(A) Advantages
1. System analysis method:
Analytic Hierarchy Process (AHP) regards the research object as a system, and makes decisions in a way of decomposition, comparative judgment and synthesis, which has become an important tool for system analysis developed after mechanism analysis and statistical analysis.
2. Concise and practical decision-making methods:
This method does not simply pursue advanced mathematics, nor does it unilaterally focus on behavior, logic and reasoning, but organically combines qualitative methods with quantitative methods.
3. Need less quantitative data information:
Analytic Hierarchy Process (AHP) is mainly based on the evaluator's understanding of the essence and elements of evaluation problems, and it pays more attention to qualitative analysis and judgment than general quantitative methods.
(2) Disadvantages
1. Unable to provide a new scheme for decision:
The function of analytic hierarchy process is to select a better scheme from the alternatives. This function only shows that AHP can only choose from the original scheme, but can't provide new schemes for decision makers.
2. Less quantitative data, more qualitative components, unconvincing:
Nowadays, in the evaluation of scientific methods, it is generally believed that a science needs strict mathematical argumentation and perfect quantitative methods. But the problems in the real world and the process of thinking in the human brain are often not simply explained by numbers.
3. When there are too many indicators, the amount of data statistics is large and the weight is difficult to determine:
When we want to solve more common problems, the number of indicators may increase.
4. The exact solutions of eigenvalues and eigenvectors are complicated:
When finding the eigenvalues and eigenvectors of the judgment matrix, the method used is the same as that used in our multivariate statistics.
Advantages and disadvantages of fuzzy comprehensive evaluation method;
1 and the advantages of fuzzy comprehensive evaluation method;
Fuzzy evaluation can deal with fuzzy evaluation objects by precise digital means, and make a scientific, reasonable and realistic quantitative evaluation of data containing fuzzy information.
The evaluation result is a vector, not a point value, and contains rich information, which can not only accurately describe the evaluated object, but also further process it to get reference information.
2. Disadvantages of fuzzy comprehensive evaluation method:
The calculation is complicated and the determination of index weight vector is subjective;
When the index set U is large, that is, the number of index sets is always large, the weight coefficient of relative membership is often small when the sum of weight vectors is 1, and the weight vector does not match the fuzzy matrix R, so the result will be ultra-fuzzy, the resolution will be poor, and it is impossible to distinguish who has higher membership, or even the evaluation will fail. At this time, it can be improved by hierarchical fuzzy evaluation method.
Extended data:
Analytic Hierarchy Process (AHP) decomposes the problem into different components according to the nature of the problem and the overall goal to be achieved, and aggregates and combines the factors according to different levels according to their interrelated influence and subordinate relationship, thus forming a multi-level analytical structure model, which ultimately boils down to the determination of the relative importance weight of the lowest level (decision-making schemes, measures, etc.). ) arrangement relative to the highest level (overall goal) or relative priority.
When using AHP, if the selected elements are unreasonable, unclear, or the relationship between elements is incorrect, the quality of AHP results will be reduced, and even the decision of AHP will fail. In order to ensure the rationality of the hierarchical structure, we need to grasp the following principles:
1 When decomposing and simplifying the problem, grasp the main factors and don't miss too much;
Pay attention to the strong and weak relationship between the compared elements, and the elements that are too different cannot be compared at the same level.
Analytic Hierarchy Process is mainly used in the fields of safety science and environmental science. The main applications of safety production science and technology include coal mine safety research, hazardous chemicals evaluation, oil depot safety evaluation, urban disaster emergency response capability research and traffic safety evaluation. The application in environmental protection research mainly includes:
Determination of water safety evaluation, water quality index and environmental protection measures, ecological environment quality evaluation index system and pollution sources in aquatic wildlife reserves.
In addition, analytic hierarchy process can be used to guide and solve problems encountered in personal life, such as the choice of major, work and buying a house. Through the establishment of hierarchical structure and measurement indicators, we can clarify the working ideas and thinking levels.
For the convenience of description, according to the basic concepts of fuzzy mathematics, the related terms in fuzzy comprehensive evaluation method are defined as follows:
1. evaluation factor (f): refers to the specific contents of the evaluation of the project subject to tender (for example, price, various indicators, parameters, specifications, performance, status, etc.). ).
In order to facilitate weight distribution and evaluation, evaluation factors can be divided into several categories according to their attributes (for example, business, technology, price, accompanying services, etc.). ), and each category is regarded as a separate evaluation factor, which is called the first-level evaluation factor (F 1). The first-level evaluation factor can set the second-level evaluation factor at a lower level (for example, the first-level evaluation factor "business" can have the second-level evaluation factor at a lower level: delivery date, payment terms and payment methods, etc. ). The secondary evaluation factor can set the subordinate tertiary evaluation factor (F3). And so on.
2. Value of evaluation factor (Fv): refers to the specific value of evaluation factor. For example, if the technical parameter of a bidder is 120, then the evaluation factor value of the bidder is 120.
3. Evaluation value (e): refers to the degree of evaluation factors. The optimal evaluation value of the evaluation factor is 1 (100 when the percentage system is adopted); According to the degree of deterioration, the evaluation value of the deterioration factor is greater than or equal to zero and less than or equal to 1 (100 when the percentage system is adopted), that is, 0≤E≤ 1 (0≤E≤ 100 when the percentage system is adopted).
4. Average evaluation value (Ep): refers to the average evaluation value of a certain evaluation factor by members of the bid evaluation committee.
Average evaluation value (Ep)= sum of evaluation values of all members of the bid evaluation committee ÷ number of judges.
5. Weight (w): refers to the status and importance of evaluation factors.
The sum of the weights of the first-level evaluation factors is1; The sum of the weights of the lower evaluation factors of each evaluation factor is 1.
6. Weighted average evaluation value (Epw): refers to the weighted average evaluation value.
Weighted average evaluation value (Epw)= average evaluation value (Ep)× weight (w).
7. Comprehensive evaluation value (Ez): refers to the sum of weighted average evaluation values (Epw) of evaluation factors at the same level. The comprehensive evaluation value is also the corresponding superior evaluation.
References:
Baidu Encyclopedia-Fuzzy Comprehensive Evaluation Method
References:
Baidu encyclopedia-analytic hierarchy process