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

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

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CN111369283A
CN111369283A CN202010131578.6A CN202010131578A CN111369283A CN 111369283 A CN111369283 A CN 111369283A CN 202010131578 A CN202010131578 A CN 202010131578A CN 111369283 A CN111369283 A CN 111369283A
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刘振元
王兆成
马东伟
曾伟
钟卫华
陈晞
陈华林
董志荣
张振东
樊垚堤
陈曦
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Huazhong University of Science and Technology
Yalong River Hydropower Development Co Ltd
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Abstract

本发明公开了一种基于仿真的工程物资采购调价策略选择方法,包括以下步骤:基于工程物资采购调价策略所涉及的各项数据,选取不同参数组合,生成多种调价策略;分析价格影响因素及历史数据,选取价格走势;生成选定价格走势下的仿真案例集合;在多种不同调价策略下对仿真案例集合进行计算,统计结果进行分析。本发明通过对历史价格数据的分析,结合价格影响因素确定走势,使用仿真优化算法生成贴近实际的仿真案例,运用统计学方法对结果进行分析,旨在选取符合要求的调价策略,分析结果可视化,辅助决策者做出决策。

Figure 202010131578

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.

Figure 202010131578

Description

一种基于仿真的工程物资采购调价策略选择方法及系统A simulation-based method and system for selecting price adjustment strategy for engineering material procurement

技术领域technical field

本发明属于仿真分析技术领域,具体涉及一种基于仿真的工程物资采购调价策略选择方法及系统。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.

背景技术Background technique

在长期工程项目中,受多方面因素影响,物资价格变化难以预测,而由于消耗量巨大,细微的价格变化可能会导致合同期内高达数百万元的结算金额变动,合理设置物资调价策略具有重要意义。在工程物资采购招标中,它可以使投标人在投标时无需考虑价格上涨的风险费用,使招投标制度更加合理,而对招标人来说,更利于获得价格较低的投标人。在工程项目施工中,承包合同中存在价格调整条款可以使承包商避免在项目建设过程中出现资金亏损,业主不必担心承包商因为资金亏损而导致工程延期或低质量交付。总之,物资调价策略的合理设计既有利于业主在工程实施中有效控制成本,保障物资供给稳定性,也有助于投标人(物资供应商)合理调整竞标价格,从而提高投标的竞争力。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.

工程物资调价策略的制定是物资采购招标中的一项重要内容,也是一项难题。物资调价策略的理论框架以及其中的调价差额的风险分担方面有一些研究,但是对于分层次的处理价格波动以及调价策略中具体参数的选取确定目前尚未有深入研究。在系统仿真及优化领域,相应的理论或算法已有很多研究,这些理论及应用方法可为物资调价策略的研究提供借鉴。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.

因此,现阶段需要提供一种基于仿真的工程物资采购调价策略选择方法及系统来克服上述缺陷。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.

发明内容SUMMARY OF THE INVENTION

本发明目的在于提供一种基于仿真的工程物资采购调价策略选择方法及系统,用于解决上述现有技术中存在的技术问题之一,如:工程物资调价策略的制定是物资采购招标中的一项重要内容,也是一项难题。物资调价策略的理论框架以及其中的调价差额的风险分担方面有一些研究,但是对于分层次的处理价格波动以及调价策略中具体参数的选取确定目前尚未有深入研究。在系统仿真及优化领域,相应的理论或算法已有很多研究,这些理论及应用方法可为物资调价策略的研究提供借鉴。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.

为实现上述目的,本发明所采用的技术方案是:For achieving the above object, the technical scheme adopted in the present invention is:

一种基于仿真的工程物资采购调价策略选择方法,包括以下步骤:A method for selecting a price adjustment strategy for engineering material procurement based on simulation, comprising the following steps:

S1、基于工程物资采购调价策略所涉及的各项数据,选取不同参数组合,生成多种调价策略;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、分析价格影响因素及历史数据,选取价格走势;S2. Analyze price influencing factors and historical data, and select price trends;

S3、在步骤S2的基础上,生成选定价格走势下的仿真案例集合;S3. On the basis of step S2, generate a set of simulation cases under the selected price trend;

S4、结合步骤S1和S3,在多种不同调价策略下对仿真案例集合进行计算,统计结果进行分析。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.

步骤S1选取的参数包括信息价格,价格波动区间数目、价格波动区间界线、波动区间的风险分担比例,不同参数组合产生不同的调价策略。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.

步骤S2的历史数据分析是根据物资历史价格波动率的分布划分价格波动类别,分布以频率分布表进行描述,将波动区间细化分层并统计历史波动率在各个小区间出现的频率,价格走势的选取包括波动曲线的分段数以及每段曲线的波动类别。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.

步骤S3的仿真案例由代表月度价格波动率的一系列离散点组成,具体生成包含以下步骤: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:

分析历史数据得到物资价格月度环比波动率分布;Analyze historical data to obtain the monthly month-on-month volatility distribution of material prices;

根据选定的曲线分段数以及波动类别生成段内曲线,段内曲线第一个点根据所述的历史价格波动率分布生成,后续点根据月度环比波动率生成;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;

将生成的曲线段进行连接,连接处前后离散点数值差异不能过大,形成多个完整的仿真案例。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.

案例生成时使用OCBA算法进行优化,分阶段生成仿真案例,每个阶段根据已生成的案例数据分布和分配计量规则,调整下一阶段各波动区间内生成的案例数目。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.

步骤计量分配原则中,将大的波动区间细化分成的若干小的区间视为不同方案,该小区间在大的波动区间内出现的频率视为该方案的性能值。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.

步骤S4的统计分析以结算差额的偏差率、调整后的价格波动率与价格未发生变化的拟合优度、调整后的价格波动率曲线和原波动率曲线的拟合优度、月结算价的分布这四项指标进行刻画。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.

月结算价以箱型图来展现,箱型图中包含结算单价最高、最低值,四分位值、中位数以及均值,箱体整体高低代表该调价策略产生的调价差额的高低,箱体的长短代表调价波动范围,越短则代表稳定性越好,反之代表市场性更好。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.

其中,选取不同参数组合,生成多种调价策略;Among them, different parameter combinations are selected to generate a variety of price adjustment strategies;

具体地,计算模型为:Specifically, the calculation model is:

第j月的价格波动率:Price volatility in month j:

Figure BDA0002395903620000021
Figure BDA0002395903620000021

其中,j为合同期限中的月份编号,j=1,2…m,Mj为合同中签订的基期价格权威机构公布的物资在第j月的信息价格指数,M0为合同中签订的基期价格;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;

为考虑风险分担后,第j月的价格波动率:In order to take into account the risk sharing, the price volatility of the jth month:

Figure BDA0002395903620000031
Figure BDA0002395903620000031

其中,k为波动层编号,k=1,2…n,αk为第k层波动区间的上界,βk为在第k层波动区间中业主对调价差额风险的分担比例,取值为[0,1]。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].

第j月的物资价格结算价:Material price settlement price of the jth month:

Pj=P0*(1+Uj)#(3)P j =P 0 *(1+U j )#(3)

其中,P0为物资的合同出厂价。Among them, P 0 is the contract ex-factory price of materials.

m个月的总结算额:The total settlement amount for m months:

Figure BDA0002395903620000032
Figure BDA0002395903620000032

需要选取的参数包括信息价格Mj、分价格波动区间数目n、价格波动区间界线αk、波动区间的风险分担比例βk,不同参数组合产生不同的调价策略。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.

分析价格影响因素及历史数据,选取价格走势;Analyze price influencing factors and historical data, and select price trends;

价格影响因素包括成本因素、市场环境竞争因素和供求关系变化;历史数据分析是根据物资历史价格波动率的分布划分价格波动类别,分布以频率分布表进行描述,将波动区间细化分层并统计历史波动率在各个小区间出现的频率,价格走势的选取包括波动曲线的分段数以及每段曲线的波动类别。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.

生成选定走势下的仿真案例集合;Generate a set of simulation cases under the selected trend;

仿真案例由代表月度价格波动率的一系列离散点组成,具体包含以下步骤:The simulation case consists of a series of discrete points representing monthly price volatility, and includes the following steps:

分析历史数据得到物资价格月度环比波动率分布;Analyze historical data to obtain the monthly month-on-month volatility distribution of material prices;

根据选定的曲线分段数以及波动类别生成段内曲线,段内曲线第一个点由步骤(2)中所述的历史价格波动率分布生成,后续点由月度环比波动率生成;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;

将生成的曲线段连接形成完整的案例,连接处前后离散点数值差异不能过大;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;

案例生成时同时使用OCBA算法进行优化,分阶段生成仿真案例,每个阶段根据已生成的案例数据分布和分配计量规则,调整下一阶段各波动区间内生成的案例数目。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.

计量分配原则中,将大的波动区间细化分成的若干小的区间视为不同方案,该小区间在大的波动区间内出现的频率视为该方案的性能值。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.

在不同调价策略下对案例集合进行计算,统计结果进行分析。The case set is calculated under different price adjustment strategies, and the statistical results are analyzed.

统计分析以结算差额的偏差率、调整后的价格波动率与价格未发生变化的拟合优度、调整后的价格波动率曲线和原波动率曲线的拟合优度、月结算价的分布这四项指标进行刻画。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.

结算差额的偏差率μ:Deviation rate μ of settlement difference:

Figure BDA0002395903620000041
Figure BDA0002395903620000041

μ表示调价后的结算总额与不调价的结算总额之间的差异程度,越小表示调价策略的稳定性越好。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.

调整后的价格波动率(Uj)与0(0表示价格未发生变化)的拟合优度R1 2Goodness-of-fit R 1 2 of the adjusted price volatility (U j ) to 0 (0 means the price has not changed):

Figure BDA0002395903620000042
Figure BDA0002395903620000042

U为Uj的均值,R1 2的取值在[0,1]之间,其值越接近于1说明产生的结算价更贴近出厂价,即稳定性更好。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.

调整后的价格波动率(Uj)曲线和原波动率(Vj)曲线的拟合优度R2 2The goodness of fit R 2 2 of the adjusted price volatility (U j ) curve and the original volatility (V j ) curve:

Figure BDA0002395903620000043
Figure BDA0002395903620000043

R2 2的取值在[0,1]之间,其值越接近于1说明产生的结算价与市场实际情况更贴近,即市场性越好。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.

月结算价的分布情况:Distribution of monthly settlement prices:

月结算价以箱型图来展现,横向的每个箱体代表一种调价策略的计算结果统计分布:分别计算生成的仿真案例在该调价策略下产生的月结算价Pj(也可结合消耗量考虑结算总额),并将月结算价Pj按照从小到大的顺序排列。引出细线的上下端分别代表生成的案例集合在这种调价策略下产生的最高、最低月结算单价;箱型实体的上下端表示四分位75%、四分位25%处的月结算单价;箱体中间的横线代表中位数;×符号处为调价均值。箱体整体位置的高低代表了这种调价策略产生的调价差额的高低;箱体长短代表了调价额波动范围,箱型图越短,代表稳定性越好,反之则代表市场性更好一些。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.

此外,本发明还提供一种基于仿真的工程物资采购调价仿真系统,包括输入模块、案例生成模块、仿真计算模块、结果展示模块。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.

所述输入模块用于收集仿真所需的参数,包含调价策略,价格走势和计算模型相关数据。调价策略即不同的参数组合,参数包括波动区间数目n、波动区间界线αk、波动区间风险分担比例βk和信息价格Mj来源;价格走势包括波动曲线分段数和段内曲线波动类别;计算模型相关数据包括合同出厂价P0、信息基准价M0、消耗量Qj和历史价格,仿真案例数N。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.

所述案例生成模块根据价格走势以及历史数据,得到历史价格波动分布与月度环比价格波动分布,根据历史价格波动分布得到段内首个点,根据月度环比价格波动分布生成后续点,并使用OCBA算法优化,分阶段生成仿真案例,每个阶段根据已生成的案例数据分布和分配计量规则,调整下一阶段各波动区间内生成的案例数目,直到生成指定数目的案例集合。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.

所述仿真计算模块在输入模块输入的不同调价策略下将案例生成模块的案例集合带入模型计算。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.

所述结果展示模块以箱型图的形式来展现大量案例在不同种调价策略下的计算结果,包括采用不同调价策略调整后的月结算单价分布和不同调价策略调整后的结算总额分布,横向的每个箱体代表一种调价策略的计算结果统计分布,箱型图中包含不同调价策略的结算价(或结算额)最高、最低值、四分位值、中位数和均值,箱体整体高低代表该调价策略产生的调价差额的高低,箱体的长短代表调价波动范围,越短则代表稳定性越好,反之代表市场性更好。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.

本发明的有益技术效果是:通过对历史价格数据的分析,结合价格影响因素确定走势,使用仿真优化算法生成贴近实际的仿真案例,运用统计学方法对结果进行分析,旨在选取符合要求的调价策略,分析结果可视化,辅助决策者做出决策。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.

附图说明Description of drawings

图1是本发明提供的工程物资采购调价仿真方法的流程图。FIG. 1 is a flowchart of a simulation method for price adjustment of engineering material procurement provided by the present invention.

图2是本发明提供的工程物资采购调价仿真方法中采用OCBA方法优化案例生成流程图。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.

图3是本发明实施例的箱型图示意图。FIG. 3 is a schematic diagram of a box diagram according to an embodiment of the present invention.

具体实施方式Detailed ways

下面结合本发明的附图1-3,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。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.

实施例1:Example 1:

一种基于仿真的工程物资采购调价策略选择方法与系统,包括如下:A method and system for selecting a price adjustment strategy for engineering material procurement based on simulation, including the following:

一、选取不同参数组合,生成多种调价策略(包含第1、第2个步骤)1. Select different parameter combinations to generate a variety of price adjustment strategies (including the first and second steps)

第1步:信息价格选取:Step 1: Information price selection:

信息价格的组合选取有4种方式:方式1只选用中国水泥网上公布的四川省水泥信息价格指数;方式2只选用中国价格信息网公布的水泥信息价格指数;方式3选用信息价格1和信息价格2;方式4选用信息价格1和中国价格信息网上公布的四川省煤电信息价格指数。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.

第2步:层次参数选取:Step 2: Layer parameter selection:

信息价格确定后,需对3个层次参数进行选取:信息价格的波动层数、波动层的上下界线和各个波动层中风险分担比例。当为2层时,每层的区间为[0,0.2]、(0.2,0.5]和[0,0.3]、(0.3,0.5]两种情况,分担率取(0,0.5)和(0,0.4)两种情况;当为3层时,每层的区间分为[0,0.05]、(0.05,0.2]、(0.2,0.5]和[0,0.05]、(0.05,0.3]、(0.3,0.5]两种情况。分担率取(0,1,0.5)和(0,1,0.4)两种情况;当信息价格的波动率超过0.5时,具体的分担比例再由合同双方协商确定。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.

不同的信息价格和层次的组合可形成若干调价策略。The combination of different information prices and levels can form several price adjustment strategies.

二、分析价格影响因素及历史数据,选取价格走势(包含第3、第4两个步骤);2. Analyze price influencing factors and historical data, and select price trends (including steps 3 and 4);

第3步:分析价格影响因素及历史数据:Step 3: Analyze price influencing factors and historical data:

价格影响因素包括成本因素、市场环境竞争因素和供求关系变化;历史数据分析是根据物资历史价格波动率的分布划分价格波动类别,分布以频率分布表进行描述,将波动区间细化分层并统计历史波动率在各个小区间出现的频率,价格走势的选取包括波动曲线的分段数以及每段曲线的波动类别。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.

第4步:选取价格走势:Step 4: Select the price trend:

根据第3步的分析结果,选取波动曲线的分段数目以及每段曲线的波动类别。According to the analysis results in step 3, select the number of segments of the fluctuation curve and the fluctuation type of each segment.

三、生成选定走势下的仿真案例集合(包含第5、第6、第7三个步骤)3. Generate a set of simulation cases under the selected trend (including the 5th, 6th, and 7th steps)

在Windows 10下利用JDK1.8编程实现数据处理,包括历史数据分析、仿真案例优化生成。Using JDK1.8 programming under Windows 10 to realize data processing, including historical data analysis, simulation case optimization generation.

第5步:历史数据分析:Step 5: Historical Data Analysis:

分析历史数据,得到物资价格月度环比波动率分布;Analyze historical data to obtain the monthly month-on-month volatility distribution of material prices;

第6步:生成段内曲线:Step 6: Generate Intrasegment Curves:

根据第4步选定的曲线分段数以及波动类别生成段内曲线,段内曲线第一个点根据第3步中的历史价格波动率分布生成,后续点根据第5步中的月度环比波动率生成。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.

第7步:形成完整仿真案例:Step 7: Form a complete simulation case:

生成的段内曲线连接形成完整的案例,连接处前后离散点数值差异不能过大。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.

第8步:仿真案例生成优化:Step 8: Simulation case generation optimization:

同时使用OCBA算法进行优化,流程图如图2所示,分阶段生成仿真案例,每个阶段根据已生成的案例数据分布和分配计量规则,调整下一阶段各波动区间内生成的案例数目,直至生成指定数目的案例。计量分配原则中,将大的波动区间细化分成的若干小的区间视为不同方案,该小区间在大的波动区间内出现的频率视为该方案的性能值。则最优解满足: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:

Figure BDA0002395903620000071
Figure BDA0002395903620000071

其中,Ji为第i种方案的性能指标,即该方案的历史出现频率,设共有m种方案;Ni为第i种方案下一次分配的案例数目;σi为已分配的案例集合中第i种方案出现频率与历史频率间的绝对误差;设J1≥J2≥…≥JmAmong 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 .

由式(8)可知,第i种方案的性能指标Ji越大,Ni值就越大,即该波动区间历史出现频率越高,下一次案例分配时该区间就会分配到更多案例;绝对误差σi越大,Ni值越小,即该波动区间出现的频率已经大于其历史频率较多时,在下一次案例分配时该区间内会分配到更少案例。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为下一次分配的总体案例。则:Tc is the overall case for the next assignment. but:

Tc=N1+N2+…+Nm#(2)Tc=N 1 +N 2 +...+N m #(2)

结合式(8)和式(9),进一步可将Tc表示为关于N1的函数,通过求最值的方法可求得最优方案中N1值,进而求得所有的Ni,i=1,2,…,m,就是最佳的分配方案。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.

四、在不同调价策略下对案例集合进行计算,统计结果进行分析(包含第9、第10两个步骤)4. Calculate the case set under different price adjustment strategies, and analyze the statistical results (including the 9th and 10th steps)

采用Excel工具进行计算并分析,并对结果进行可视化展示。The Excel tool was used for calculation and analysis, and the results were visualized.

第9步:获取输入数据:Step 9: Get Input Data:

仿真输入数据包括第1步的信息价格,第2步的层次参数,第4步的价格走势、用户输入的信息基准价、合同出厂价、消耗量,仿真案例数。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.

第10步:运行输出,分析结果:Step 10: Run the output, analyze the results:

将第9步获取的参数第8步生成的案例带入按式(3)~式(6)的模型进行计算,分析输出获得结果。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.

Figure BDA0002395903620000072
Figure BDA0002395903620000072

Figure BDA0002395903620000081
Figure BDA0002395903620000081

Pj=P0*(1+Uj)#(5)P j =P 0 *(1+U j )#(5)

Figure BDA0002395903620000082
Figure BDA0002395903620000082

其中,j为合同期限中的月份编号,j=1,2…m,第j月的价格波动率,Mj为合同中签订的基期价格权威机构公布的物资在第j月的信息价格指数,M0为合同中签订的基期价格;Uj为考虑风险分担后,第j月的价格波动率,其中,k为波动层编号,k=1,2…n,αk为第k层波动区间的上界,βk为在第k层波动区间中业主对调价差额风险的分担比例,取值为[0,1];Pj为第j月的物资价格结算价,P0为物资的合同出厂价;S为m个月的总结算额,Qj为第j月的物资消耗量。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.

结果分析以统计分析以结算差额的偏差率、调整后的价格波动率与价格未发生变化的拟合优度、调整后的价格波动率曲线和原波动率曲线的拟合优度、月结算价的分布这四项指标进行刻画。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.

Figure BDA0002395903620000083
Figure BDA0002395903620000083

Figure BDA0002395903620000084
Figure BDA0002395903620000084

Figure BDA0002395903620000085
Figure BDA0002395903620000085

结算差额的偏差率μ表示调价后的结算总额与不调价的结算总额之间的差异程度,越小表示调价策略的稳定性越好;整后的价格波动率(Uj)与0(0表示价格未发生变化)的拟合优度R1 2,U为Uj的均值,R1 2的取值在[0,1]之间,其值越接近于1说明产生的结算价更贴近出厂价,即稳定性更好;调整后的价格波动率(Uj)曲线和原波动率(Vj)曲线的拟合优度R2 2的取值在[0,1]之间,其值越接近于1说明产生的结算价与市场实际情况更贴近,即市场性越好。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.

月结算价以如图3所示的箱型图来展现,横向的每个箱体代表一种调价策略的计算结果统计分布:分别计算生成的仿真案例在该调价策略下产生的月结算价Pj(或结算总额),并将月结算价Pj(或结算总额)按照从小到大的顺序排列。引出细线的上下端分别代表生成的案例集合在这种调价策略下产生的最高、最低月结算单价(或结算总额);箱型实体的上下端表示四分位75%、四分位25%处的月结算单价(或结算总额);箱体中间的横线代表中位数;×符号处为调价均值。箱体整体位置的高低代表了这种调价策略产生的调价差额的高低;箱体长短代表了调价额波动范围,箱型图越短,代表稳定性越好,反之则代表市场性更好一些。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.

实施例2:Example 2:

一种基于仿真的工程物资采购调价仿真系统,包括输入模块、案例生成模块、仿真计算模块、结果展示模块。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.

所述输入模块用于收集仿真所需的参数,包含调价策略,价格走势和计算模型相关数据。调价策略即不同的参数组合,参数包括波动区间数目n、波动区间界线αk、波动区间风险分担比例βk和信息价格Mj来源;价格走势包括波动曲线分段数和段内曲线波动类别;计算模型相关数据包括合同出厂价P0、信息基准价M0、消耗量Qj和历史价格,仿真案例数N。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.

所述案例生成模块根据价格走势以及历史数据,得到历史价格波动分布与月度环比价格波动分布,根据历史价格波动分布得到段内首个点,根据月度环比价格波动分布生成后续点,并使用OCBA算法优化,分阶段生成仿真案例,每个阶段根据已生成的案例数据分布和分配计量规则,调整下一阶段各波动区间内生成的案例数目,直到生成指定数目的案例集合。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.

所述仿真计算模块在输入模块输入的不同调价策略下将案例生成模块的案例集合带入模型计算。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.

所述结果展示模块以箱型图的形式来展现大量案例在不同种调价策略下的计算结果,包括采用不同调价策略调整后的月结算单价分布和不同调价策略调整后的结算总额分布,横向的每个箱体代表一种调价策略的计算结果统计分布,箱型图中包含不同调价策略的结算价(或结算额)最高、最低值、四分位值、中位数和均值,箱体整体高低代表该调价策略产生的调价差额的高低,箱体的长短代表调价波动范围,越短则代表稳定性越好,反之代表市场性更好。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.

在本发明的描述中,需要理解的是,术语“逆时针”、“顺时针”“纵向”、“横向”、“上”、“下”、“前”、“后”、“左”、“右”、“竖直”、“水平”、“顶”、“底”、“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系,仅是为了便于描述本发明,而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作,因此不能理解为对本发明的限制。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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