- Email: [email protected]
Closure to “Hybrid decision-making method for assessing interdependency and priority of critical infrastructure”
International Journal of Disaster Risk Reduction xxx (xxxx) xxx
Contents lists available at ScienceDirect
International Journal of Disaster Risk Reduction journal homepage: http://www.elsevier.com/locate/ijdrr
Closure to “Hybrid decision-making method for assessing interdependency and priority of critical infrastructure” A R T I C L E I N F O
A B S T R A C T
Keywords Critical infrastructure Decision-making Fuzzy and grey theories Empirical analysis
This paper constitutes the authors’ reply to the comments on the original article, “Hybrid decision-making method for assessing interdependency and priority of critical infrastructure” by Chou and Ongkowijoyo (2019). First, we respond regarding the issues of questionable critical infrastructures (CIs) and their definitions. Second, we clarify the influential network-relation map, which was presented within a comprehensive model flowchart in the original publication. Third, we confirm that the inclusion of fuzzy and grey theories in the conventional method provides more robust outputs rather than redundancy. Finally, we describe the rationality of input data, which influence the analytical process and its results, in terms of the characteristic relationship of CI network between each sector and sub-sector. We verify the reliability and accuracy of the FDANP model with reference to an empirical case and the results of the analysis in the original publication. The FDANP method thus developed is demonstrated to contribute to the theoretical framework of multiple decision-making environment and the relevant literature, as a superior practical technique.
1. Introduction The authors appreciate the discussers for their interest in our original paper entitled, “Hybrid decision-making method for assessing priority and interdependency of critical infrastructure” by Jui-Sheng Chou and Citra S. Ongkowijoyo. We would like to respond to the discussers’ comments and to provide some clarifications. 2. Case study of critical infrastructure in Taiwan Although the discussers noted that the case study of CI sectors, subsectors and their definitions is taken from Huang et al. (2014) [2], as stated in the original paper, the intention of the research of Chou and Ongkowijoyo (2019) [1] was to demonstrate the superior applicability of the developed analytical method by applying it to that case study to provide a fair comparison. The authors addressed this issue in the following statement, in the first sentence of Section 4 of the original publication [1]; “To validate and show the applicability of the FDANP, this section demonstrates its application to a case study of the CI system in Taiwan, taken from Huang et al. (2014).” 3. Obtaining a reliable influential network-relation map The discussers raised concerns about the influential network-relation map (INRM), which is one component of the original proposed FDANP framework, shown in Fig. 2 in Ref. [1], which is the same as Fig. 1 in Ref. [2]. The INRM was provided as a minor illustration, within the analytical flowchart of the FDANP method, of one of the advanced
FDANP processes. As shown in Fig. 2 in the original paper [1], the FDANP comprises eight significant procedures. One of the steps constitutes a numerical analysis that yields various influential network-relation maps. The network information that is obtained in the total influence matrix con cerns each criterion, which describes the effect of each criterion on the other criteria. Thus, a rather reliable INRM is obtained. 4. Accommodating divergent perceptions using fuzzy and grey theories The discussers mentioned that the conventional DANP “implies” a certain degree of vagueness and so suggested that the application of the fuzzy concept was redundant. The authors disagree with this assertion. In fact, as indicated by the analysis in the original publication [1], the application of the fuzzy and grey theories incorporates divergent expert perceptions in a group-decision making process. The authors demonstrated the effectiveness of the FDANP by applying it to the case study [1]. Although Huang et al. (2014) [2] used input data from multiple experts, they did not elaborate the handling of the multiple inputs but only applied a simple averaging method to process the data, lacking rigor. Therefore, fuzzy and grey theories are used to accommodate divergent judgments of multiple experts con cerning the degrees of importance of CI and to reduce the effects of subjectivity, decision vagueness, and imprecision. In the original paper, various judgments about the degrees of importance of CI, made by multiple experts were synthesized. A simu lation was carried out to demonstrate the effectiveness of the proposed
https://doi.org/10.1016/j.ijdrr.2020.101471 Received 2 January 2020; Accepted 2 January 2020 Available online 7 January 2020 2212-4209/© 2020 Elsevier Ltd. All rights reserved.
Please cite this article as: Jui-Sheng https://doi.org/10.1016/j.ijdrr.2020.101471
Chou,
Citra
S.
Ongkowijoyo,
International
Journal
of
Disaster
Risk
Reduction,
J.-S. Chou and C.S. Ongkowijoyo
International Journal of Disaster Risk Reduction xxx (xxxx) xxx
FDANP method with diverse expert opinions, which improves the pre cision of decision-making with a particular set of input data. Based on the results of the analysis in Table 4 [1], FDANP yielded very different INRMs and local weight values for each CI sector and sub-sector from those generated using the DANP method. Table 4 in Ref. [1] clearly shows that the FDANP generates more accurate results than the DANP by presenting significant changes in the rankings for both CI sectors and sub-sectors owing to its better synthesis capability than that of conventional methods. The change in ranking orders is dominated by the input data, which incorporate the diverse expert judgments. Treating the results of the analysis as empirical evi dence, the authors strongly believe that the application of both fuzzy and grey theories fills a knowledge gap and therefore constitutes a valuable contribution to the field.
Table C.1 Multiple expert judgments for CI sectors. Expert
Degree of importance of CI sectors
1st Expert
Sector C1 C2 C3 C4 Sector C1 C2 C3 C4 Sector C1 C2 C3 C4 Sector C1 C2 C3 C4 Sector C1 C2 C3 C4
2nd Expert
3rd Expert
5. Rationality of input data and derived results
4th Expert
As discussed above, the case study that was used in the original paper [1] is used as an example to demonstrate the applicability of the advanced model. The assumed expert judgments regarding the degrees of importance for both the CI sectors and CI sub-sectors were extensive and a full data set was used. However, the Journal limits the length of the paper and the authors worked within this restriction. The authors accept that the information provided regarding the case study in Ref. [2], which was cited, was insufficient to enable readers to apply the advanced FDANP model to a similar scenario. To clarify the result of the analysis, the assumed expert judgments as input data for CI sectors are provided in Table C.1. Although the other input data are extensive (the assumed expert judgments as inputs for CI sub-sectors), the authors would be happy to provide more information on the assumed input data set upon request. Analytically, the FDANP can handle more complex decision-making than conventional methods. The rationality of input data and derived results concerning both the influencing and affected CI sectors should be clarified. The main point of the analysis and discussion concerns the development of model by accomodating the diversity of expert percep tions that are used in the FDANP, rather than the output of the analysis. With the assumed input data and derived results in the case study that was taken from Ref. [2], a distinction must be drawn between the discrepancy of the analytical process and the accuracy of the solutions that are generated by the model.
5th Expert
C1 0 2 3 1 C1 0 3 3 3 C1 0 3 3 3 C1 0 4 4 4 C1 0 4 4 4
C2 3 0 3 3 C2 1 0 3 4 C2 1 0 3 4 C2 1 0 4 3 C2 4 0 1 3
C3 4 1 0 4 C3 3 3 0 3 C3 3 3 0 3 C3 4 4 0 3 C3 2 4 0 2
C4 4 2 4 0 C4 3 1 3 0 C4 3 1 3 0 C4 3 2 2 0 C4 3 4 1 0
decision-makers should be concerned about the accuracy of the results of FDANP analysis, which is affected by the divergence of expert perceptions. Importantly, the authors would like to emphasize that the contri bution of the original paper to the pertinent body of knowledge is development of the model, itself, in which the authors account for multiple expert judgments and the way to process them, affecting the interdependencies and the importance of CI as a priori on the accuracy of the structure of the model. Filling knowledge gaps of group decision-making processes by improving conventional techniques using advanced methods and proving the model adequacy by empirical analysis constitute continuous academic research and a particular contribution to the relevant body of knowledge. The original paper by Chou and Ongkowijoyo (2019) [1] improved upon, and contributed to, theoretical framework and litera ture, in its originality and in the provision of practical tools to support group decision-making processes.
6. Conclusion
Declaration of competing interest
This paper is a response to comments on the original article, “Hybrid decision-making method for assessing priority and interdependency of critical infrastructure” by Chou and Ongkowijoyo (2019) [1]. It dem onstrates the superior applicability of the developed analytical method by fairly comparing it with other methods using a case study in Ref. [2]. The paper clarifies the function of INRM, which was mentioned in the FDANP flowchart, by explaining that the map is used only for exemplary illustration to show one of the FDANP processes. The paper also considers the effectiveness of fuzzy and grey theories in accom modating divergent perceptions. With reference to the knowledge gaps in Ref. [2], the paper clarifies the aim of the original publication and provides the rationale for using both fuzzy and grey theories. The conventional DANP does not consider degrees of uncertainty, vagueness, and imprecision in a group decision-making environment. Using the advanced model thus developed and the empirical evidence obtained, the incorporation of fuzzy and grey theories into FDANP is proved to provide superior results to those of the conventional model. The paper addresses the rationality of input data and derived results by noting that the primary goal of the original publication was to demonstrate the applicability of FDANP and its superiority to the con ventional method. Accordingly, the outcome of the empirical applica tion in a particular case should not be overly generalized. Rather,
The authors declare that they have no known competing financial interests or personal relationships that could appear to have influenced this paper. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Acknowledgements The authors greatly appreciate the comments and questions of the commenters. References [1] J.-S. Chou, C.S. Ongkowijoyo, Hybrid decision-making method for assessing inter dependency and priority of critical infrastructure, Int. J. Disaster Risk Reduct. 39 (2019) 101134.
2
J.-S. Chou and C.S. Ongkowijoyo
International Journal of Disaster Risk Reduction xxx (xxxx) xxx
School of Architecture and Built Environment, Deakin University, Geelong, Victoria, Australia E-mail address: [email protected].
[2] C.-N. Huang, J.J.H. Liou, Y.-C. Chuang, A method for exploring the in terdependencies and importance of critical infrastructures, Knowl. Based Syst. 55 (2014) 66–74.
Jui-Sheng Chou* Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
* Corresponding author. E-mail address: [email protected] (J.-S. Chou).
Citra S. Ongkowijoyo
3
Contents lists available at ScienceDirect
International Journal of Disaster Risk Reduction journal homepage: http://www.elsevier.com/locate/ijdrr
Closure to “Hybrid decision-making method for assessing interdependency and priority of critical infrastructure” A R T I C L E I N F O
A B S T R A C T
Keywords Critical infrastructure Decision-making Fuzzy and grey theories Empirical analysis
This paper constitutes the authors’ reply to the comments on the original article, “Hybrid decision-making method for assessing interdependency and priority of critical infrastructure” by Chou and Ongkowijoyo (2019). First, we respond regarding the issues of questionable critical infrastructures (CIs) and their definitions. Second, we clarify the influential network-relation map, which was presented within a comprehensive model flowchart in the original publication. Third, we confirm that the inclusion of fuzzy and grey theories in the conventional method provides more robust outputs rather than redundancy. Finally, we describe the rationality of input data, which influence the analytical process and its results, in terms of the characteristic relationship of CI network between each sector and sub-sector. We verify the reliability and accuracy of the FDANP model with reference to an empirical case and the results of the analysis in the original publication. The FDANP method thus developed is demonstrated to contribute to the theoretical framework of multiple decision-making environment and the relevant literature, as a superior practical technique.
1. Introduction The authors appreciate the discussers for their interest in our original paper entitled, “Hybrid decision-making method for assessing priority and interdependency of critical infrastructure” by Jui-Sheng Chou and Citra S. Ongkowijoyo. We would like to respond to the discussers’ comments and to provide some clarifications. 2. Case study of critical infrastructure in Taiwan Although the discussers noted that the case study of CI sectors, subsectors and their definitions is taken from Huang et al. (2014) [2], as stated in the original paper, the intention of the research of Chou and Ongkowijoyo (2019) [1] was to demonstrate the superior applicability of the developed analytical method by applying it to that case study to provide a fair comparison. The authors addressed this issue in the following statement, in the first sentence of Section 4 of the original publication [1]; “To validate and show the applicability of the FDANP, this section demonstrates its application to a case study of the CI system in Taiwan, taken from Huang et al. (2014).” 3. Obtaining a reliable influential network-relation map The discussers raised concerns about the influential network-relation map (INRM), which is one component of the original proposed FDANP framework, shown in Fig. 2 in Ref. [1], which is the same as Fig. 1 in Ref. [2]. The INRM was provided as a minor illustration, within the analytical flowchart of the FDANP method, of one of the advanced
FDANP processes. As shown in Fig. 2 in the original paper [1], the FDANP comprises eight significant procedures. One of the steps constitutes a numerical analysis that yields various influential network-relation maps. The network information that is obtained in the total influence matrix con cerns each criterion, which describes the effect of each criterion on the other criteria. Thus, a rather reliable INRM is obtained. 4. Accommodating divergent perceptions using fuzzy and grey theories The discussers mentioned that the conventional DANP “implies” a certain degree of vagueness and so suggested that the application of the fuzzy concept was redundant. The authors disagree with this assertion. In fact, as indicated by the analysis in the original publication [1], the application of the fuzzy and grey theories incorporates divergent expert perceptions in a group-decision making process. The authors demonstrated the effectiveness of the FDANP by applying it to the case study [1]. Although Huang et al. (2014) [2] used input data from multiple experts, they did not elaborate the handling of the multiple inputs but only applied a simple averaging method to process the data, lacking rigor. Therefore, fuzzy and grey theories are used to accommodate divergent judgments of multiple experts con cerning the degrees of importance of CI and to reduce the effects of subjectivity, decision vagueness, and imprecision. In the original paper, various judgments about the degrees of importance of CI, made by multiple experts were synthesized. A simu lation was carried out to demonstrate the effectiveness of the proposed
https://doi.org/10.1016/j.ijdrr.2020.101471 Received 2 January 2020; Accepted 2 January 2020 Available online 7 January 2020 2212-4209/© 2020 Elsevier Ltd. All rights reserved.
Please cite this article as: Jui-Sheng https://doi.org/10.1016/j.ijdrr.2020.101471
Chou,
Citra
S.
Ongkowijoyo,
International
Journal
of
Disaster
Risk
Reduction,
J.-S. Chou and C.S. Ongkowijoyo
International Journal of Disaster Risk Reduction xxx (xxxx) xxx
FDANP method with diverse expert opinions, which improves the pre cision of decision-making with a particular set of input data. Based on the results of the analysis in Table 4 [1], FDANP yielded very different INRMs and local weight values for each CI sector and sub-sector from those generated using the DANP method. Table 4 in Ref. [1] clearly shows that the FDANP generates more accurate results than the DANP by presenting significant changes in the rankings for both CI sectors and sub-sectors owing to its better synthesis capability than that of conventional methods. The change in ranking orders is dominated by the input data, which incorporate the diverse expert judgments. Treating the results of the analysis as empirical evi dence, the authors strongly believe that the application of both fuzzy and grey theories fills a knowledge gap and therefore constitutes a valuable contribution to the field.
Table C.1 Multiple expert judgments for CI sectors. Expert
Degree of importance of CI sectors
1st Expert
Sector C1 C2 C3 C4 Sector C1 C2 C3 C4 Sector C1 C2 C3 C4 Sector C1 C2 C3 C4 Sector C1 C2 C3 C4
2nd Expert
3rd Expert
5. Rationality of input data and derived results
4th Expert
As discussed above, the case study that was used in the original paper [1] is used as an example to demonstrate the applicability of the advanced model. The assumed expert judgments regarding the degrees of importance for both the CI sectors and CI sub-sectors were extensive and a full data set was used. However, the Journal limits the length of the paper and the authors worked within this restriction. The authors accept that the information provided regarding the case study in Ref. [2], which was cited, was insufficient to enable readers to apply the advanced FDANP model to a similar scenario. To clarify the result of the analysis, the assumed expert judgments as input data for CI sectors are provided in Table C.1. Although the other input data are extensive (the assumed expert judgments as inputs for CI sub-sectors), the authors would be happy to provide more information on the assumed input data set upon request. Analytically, the FDANP can handle more complex decision-making than conventional methods. The rationality of input data and derived results concerning both the influencing and affected CI sectors should be clarified. The main point of the analysis and discussion concerns the development of model by accomodating the diversity of expert percep tions that are used in the FDANP, rather than the output of the analysis. With the assumed input data and derived results in the case study that was taken from Ref. [2], a distinction must be drawn between the discrepancy of the analytical process and the accuracy of the solutions that are generated by the model.
5th Expert
C1 0 2 3 1 C1 0 3 3 3 C1 0 3 3 3 C1 0 4 4 4 C1 0 4 4 4
C2 3 0 3 3 C2 1 0 3 4 C2 1 0 3 4 C2 1 0 4 3 C2 4 0 1 3
C3 4 1 0 4 C3 3 3 0 3 C3 3 3 0 3 C3 4 4 0 3 C3 2 4 0 2
C4 4 2 4 0 C4 3 1 3 0 C4 3 1 3 0 C4 3 2 2 0 C4 3 4 1 0
decision-makers should be concerned about the accuracy of the results of FDANP analysis, which is affected by the divergence of expert perceptions. Importantly, the authors would like to emphasize that the contri bution of the original paper to the pertinent body of knowledge is development of the model, itself, in which the authors account for multiple expert judgments and the way to process them, affecting the interdependencies and the importance of CI as a priori on the accuracy of the structure of the model. Filling knowledge gaps of group decision-making processes by improving conventional techniques using advanced methods and proving the model adequacy by empirical analysis constitute continuous academic research and a particular contribution to the relevant body of knowledge. The original paper by Chou and Ongkowijoyo (2019) [1] improved upon, and contributed to, theoretical framework and litera ture, in its originality and in the provision of practical tools to support group decision-making processes.
6. Conclusion
Declaration of competing interest
This paper is a response to comments on the original article, “Hybrid decision-making method for assessing priority and interdependency of critical infrastructure” by Chou and Ongkowijoyo (2019) [1]. It dem onstrates the superior applicability of the developed analytical method by fairly comparing it with other methods using a case study in Ref. [2]. The paper clarifies the function of INRM, which was mentioned in the FDANP flowchart, by explaining that the map is used only for exemplary illustration to show one of the FDANP processes. The paper also considers the effectiveness of fuzzy and grey theories in accom modating divergent perceptions. With reference to the knowledge gaps in Ref. [2], the paper clarifies the aim of the original publication and provides the rationale for using both fuzzy and grey theories. The conventional DANP does not consider degrees of uncertainty, vagueness, and imprecision in a group decision-making environment. Using the advanced model thus developed and the empirical evidence obtained, the incorporation of fuzzy and grey theories into FDANP is proved to provide superior results to those of the conventional model. The paper addresses the rationality of input data and derived results by noting that the primary goal of the original publication was to demonstrate the applicability of FDANP and its superiority to the con ventional method. Accordingly, the outcome of the empirical applica tion in a particular case should not be overly generalized. Rather,
The authors declare that they have no known competing financial interests or personal relationships that could appear to have influenced this paper. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Acknowledgements The authors greatly appreciate the comments and questions of the commenters. References [1] J.-S. Chou, C.S. Ongkowijoyo, Hybrid decision-making method for assessing inter dependency and priority of critical infrastructure, Int. J. Disaster Risk Reduct. 39 (2019) 101134.
2
J.-S. Chou and C.S. Ongkowijoyo
International Journal of Disaster Risk Reduction xxx (xxxx) xxx
School of Architecture and Built Environment, Deakin University, Geelong, Victoria, Australia E-mail address: [email protected].
[2] C.-N. Huang, J.J.H. Liou, Y.-C. Chuang, A method for exploring the in terdependencies and importance of critical infrastructures, Knowl. Based Syst. 55 (2014) 66–74.
Jui-Sheng Chou* Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
* Corresponding author. E-mail address: [email protected] (J.-S. Chou).
Citra S. Ongkowijoyo
3