is adjusted according to the distribution and distribution measurement rules of the generated case data, until Generates the specified number of cases. In the principle of metering and distribution, a large fluctuation interval is subdivided into several small intervals as different schemes, and the frequency of the small interval in the large fluctuation interval is regarded as the performance value of the scheme. Then the optimal solution satisfies:

Among them, J _i is the performance index of the i-th scheme, that is, the historical frequency of the scheme, and there are m schemes in total; Ni is the number of cases allocated for the _i -th scheme next time; σ _i is the number of cases in the set of allocated cases The absolute error between the occurrence frequency of the i-th scheme and the historical frequency; let J ₁ ≥J ₂ ≥...≥J _m .

It can be seen from formula (8) that the larger the performance index J _i of the i-th scheme, the larger the value of _Ni , that is, the higher the historical frequency of the fluctuation interval, the more cases will be assigned to this interval in the next case assignment. ; The larger the absolute error σ _i , the smaller the value of _Ni , that is, when the frequency of the fluctuation interval has been greater than its historical frequency, fewer cases will be assigned in this interval in the next case assignment.

Tc is the overall case for the next assignment. but:

Tc=N ₁ +N ₂ +...+N _m #(2)

Combining Equation (8) and Equation (9), Tc can be further expressed as a function of N ₁ , the value of N ₁ in the optimal solution can be obtained by the method of finding the most value, and then all N _i , i= 1,2,…,m is the best allocation scheme.

4. Calculate the case set under different price adjustment strategies, and analyze the statistical results (including the 9th and 10th steps)

The Excel tool was used for calculation and analysis, and the results were visualized.

Step 9: Get Input Data:

The simulation input data includes the information price of the first step, the hierarchical parameters of the second step, the price trend of the fourth step, the information base price entered by the user, the contract ex-factory price, the consumption, and the number of simulation cases.

Step 10: Run the output, analyze the results:

The parameters obtained in step 9 and the case generated in step 8 are brought into the model according to formula (3) to formula (6) for calculation, and the result is obtained by analyzing the output.

P _j =P ₀ *(1+U _j )#(5)

Among them, j is the month number in the contract period, j=1,2...m, the price volatility of the jth month, Mj is the information price index of the materials in the _jth month announced by the price authority of the base period signed in the contract, M ₀ is the price of the base period signed in the contract; U _j is the price volatility of the jth month after considering the risk sharing, where k is the number of the volatility layer, k=1, 2...n, α _k is the fluctuation interval of the kth layer The upper bound of β _k is the proportion of the owner’s share of the risk of the price adjustment difference in the fluctuation range of the kth layer, which is [0, 1]; P _j is the material price settlement price of the jth month, and P ₀ is the material contract The ex-factory price; S is the total settlement amount in m months, and Q _j is the material consumption in the jth month.

Result analysis is based on statistical analysis to determine the deviation rate of the settlement difference, the goodness of fit between the adjusted price volatility and the unchanged price, the goodness of fit between the adjusted price volatility curve and the original volatility curve, and the monthly settlement price. The distribution of these four indicators is described.

The deviation rate _μ of the settlement difference represents the difference between the total settlement amount after price adjustment and the total settlement amount without price adjustment. The smaller the value, the better the stability of the price adjustment strategy. The goodness of fit R ₁ ² of the price does not change), U is the mean value of U _j , the value of R ₁ ² is between [0, 1], the closer the value is to 1, the closer the settlement price is to the factory The value of R 2 2 is between [0, 1], and the value of the goodness of fit R ₂ ² between the adjusted price volatility (U _j ) curve and the original volatility (V _j ) curve is between [0, 1]. The closer it is to 1, the closer the generated settlement price is to the actual market situation, that is, the better the marketability.

The monthly settlement price is shown in the box chart shown in Figure 3. Each horizontal box represents the statistical distribution of the calculation results of a price adjustment strategy: the monthly settlement price P generated by the simulation case generated under the price adjustment strategy is calculated separately. _j (or total settlement), and arrange the monthly settlement prices P _j (or total settlement) in ascending order. The upper and lower ends of the leading thin line respectively represent the highest and lowest monthly settlement unit price (or total settlement) generated by the generated case set under this price adjustment strategy; the upper and lower ends of the box-shaped entity represent the quartile 75% and the quartile 25% The monthly settlement unit price (or the total settlement amount) at ; the horizontal line in the middle of the box represents the median; the × symbol is the average price adjustment. The overall position of the box represents the level of the price adjustment difference generated by this price adjustment strategy; the length of the box represents the fluctuation range of the price adjustment. The shorter the box chart, the better the stability, and vice versa, the better the marketability.

Example 2:

A simulation-based engineering material procurement price adjustment simulation system includes an input module, a case generation module, a simulation calculation module, and a result display module.

The input module is used to collect parameters required for simulation, including price adjustment strategies, price trends and data related to calculation models. The price adjustment strategy is a combination of different parameters. The parameters include the number of fluctuation intervals n, the boundary of the fluctuation interval α _k , the risk sharing ratio of the fluctuation interval β _k and the source of the information price M _j ; the price trend includes the number of segments of the fluctuation curve and the fluctuation type of the curve within the segment; The relevant data of the calculation model include contract ex-factory price P ₀ , information base price M ₀ , consumption Q _j and historical price, and the number of simulation cases N.

The case generation module obtains the historical price fluctuation distribution and the monthly price fluctuation distribution according to the price trend and historical data, obtains the first point in the segment according to the historical price fluctuation distribution, generates subsequent points according to the monthly price fluctuation distribution, and uses the OCBA algorithm Optimization, generate simulation cases in stages, each stage adjusts the number of cases generated in each fluctuation interval of the next stage according to the distribution and distribution measurement rules of the generated case data, until a specified number of case sets are generated.

The simulation calculation module brings the case set of the case generation module into the model calculation under different price adjustment strategies input by the input module.

The result display module displays the calculation results of a large number of cases under different price adjustment strategies in the form of box plots, including the monthly settlement unit price distribution adjusted by different price adjustment strategies and the total settlement distribution adjusted by different price adjustment strategies. Each box represents the statistical distribution of the calculation results of a price adjustment strategy. The box plot contains the highest, lowest, quartile, median and mean of the settlement price (or settlement amount) of different price adjustment strategies. The level represents the price adjustment difference generated by the price adjustment strategy. The length of the box represents the price adjustment fluctuation range. The shorter the box, the better the stability, and vice versa, the better the marketability.

In the description of the present invention, it should be understood that the terms "counterclockwise", "clockwise", "longitudinal", "horizontal", "upper", "lower", "front", "rear", "left", The orientation or positional relationship indicated by "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the purpose of It is convenient to describe the present invention, not to indicate or imply that the device or element referred to must have a particular orientation, be constructed and operate in a particular orientation, and therefore should not be construed as a limitation of the present invention.

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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