CN113537684B - Overseas refining chemical engineering risk management control method under multi-target coupling constraint - Google Patents
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Abstract
The invention provides an oversea chemical industry risk management control method under multi-target coupling constraint, which comprises the following steps: step 1, risk analysis and variable setting are carried out; step 2, performing variable correlation analysis; step 3, calculating risk factor probability; step 4, establishing a multi-objective constraint risk control model based on the interrelation between various risk losses and various risk factor occurrence probabilities; step 5, carrying out scheme optimization calculation; and 6, verifying the prediction scheme. The oversea chemical engineering risk management control method under the multi-target coupling constraint can overcome the defects of traditional qualitative or semi-quantitative risk management, can simulate a model structure under more real conditions, and can effectively assist in understanding and measuring the complexity degree of project risk factors and assist in the risk management decision of related offshore projects through the risk factors or risk loss prediction.
Description
Technical Field
The invention relates to the technical field of risk control, in particular to an oversea chemical industry risk management control method under multi-target coupling constraint.
Background
In recent years, more and more Chinese enterprises are beginning to be put into the large arena of the international market. Overseas engineering projects are always accompanied by numerous risk factors, are limited by political economic situations, foreign relations, exchange rate fluctuation, related policies and legal regulations and other aspects of the country where the engineering is located, and are also often subjected to constraint influences of different owners, different technical specifications, different geographies and climate conditions. There are a number of open sea projects that fail due to inadequate risk management control. How to effectively perform risk management control is a key for ensuring smooth implementation of overseas chemical engineering projects. However, at present, the risk management about overseas refining engineering projects is mostly qualitative management, quantitative risk management is reported recently, and particularly quantitative risk management under multi-objective coupling constraint is rare.
Therefore, the overseas refining chemical engineering risk management control method under the multi-target coupling constraint is invented, and the technical problems are solved.
Disclosure of Invention
The invention aims to provide the oversea chemical industry risk management control method under the multi-objective coupling constraint, which realizes quantitative evaluation and management of overseas quantitative engineering project risks, effectively assists in understanding and measuring the complexity of project risk factors and assists in the risk management decision of related oversea projects.
The aim of the invention can be achieved by the following technical measures: the oversea chemical industry risk management control method under the multi-target coupling constraint comprises the following steps: step 1, risk analysis and variable setting are carried out; step 2, performing variable correlation analysis; step 3, calculating risk factor probability; step 4, establishing a multi-objective constraint risk control model based on the interrelation between various risk losses and various risk factor occurrence probabilities; step 5, carrying out scheme optimization calculation; and 6, verifying the prediction scheme.
The aim of the invention can be achieved by the following technical measures:
in the step 1, the quantitative recognition of engineering risks is carried out by adopting a sensitivity analysis method and a struggle value method on the basis of qualitative risk analysis according to specific conditions such as engineering characteristics, natural social environment in which the engineering is positioned, engineering implementation stage and mastered information resources, the specific risks of projects which are received and analyzed quantitatively are screened out as input variables according to risk grade standards, and the linkage relation between the input variables and affected expense subjects is established.
In step 1, according to the setting difference of the input variables, obtaining a price estimation based on risks and unpredictable fees based on risk preference, and further quantitatively analyzing the influence of risks with higher risk grades on the overall fees of the project.
In step 1, the risk itself selected as the input variable is set as a probability distribution, or the probability distribution is set as the risk occurrence probability and the influence degree, respectively, and the type of the probability distribution is obtained by combining historical data fitting with expert experience.
In the step 2, on the basis of risk quantitative identification, an engineering risk list is established, and the correlation of input variables is set; the engineering risk list should include the following: detailed division and description of engineering risks; reasons, time periods and affected engineering ranges of engineering risks; probability and consequences of engineering risk occurrence; obtaining a correlation coefficient through historical data calculation, and determining the correlation and the correlation degree between risks; after the input variables and the distribution types thereof are quantized and defined, and the correlation between risks is established, quantization simulation and analysis are performed.
In step 3, according to a similar engineering windQuantitative analysis is carried out on probability statistics data of risks, conditions of the engineering and the environment of the engineering based on an engineering risk list, and probability x of occurrence of various risk factors is obtained i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2, … n, n is the number of risk factors; when the probability or probability distribution aspect of the risk event has full and complete historical data and data, a theoretical probability distribution method, a Monte Carlo simulation method and a key event method are adopted to obtain the risk occurrence probability; when the probability or probability distribution aspect of the risk event does not have enough historical data and data, an expert judgment method is adopted to give the probability of risk occurrence; when the probability or probability distribution of the risk event has certain historical data and data, the risk occurrence probability is given by adopting a hierarchical analysis method, a fuzzy mathematical method and a sensitivity analysis method.
In step 4, the risk control model of the multi-objective constraint is established as follows:
in the formula, F (X) =min (P (X)) is an objective function equation; f (X) is a K-dimensional objective function, K is the number of project risk categories, P k (X) is the loss due to risk of developing class K; psi (X) is less than or equal to G and is m-dimensional constraint equation, m is the number of constraint conditions,a constraint function corresponding to the mth constraint condition; g is m-dimension Chang Xiangliang, G m The value corresponding to the mth constraint condition is taken; x is X i The probability of occurrence of the ith risk factor, where i=1, 2, … n, n is the number of risk factors, and the risk of a certain category contains several risk factors, including such risk factors as exchange rate fluctuation, expansion in currency, interest rate fluctuation, tax/tariff differences.
In step 5, the risk control model formed by the objective function equation and the boundary constraint equation in step 4 is solved by adopting a penalty function method, and the boundary constraint equation is solved by adopting a subspace cut-off Newton method, so that the corresponding occurrence probability X of various risk factors meeting the risk loss minimization condition is obtained i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2, … n, n is the number of risk factors.
In step 6, the i-th risk factor occurrence probability X which meets the risk loss minimization condition and is obtained by solving in step 5 i Probability x of occurrence of corresponding risk factor obtained in step 3 i For comparison, if x in step 3 i Less than or equal to the risk factor occurrence probability X after the risk optimization control obtained in the step 5 i Then the risk control target can be considered to be reached, and the project can be implemented; where i=1, 2, … n, n is the number of risk factors.
According to the oversea chemical industry risk management control method under the multi-objective coupling constraint, based on the purpose of minimizing project risk loss, the multi-objective optimization control thought is utilized to solve and obtain corresponding occurrence probability requirements of various risks meeting the risk loss minimization condition, and as long as the occurrence probability of various risks is not larger than the corresponding target value obtained by solving, the project risk is considered to be controllable, and the project can be implemented. The invention can overcome the defects of traditional qualitative or semi-quantitative risk management, focuses on the correlation among risks and the mutual influence between the risks and the environments where the projects are located, so as to simulate the model structure under more practical conditions, effectively assist in understanding and measuring the complexity degree of the project risk factors through the risk factors or the risk loss prediction, and assist in the risk management decision of related offshore projects. Compared with the prior art, the invention has the following advantages:
the method can overcome the defects of traditional qualitative or semi-quantitative risk management, focuses on the correlation among risks and the interaction between the risks and the environments where the projects are located, so as to simulate model structures under more practical conditions, effectively assist in understanding and measuring the complexity degree of the project risk factors through the risk factors or risk loss prediction, and assist in the risk management decision of related offshore projects.
Drawings
FIG. 1 is a flow chart of one embodiment of a method of overseas process risk management control under the multi-objective coupling constraints of the present invention.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects solved by the invention more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, fig. 1 is a flowchart of an oversea chemical industry risk management control method under the multi-objective coupling constraint of the present invention.
Step 101, risk analysis and variable setting
Weighing according to specific conditions of engineering characteristics, natural social environment where the engineering is located, engineering implementation stage, mastered information resources and the like, and quantitatively identifying engineering risks by adopting a sensitivity analysis method, a struggle value method and the like on the basis of qualitative risk analysis; and screening the project specific risks subjected to the cashing quantitative analysis as input variables according to the risk level standard, and establishing a linkage relation between the input variables and the affected expense subjects.
According to the setting difference of the input variables, the price estimation based on risks and the unpredictable fee based on the risk preference are obtained, and then the influence of risks with higher risk level on the overall cost of the project is quantitatively analyzed.
The risk itself selected as the input variable is set as a probability distribution, or the probability distribution is set as the risk occurrence probability and the influence degree, respectively, and the type of the probability distribution is obtained by combining historical data fitting with expert experience.
Step 102, variable correlation analysis
On the basis of the risk quantitative identification, an engineering risk list (table 1) is established.
TABLE 1 engineering risk list
Setting the correlation of input variables, calculating through historical data to obtain a correlation coefficient, and determining the correlation and the correlation degree between risks. After the input variables and the distribution types thereof are quantized and defined, and the correlation between risks is established, quantization simulation and analysis are performed.
Step 103, risk factor probability calculation
According to similar engineering risk probability statistical data, engineering self conditions and environments, quantitative analysis is carried out on the occurrence probability of various risk factors based on an engineering risk list, and the occurrence probability x of various risk factors is obtained i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2, … n, n is the number of risk factors; when the probability or probability distribution aspect of the risk event has full and complete historical data and data, obtaining the probability of the risk event by adopting a theoretical probability distribution method, a Monte Carlo simulation method, a key event method and the like; when the probability or probability distribution aspect of the risk event does not have enough historical data and data, an expert judgment method is adopted to give the probability of risk occurrence; when the probability or probability distribution aspect of the risk event has certain historical data and data, the risk occurrence probability is given by adopting a hierarchical analysis method, a fuzzy mathematic method, a sensitivity analysis method and the like.
Step 104, risk control model establishment
Based on the interrelation between various risk losses and various risk factor occurrence probabilities, a multi-objective constraint risk control model is established:
in the formula, F (X) =min (P (X)) is an objective function equation; f (X) is a K-dimensional objective function, K is project windNumber of risk categories, P k (X) is the loss due to risk of developing class K; psi (X) is less than or equal to G and is m-dimensional constraint equation, m is the number of constraint conditions,a constraint function corresponding to the mth constraint condition; g is m-dimension Chang Xiangliang, G m And the value corresponding to the mth constraint condition is obtained. X is X i The i-th risk factor occurrence probability, where i=1, 2, … n, n is the number of risk factors, and a certain class of risk may include several risk factors, such as risk factors like exchange rate fluctuation, expansion in currency, interest rate fluctuation, tax/tariff differences, etc.
Step 105, scheme optimization calculation
Solving a risk control model formed by an objective function equation and a boundary constraint equation in step 104 by adopting a penalty function method, and solving the boundary constraint equation by adopting a subspace truncated Newton method to obtain the corresponding occurrence probability X of various risk factors meeting the risk loss minimization condition i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2, … n, n is the number of risk factors.
Step 106, predictive scheme verification
The ith risk factor occurrence probability X which meets the risk loss minimization condition and is obtained by solving in the step 105 i Probability x of occurrence of corresponding risk factor obtained in step 103 i For comparison, if x in step 103 i Less than or equal to the risk factor occurrence probability X after the risk optimization control obtained in the step 105 i Then the risk control target can be considered to be reached, and the project can be implemented; where i=1, 2, … n, n is the number of risk factors.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (1)
1. The oversea chemical industry risk management control method under the multi-target coupling constraint is characterized by comprising the following steps:
step 1, risk analysis and variable setting are carried out;
step 2, performing variable correlation analysis;
step 3, calculating risk factor probability;
step 4, establishing a multi-objective constraint risk control model based on the interrelation between various risk losses and various risk factor occurrence probabilities;
step 5, carrying out scheme optimization calculation;
step 6, verifying the prediction scheme;
in the step 1, the engineering risks are quantitatively identified by adopting a sensitivity analysis method and a struggle value method on the basis of qualitative risk analysis according to specific conditions such as engineering characteristics, a natural social environment in which the engineering is positioned, an engineering implementation stage and mastered information resources, the specific risks of projects which are received and analyzed quantitatively are screened out as input variables according to risk grade standards, and the linkage relation between the input variables and affected expense subjects is established;
according to the setting difference of the input variables, obtaining price estimation based on risks and unpredictable fees based on risk preference, and further quantitatively analyzing the influence of risks with higher risk grades on the overall expense of the project;
the risk itself selected as an input variable is set as probability distribution, or the probability of occurrence and the influence degree of the risk are respectively set as probability distribution, and the type of the probability distribution is obtained by combining historical data fitting with expert experience;
in the step 2, on the basis of risk quantitative identification, an engineering risk list is established, and the correlation of input variables is set; the engineering risk list should include the following: detailed division and description of engineering risks; reasons, time periods and affected engineering ranges of engineering risks; probability and consequences of engineering risk occurrence; obtaining a correlation coefficient through historical data calculation, and determining the correlation and the correlation degree between risks; after the input variables and the distribution types thereof are quantized and defined and the correlation among risks is established, performing quantization simulation and analysis;
in step 3, quantitative analysis is performed on the occurrence probability of various risk factors based on the engineering risk list according to similar engineering risk probability statistics, engineering self conditions and environments to obtain the occurrence probability x of various risk factors i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2, … n, n is the number of risk factors; when the probability or probability distribution aspect of the risk event has full and complete historical data and data, a theoretical probability distribution method, a Monte Carlo simulation method and a key event method are adopted to obtain the risk occurrence probability; when the probability or probability distribution aspect of the risk event does not have enough historical data and data, an expert judgment method is adopted to give the probability of risk occurrence; when a certain historical data and data exist in the aspect of probability or probability distribution of risk events, a hierarchical analysis method, a fuzzy mathematical method and a sensitivity analysis method are adopted to give risk occurrence probability;
in step 4, the risk control model of the multi-objective constraint is established as follows:
in the formula, F (X) =min (P (X)) is an objective function equation; f (X) is a K-dimensional objective function, K is the number of project risk categories, P k (X) is the loss due to risk of developing class K; psi (X) is less than or equal to G and is an m-dimensional constraint equation, m is the number of constraint conditions,a constraint function corresponding to the mth constraint condition; g is m-dimension Chang Xiangliang, G m The value corresponding to the mth constraint condition is taken; x is X i The probability of occurrence of the i-th risk factor, where i=1, 2, … n, n is the number of risk factors, and the risk of a certain class contains several windsRisk factors, including market environmental risk factors including exchange rate fluctuations, currency expansion, interest rate fluctuations, tax/tariff differences;
in step 5, the risk control model formed by the objective function equation and the boundary constraint equation in step 4 is solved by adopting a penalty function method, and the boundary constraint equation is solved by adopting a subspace cut-off Newton method, so that the corresponding occurrence probability X of various risk factors meeting the risk loss minimization condition is obtained i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2, … n, n is the number of risk factors;
in step 6, the i-th risk factor occurrence probability X which satisfies the risk loss minimization condition and is obtained by solving in step 5 i Probability x of occurrence of corresponding risk factor obtained in step 3 i For comparison, if x in step 3 i Less than or equal to the risk factor occurrence probability X after the risk optimization control obtained in the step 5 i Then the risk control target can be considered to be reached, and the project can be implemented; where i=1, 2, … n, n is the number of risk factors.
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| 基于***动力学的海外EPC工程风险评估研究――以乍得拉尼亚油田建设EPC工程项目为例;徐过;;中国安全生产科学技术(S2);第52-57页 * |
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