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Research on Evaluation Method Based on Modified Buckley Decision Making and Bayesian Network
This work presents a novel evaluation method, which can be applied in the field of risk assessment, project management, cause analysis, and so forth. Two core technologies are used in the method, namely, modified Buckley Decision Making and Bayesian Network. Based on the modified Buckley Decision Making, the fuzzy probabilities of element factors are calibrated. By the forward and backward calculation of Bayesian Network, the structure importance, probability importance, and criticality importance of each factor are calculated and discussed. A numerical example of risk evaluation for dangerous goods transport process is given to verify the method. The results indicate that the method can efficiently identify the weakest element factor. In addition, the method can improve the reliability and objectivity for evaluation.
The applications of evaluation models almost extend to every aspect of research, for example, forecasting traffic flows , analyzing the causes of traffic accidents , evaluating the risk of transport process , and discussing the origins of driver fatigue . In these topics, two targets attract researchers’ interest: investigating the top-event occurrence rate and exploring the influence degree of element factors. Analytic hierarchy process (AHP), fuzzy analytic hierarchy process (FAHP), fishbone diagram model (FDM), fault tree analysis (FTA), and Bayesian Network (BN) have proved to be efficient approaches for studying them [5–9]. In these models, the probabilities of element factors are the foundation of quantitative analysis. They are calibrated in the traditional approach by the following three steps. (1) Construct judgment matrix by introducing 1~9 contrast scaling (K). (2) Normalize processing of the matrix to calculate the eigenvector. (3) Assign each element factor a value of element in eigenvector as its probability value correspondingly. However, in the first step, it has natural deficiency by using 1~9 contrast scaling to construct the judgment matrix. To illustrate this point, an intensity of importance scale is given in Table 1.
Suppose,, andare element factors. According to Table 1, ifis slightly more important than, then. Moreover, if(in the view of scale table, the weight ratio of element factors “” and “” is), then the importance of factor “” compared with “” belongs to the level of “strong importance.” At the same time,is equal tomathematically. In other words, the weight ratio of factors “” and “” is, so factor “” is a little more important than “.” Therefore, it is conflicting between the above two standpoints. Furthermore, if the relationship between factors “” and “” applies to nexus between “” and “,” then the weight ratio of factors “” and “” is. Although factor “” is 9 times of “” in weight ratio, it is not definite that factor “” is extremely more important than “.” Therefore, it inevitably causes the confusion when using the traditional approach. In addition, the judgment matrix constructed by the traditional approach is a deterministic matrix, which cannot well represent the fuzzy comparisons between element factors ideally.
Aiming at the defects of traditional method, the 9/9~9/1 contrast scaling will be introduced into the Buckley matrix; in addition, K-value will be replaced by trapezoidal fuzzy number [3, 10]. Then the modified Buckley Decision Making method will be applied to calibrate the probabilities of element factors. Using this technique, the calculation becomes more rigorous and makes the importance degree much more scientific despite of its complexity.
In the field of reasoning model, BN can be mapped easily from the AHP, FAHP, FDM, and FTA models. Owing to the conditional probability tables of BN, the diagram calculation process of AHP, FAHP, FDM, and FTA can be transformed into a logical table calculation process, which helps to calculate intelligently. Based on the chain rule in BN, the evaluation value of top-event as well as the structure (probability, criticality) importance of each factor will be obtained by the forward and backward calculation of BN. The influence degree of each element factor can be effectively indicated by the previous three importance indexes.
The remaining parts of this paper are organized as follows. Section 2 introduces the new evaluation method. In the method, modified Buckley Decision Making method is applied to calibrate the probability of each element factor; BN is served as the reasoning computation model; structure importance, probability importance and criticality importance are designed as the evaluation indicators; Bucket Elimination algorithm is modified to solve the BN. In Section 3, a numerical example is given to verify the method. Conclusions are finally drawn in Section 4, along with recommendations for future research.
2. The New Evaluation Method
2.1. Modified Buckley Decision Making
The probabilities of element factors are fundamental to the evaluation process, which rely on judgment matrix. Based on the Buckley Decision Making method, the trapezoidal fuzzy number is introduced to construct the judgment matrix. If element factor is extremely more important than , then the initialized fuzzy judgment matrix can be constructed asThen, the formulais applied to modify each element in the upper right corner of the matrix . The elements in the lower left corner of the matrix are derived by construction rules of Buckley matrix . Therefore, the modified fuzzy judgment matrix is
Similarly, based on the modified Buckley Decision Making method, the matrix contained contrast between all the element factors which is defined aswhere, , and.
Then the fuzzy weightof element factoris calculated: Performing the same procedure forfrom 1 to n, we have
Then, the value of element inis assigned to each element factor as its fuzzy probability value correspondingly.
2.2. Bayesian Network
Bayesian Network is a directed acyclic graph with a series of conditional probability tables (CPTs) [3, 12, 13]. It has become widely used in the field of evaluation because of the conditional independence of BN nodes and bidirectional reasoning mechanism. In addition, AHP, FAHP, FDM, and FTA models can be mapped to a BN easily, which helps to establish BN models. Roughly speaking, in most of AHP, FAHP, FDM, and FTA models, the relationship between subnodes and parent nodes only involves “OR gate” and “AND gate” (the logical relationships between events and causes in AHP, FAHP, and FDM models are represented by means of logical “AND” and “OR” gates). Therefore, it is critical to discuss how an “OR gate” and an “AND gate” convert into an equivalent BN. The conversions are shown in Figures 1 and 2.
According to Figure 1, we have
According to Figure 2, we have
Based on the conversions, each gate (OR or AND) in the AHP, FAHP, FDM, and FTA assigns the equivalent CPT to the corresponding node in the BN and then the whole BN is established . Taking the conditional independence of BN nodes into consideration, we get the joint probability distribution function using chain rule, which is the foundation of forward and backward calculation.
2.3. Evaluation Indicators
In the evaluation method, three indicators, that is, structure importance, probability importance and criticality importance, are designed to evaluate the importance of each factor.analyzes the influence of each factor with respect to model structure.analyzes the influence of each factor with respect to probability of each factor and model structure.analyzes the influence of each factor with respect to sensitivity and probability, which can reflect the fact that reducing the occurrence probability of a large probability event is easier than a rare event . The calculation formulas of the above three indicators are defined by (9), where the parameters are shown in Table 2:
2.4. Evaluation Methodology
Among various techniques for solving BN, Bucket Elimination has proved to be one of the most efficient approaches . Considering the dimorphism of BN mapped from AHP, FAHP, FDM, and FTA model, the Bucket Elimination algorithm can be modified to reduce the computational difficulty and to improve the efficiency of calculation. Therefore, we define calculation rules of the modified Bucket Elimination algorithm firstly; then, based on the rules and modified Buckley Decision Making, put forward the evaluation process.
Rule 1. To deal with “singer-factor,” if we have , then
Rule 2. To deal with “AND gate,” if we have, then
Rule 3. To deal with “OR gate,” if we have , then
(2) Evaluation Process. The calculation process of the new evaluation method is shown in Figure 3. According to the evaluation process, the evaluation indicators are derived, which indicate the importance of each element factor. Based on the results, we can efficiently identify the weakest element factor.
3. Numerical Example
Figure 4 shows an FTA model of risk evaluation for dangerous goods transport process, whererepresents an element factor. The corresponding importance of each factoris given by experts. Based on the modified Buckley Decision Making method, the fuzzy probabilities of all element factors are calibrated (see Table 4).
As illustrated in Figure 5, the joint probability distribution function is derived in
According to Figure 6, the structure importance of each element factor is sorted to be
According to Figure 7, the probability importance of each element factor is sorted to be
According to Figure 8, the criticality importance of each element factor is sorted to be
As shown in (14), most structure importance of element factors is equal. Therefore, it is not proper to indicate the influence extent of different factors using only structure importance. By a comparison of (14), (15), and (16), we find thatandare relatively large whileis relatively small. It indicates thathas great effects on, but it is difficult to reduce the influence ofonby taking measures. In addition, and. It can be concluded that and have larger effects onthan the other factors, and it can ameliorate the value ofefficiently by reducing the occurrence probabilities ofand. Therefore, in the actual productions, it is significantly important to monitor the procedures ofandto ensure safety.
Based on the combination of modified Buckley Decision Making and Bayesian Network, we present a new evaluation method in this paper, which can be widely used in the field of risk assessment, project management, cause analysis, and so forth. By using modifier formula, 9/9~9/1 contrast scaling is successfully introduced into the Buckley matrix. In addition, the elements in Buckley matrix are trapezoidal fuzzy number, which can well represent the fuzzy comparisons between element factors ideally. According to the modified Buckley Decision Making method, we can calibrate the probability of each element factor more logically. As has been said, the probabilities of factors are fundamental to evaluation. Therefore, the application of this core technology makes our evaluation method more reliable and objective. Based on the bidirectional reasoning mechanism of BN, the top-event occurrence rate (or evaluation value) and influence indexes of element factors are gained by the forward and backward calculation. Then, according to the sequence of these influence indexes, the impact of each element factor can be represented. Therefore, we can identify the weakest element factor efficiently. In addition, the application of CPTs in BN can make the diagram calculation of AHP, FAHP, FDM, and FTA into a logical table calculation, which is beneficial to the intelligence of calculating techniques.
There are many interesting directions in which we can extend our work. Ongoing and future research that we are pursuing are to construct a discrete time dynamic Bayesian Network model, combining the modified Buckley Decision Making method.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research was supported by China Railways Technology R&D Program (no. 2014X002-C).
- C. M. Queen and C. J. Albers, “Intervention and causality: forecasting traffic flows using a dynamic Bayesian network,” Journal of the American Statistical Association, vol. 104, no. 486, pp. 669–681, 2009.
- L. Najib and L. Abdullah, “A Lambda-Max of consistency test in Fuzzy Analytic Hierarchy Process (FAHP) for weights of road accidents causes,” in Proceedings of the 20th National Symposium on Mathematical Sciences: Research in Mathematical Sciences: A Catalyst for Creativity and Innovation (SKSM '12), pp. 426–434, AIP Publishing, Washington, DC, USA, December 2012.
- N.-P. Yang, Y.-F. Yang, and W. Feng, “Risk assessment of railway dangerous goods transport process based on fuzzy Bayesian Network,” Journal of the China Railway Society, vol. 36, no. 7, pp. 8–15, 2014.
- S. Murphy and D. Z. Leach, “The extent to which heavy goods vehicle driver training is focused on reducing the casual factors of driver stress and fatigue,” in Proceedings of the LRA Annual Conference and PHD Workshop, Birmingham , UK, September 2013.
- S.-S. Tang and X.-P. Li, “Study on method for assessment of vulnerability of railway emergency rescue system,” Journal of the China Railway Society, vol. 35, no. 7, pp. 14–20, 2013.
- A. R. Singh, P. K. Mishra, R. Jain, and M. K. Khurana, “Robust strategies for mitigating operational and disruption risks: a fuzzy AHP approach,” International Journal of Multicriteria Decision Making, vol. 2, no. 1, pp. 1–28, 2012.
- N. Distefano and S. Leonardi, “Risk assessment procedure for civil airport,” International Journal for Traffic and Transport Engineering, vol. 4, no. 1, pp. 62–75, 2014.
- L. L. Tupper, M. Chowdhury, and J. Sharp, “Tort liability risk prioritization through the use of fault tree analysis,” in Proceedings of Transportation Research Board 93rd Annual Meeting, no. 14-5153, Washington, DC, USA, 2014.
- J. C. McCall and M. M. Trivedi, “Driver behavior and situation aware brake assistance for intelligent vehicles,” Proceedings of the IEEE, vol. 95, no. 2, pp. 374–387, 2007.
- J. J. Buckley, “Fuzzy hierarchical analysis,” Fuzzy Sets and Systems, vol. 17, no. 3, pp. 233–247, 1985.
- L. Ying, “Fuzzy comprehensive evaluation on safety of railway dangerous goods transport,” Railway Freight Transport, vol. 29, no. 10, pp. 32–37, 2011.
- N. Friedman, D. Geiger, and M. Goldszmidt, “Bayesian network classifiers,” Machine Learning, vol. 29, no. 2-3, pp. 131–163, 1997.
- M. E. Borsuk, C. A. Stow, and K. H. Reckhow, “A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis,” Ecological Modelling, vol. 173, no. 2-3, pp. 219–239, 2004.
- A. Bobbio, L. Portinale, M. Minichino, and E. Ciancamerla, “Improving the analysis of dependable systems by mapping fault trees into Bayesian networks,” Reliability Engineering and System Safety, vol. 71, no. 3, pp. 249–260, 2001.
- R. Dechter, “Bucket elimination: a unifying framework for reasoning,” Artificial Intelligence, vol. 113, no. 1-2, pp. 41–85, 1999.
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