- Email: [email protected]
Prioritizing management issues of moving dangerous goods by air transport
ARTICLE IN PRESS
Journal of Air Transport Management 12 (2006) 191–196 www.elsevier.com/locate/jairtraman
Prioritizing management issues of moving dangerous goods by air transport Yu-Hern Changa, Chung-Hsing Yehb,, Yi-Lin Liua a
Department of Transportation and Communications Management Science, National Cheng Kung University, Tainan, 701 Taiwan, ROC b School of Business Systems, Monash University, Clayton, Victoria 3800, Australia
Abstract This paper presents an expert survey-based approach for prioritizing management issues of dangerous goods transportation faced by air-transport-related sectors in Taiwan. A two-stage survey process is used to ask the experts to first identify and then evaluate the management issues. To develop effective-management priorities, these issues are evaluated in terms of their importance, urgency, achievability, and effectiveness, based on experts’ comparative and absolute judgments. A pairwise comparison process is used to help the experts make comparative judgments, while a linguistic rating method is used for absolute judgments. To reflect the inherent imprecision involved in the survey process, experts’ assessments are represented by triangular fuzzy numbers. To obtain an overall priority value, a fuzzy multiattribute decision-making method is used. r 2006 Elsevier Ltd. All rights reserved. Keywords: Dangerous goods; Multiattribute decision-making; Fuzzy numbers
1. Introduction The increasingly integrated world economy and dynamic freight market have increased the demand to transport potentially dangerous goods by air (International Air Transport Association, 2005). To ensure safety and security standards for the air transport of dangerous goods, the International Civil Aviation Organization (ICAO) (2001) has promulgated its Technical Instructions for the Safe Transport of Dangerous Goods by Air as legal requirements for its member states. Aviation safety and security has always been the stated priority of Taiwan’s civil aviation authority, the Civil Aeronautics Administration (CAA). With its Category 1 rating in complying with ICAO safety standards (Button et al., 2004), the CAA has continued to enhance its safetyrelated programs including a comprehensive safety oversight assessment program for meeting its international obligations (Chang and Yeh, 2004). In dealing with the shipment of dangerous goods, the CAA set up an advisory committee on Corresponding author. Tel.: +61 3 9905 5808; fax: +61 3 9905 5159.
E-mail address: [email protected] (C.-H. Yeh). 0969-6997/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2006.01.007
dangerous goods transportation in 2001. Subsequently, Taiwan’s National Freight Transportation Policy stated the need to specifically establish an administrative structures for managing the transport of dangerous goods (Chang, 2002). The CAA consequently set up a safety and security unit in its Air Transport Division in 2003 and published the dangerous goods inspector handbook for use in conjunction with ICAO and IATA documents. With Taiwan aiming at conforming to international requirements, it is important to identify and address safetyrelated management issues at all points in the transportation chain to effectively meet international safety oversight requirements, as set out in ICAO’s Strategic Action Plan (Abeyratne, 1998). With scarce resources available (Motevalli and Stough, 2004), it is of strategic importance for the government to evaluate identified management issues so as to set up effective-management priorities for action.
2. Management of dangerous goods air movements in Taiwan To identify the management issues inherent in the transportation of dangerous goods, 31 middle level or
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above managers were selected with at least 5 years of relevant experience in the field. They also had to be personally willing to participate in a two-stage survey process, that inevitably introduced a degree of unavoidable bias. The first stage aims at identifying the management issues, and the second at evaluating the identified management issues. The experts included 6 government aviation officials (including 4 from the CAA and 2 from the airport and aviation police sector), 8 academic researchers from different institutions, and 17 practitioners (including 8 from 3 airline operators, 6 from 3 freight forwarders, and 3 from 3 shippers’ associations). After initial contacts, fieldwork and individual interviews were conducted between September and November 2004. The deficiencies and shortcomings of the security systems that they identified were combined into 20 management issues. To ensure the experts’ consensus on these issues, we conducted the first stage survey between January and February 2005 by asking the experts their opinion about the inclusion of these issues in the study. As a result, three issues were excluded because they could not be addressed under current practice (such as low inspection rates) or should be combined with other issues (such as lack of storage regulations and no penalty rules for incompliance with regulatory procedures). The other 17 issues were supported by at least 90% of the experts. Table 1 shows these 17 issues grouped into 6 management sectors (functional areas), each associated with the responsibility area of a corresponding government organization or industry sector. To identify key deficiencies in transport of dangerous goods by air, the 17 management issues identified are
evaluated in terms of their importance, urgency, achievability, and effectiveness. The importance criterion is used to reflect the significance and contribution of the issue to the safe transport of dangerous goods if fully addressed, as compared to other issues. The urgency criterion is used to indicate if and to what degree the nature of the issue demands urgent attention. The achievability criterion is concerned with the degree to which issues can be easily addressed within the current political, economic and technical settings. The effectiveness criterion is used to indicate the degree to which the deficiency or shortcoming can be rectified or improved if appropriate actions are taken. The four evaluation criteria are independent of each other. As such, the evaluation of the 17 issues can be formulated as a multiattribute decision-making (MADM) problem, from which a cardinal priority value can be generated. MADM provides a formal framework for modeling multiattribute decision problems, in particular when the nature of the problem demands a systematical analysis, such as the complexity of the decision, the regularity of the decision, the significant consequences, and the need for accountability (Belton and Stewart, 2002). Despite their diversity, MADM problems share several common characteristics: a finite number of comparable alternatives (management issues), multiple attributes (evaluation criteria) for comparison among alternatives, noncommensurable units for measuring performance rating of alternatives on each attribute, attribute weights for representing the relative importance of each attribute. To give each alternative an overall preference value as an indication of the decision maker’s preference for the
Table 1 Management issues of dangerous goods in Taiwan’s air transport sectors Management sector (responsible organization)
Management issue (deficiency or shortcoming)
A1
Policies and regulations (Ministry of Transportation and Communications)
A11 A12 A13
Insufficient laws and regulations Difficulties in identification of the responsible parties Lack of Chinese version of up-to-date IATA regulatory documentation
A2
Security oversight audit (Civil Aeronautics Administration)
A21 A22 A23
Inadequate training programs Insufficient qualified inspectors Lack of designated certification agents
A3
Loading and stowage (airline operators)
A31 A32
Unfamiliarity of regulatory procedures Insufficiency in detection of hidden or undeclared dangerous goods Insufficient regulatory materials and technical instructions
A33 A4
Freight handling (freight forwarders)
A41 A42 A43
Insufficient packaging Inadequate labeling and marking of dangerous goods information Unawareness of carriers, forwarders and handling agents
A5
Surveillance and inspection (Aviation Police Bureau)
A51 A52 A53
Insufficient or inadequate equipment Lack of qualified and experienced inspection personnel Unawareness of untrained customs personnel
A6
Shipper awareness (shippers)
A61 A62
Shippers’ incompliance with regulatory security procedures Shippers’ insufficient knowledge of dangerous goods
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alterative, the performance ratings of alternatives are to be aggregated with the attribute weights by using MADM. The resultant overall preference values provide a cardinal priority ranking of the alternatives. As a newly formulated evaluation problem in a new decision setting, the assessment data required for the MADM evaluation problem requires expert surveys. As such, we conducted the second stage survey between March and April 2005 asking the same experts as before to assess the weight of the four evaluation criteria, the relative importance of 6 management sectors and of the associated issues within each sector, and the achievability, urgency, and effectiveness of the 17 issues. The first two survey items require the experts’ comparative judgment, while the third survey item needs the experts to use their absolute judgment. Thirty-one questionnaire forms were distributed and 4 government aviation officials, 7 academic researchers, and 17 air cargo practitioners replied. 3. Criteria weights and relative importance of management issues by comparative judgment To make comparative judgment on the relative importance of the criteria, each is compared with all other criteria. As there are limitations to the amount of information that humans can effectively handle, a pairwise comparison approach used to help the experts make comparative judgment. The concept of pairwise comparisons has been known since the work of Thurstone (1927) and has been implemented in the analytic hierarchy process (AHP) of Saaty (1980). In the AHP, a 1–9 ratio scale is used to compare two alternatives (criteria or issues) for indicating the strength of their relative preference. Applying this procedure to all m alternatives results in a positive m m reciprocal matrix with all its elements xij ¼ 1=xji (i ¼ 1; 2; . . . ; m; j ¼ 1; 2; . . . ; m). Since the issues identified and the evaluation criteria may be vaguely defined, the survey assessment is conducted in an intrinsically imprecise manner. To reflect the imprecision involved in this process, the ratio value given by the experts is represented by a corresponding triangular fuzzy number (a1 , a2 , a3 ), where a2 is the most possible value, and a1 and a3 are the lower and upper bounds, respectively used to reflect the fuzziness of the survey assessment. With the use of triangular fuzzy numbers, the arithmetic operations on fuzzy numbers are based on interval arithmetic (Kaufmann and Gupta, 1991). Table 2 illustrates how a triangular fuzzy number is generated to represent the imprecise assessment given by an
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expert using a numeric ratio value. If the ratio value given is 5 (‘‘strongly more important’’), the fuzzy assessment represented as a triangular fuzzy number is (3, 5, 7). This implies that the assessment is ‘‘about 5’’ for reflecting the vagueness of the subjective judgment. If the ratio value given is 9, the fuzzy assessment is (7, 9, 9). This fuzzy representation of a crisp value facilitates the experts’ subjective assessment in the survey, as they require no knowledge of fuzzy numbers for making fuzzy (imprecise) assessment. The experts give the most possible ratio value for each comparison of two alternatives that matches the corresponding qualitative assessment term, shown as in the first row of Table 2. This fuzzy representation is similar in essence to the use of a set of linguistic terms characterized by given triangular fuzzy numbers, but it provides the experts with more options (9 possible fuzzy numbers instead of 5 for the 5 terms given in the table) for their qualitative judgments. In solving a fuzzy positive reciprocal matrix resulting from pairwise comparisons using fuzzy ratios, Buckley (1985) uses the geometric mean method to calculate the fuzzy relative importance values for all the alternatives. This method possesses a number of properties and can be easily applied to situations where multiple experts are involved in the assessment process. Given a fuzzy positive reciprocal matrix R ¼ ½xij (i ¼ 1; 2; . . . ; m; j ¼ 1; 2; . . . ; m), the method first calculates the geometric mean of each row as !1=m m Y ri ¼ xij . (1) j¼1
The fuzzy relative importance values wi for m alternatives Ai (i ¼ 1; 2; . . . ; m) are then computed as , m X wi ¼ ri rj . (2) j¼1
The pairwise comparison process with fuzzy ratios has been applied to help the experts assess the weight of the four evaluation criteria. Table 3 shows the fuzzy weights of the four evaluation criteria. Criteria weights are usually normalized to sum to unity, to allow the weight value to be interpreted as the percentage of the total importance weight (Belton and Stewart, 2002). It would be tedious for the experts to apply the above pairwise comparison process directly to all the issues. To facilitate the comparison process, we develop a hierarchical approach, where the issues are grouped and compared within
Table 2 Ratio value fuzzification of pairwise comparisons Equally important
Moderately more important
Strongly more important
Very strongly more important
Extremely more important
1
2
4
6
8
3 a1
5 a2
7 a3
9
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where r1 , r2 , y, rk are the performance ratings of the issue given by experts k (k ¼ 1; 2; . . . ; q), respectively.
Table 3 Fuzzy weights of evaluation criteria Criteria
Fuzzy weight
Importance Urgency Achievability Effectiveness
(0.22, (0.14, (0.13, (0.14,
0.35, 0.21, 0.21, 0.23,
5. Prioritizing management issues with fuzzy survey assessments
0.55) 0.34) 0.34) 0.36)
their corresponding management sector (see Table 1 again). Instead of comparing between all the 17 issues, the pairwise comparison process is conducted across six management sectors, and between issues within each management sector. As such, only 31 ( ¼ 15+3+3+3+3+3+1) comparisons are required. It is noteworthy that when comparing the management sectors in pairs, their associated issues are presented and explained to the experts. To obtain the fuzzy relative importance values of all the issues across six management sectors, the fuzzy relative importance values of the issues under each management sector are normalized by taking the relative importance value of the corresponding sector as their mean value. Taking the fuzzy relative importance value of a management sector Ai (i ¼ 1; 2; . . . ; 6) is vAi and the fuzzy relative importance values of its Ni associated issues (Ai1, Ai2, y, AiN i ) within the sector Ai are viAih (h ¼ 1; 2; . . . ; N i ), then the fuzzy relative importance values of these Ni issues among all issues are , ! Ni X i i vAih . vAih ¼ vAi vAih N i (3) h¼1
With the fuzzy weights of the four criteria and the fuzzy performance ratings of the issues obtained, an MADM method can be used to normalize and aggregated to obtain an overall priority value for each issue. Research in MADM suggests the use of simple and understandable approaches for solving practical MADM problems. The simple additive weighting (SAW) method, also known as the weighted sum method, is probably the best-known method (Hwang and Yoon, 1981). The use of an additive value function in SAW can be intuitively appealing to the decision-maker in practical applications, and can be justified theoretically and empirically (Yeh, 2003). The basic logic of the SAW method is to obtain a weighted sum of the performance ratings of each alternative over all criteria. If the performance ratings are assessed based on different scales, a normalization process is required to transform all the ratings of different units to a comparable scale, so that the inter-criteria comparisons can be made: ,sffiffiffiffiffiffiffiffiffiffiffiffiffi m X yij ¼ xij x2ij ; i ¼ 1; 2; . . . ; m; j ¼ 1; 2; . . . ; n, (5) i¼1
where xij are the performance ratings of alternatives Ai (i ¼ 1; 2; . . . ; m) with respect to criteria Cj (j ¼ 1; 2; . . . ; n). With the SAW method, the overall preference or priority value (Pi) of alternatives Ai (i ¼ 1; 2; . . . ; m) is obtained as Pi ¼
4. Assessment of urgency, achievability, and effectiveness of management issues A five-point Likert-type scale is used for rating the 17 issues with respect to their urgency, achievability, and effectiveness. The rating values in the scale are derived by using five linguistic terms {Very Low, Low, Medium, High, Very High}, that are associated with a corresponding set of numbers {1, 2, 3, 4, 5}. The experts’ assessment on the performance rating of newly established issues against vaguely defined qualitative evaluation criteria is intrinsically imprecise. As such, the assessment results given by all experts are aggregated and represented as a triangular fuzzy number. The fuzzy number (a1 , a2 , a3 ) for representing the fuzzy rating of an issue on a criterion assessed by all q experts is given as a1 ¼ minfr1 ; r2 ; . . . ; rk g; !1=q q Y a2 ¼ rk ,
k ¼ 1; 2; . . . ; q,
k¼1
a3 ¼ maxfr1 ; r2 ; . . . ; rk g;
k ¼ 1; 2; . . . ; q,
ð4Þ
n X
wj yij ;
i ¼ 1; 2; . . . ; m,
(6)
j¼1
where wj are the weights of criteria Cj (j ¼ 1; 2; . . . ; n) and yij are the normalized performance ratings of alternatives Ai (i ¼ 1; 2; . . . ; m) with respect to criteria Cj (j ¼ 1; 2; . . . ; n). The greater the value (Pi), the higher priority the alternative (Ai). To compare the overall fuzzy priority value of the various issues, we use the a-cut in fuzzy set theory (Klir and Yuan, 1995). The a-cut of a fuzzy set is the ordinary set that contains all the values with a membership degree of at least a (where 0pap1). By using a a-cut on a triangular fuzzy number, a value interval [xal , xar ] is derived. The value of a can be used to represent the decision maker’s degree of confidence in the fuzzy assessments made by the experts. A larger a-value indicates that the decision maker is more confident, as the interval is smaller and has a higher possibility. To reflect the decision maker’s relative preference between xal and xar , an attitude index l in the range of 0 and 1 can be incorporated. As a result, a crisp value can be obtained as xla ¼ lxar þ ð1 lÞxal ; 0plp1.
(7)
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The value of l can be used to reflect the decision maker’s attitude towards risk (Yeh and Kuo, 2003). In actual decision settings, l ¼ 1, 0.5, or 0 can be used to indicate that the decision maker has an optimistic, moderate, or pessimistic view respectively on fuzzy assessment results. Table 4 shows the normalized performance ratings of the 17 issues and their corresponding ranking order with respect to each evaluation criterion, with a ¼ 0 and l ¼ 0:5. With the data in Tables 3 and 4, an overall fuzzy priority value for each issue can be obtained by applying Eq. (6). Column 3 of Table 5 shows the result. To rank the issues, we apply Eq. (7) with a ¼ 0 and l ¼ 0:5. Column 4 of Table 5 shows the result and the corresponding ranking order. As each of these issues is associated with a management sector, we can obtain the priority value for each sector by averaging the priority values of its associated issues. Column 5 of Table 5 shows the result and the corresponding ranking order. The priority-ranking order of the 6 sectors remains the same if we aggregate the priority values within each sector instead of averaging. The settings used for a and l reflect no particular preference for the fuzzy assessments made by the experts. Further, a ¼ 0 implies that we use the mean value of a fuzzy number and l ¼ 0:5 indicates that we weights all the values derived from fuzzy assessments equally. In actual decision settings, the decision-maker may have specific preferences on experts’ fuzzy assessments. To examine how the decision-maker’s preference may affect the evaluation result, a sensitivity analysis process was carried out by changing the values of a and l. The result shows that most issues maintain similar priority rankings under different decision settings. This implies that the decision-maker’s preference on the handling of the uncertainty associated with the fuzzy assessments in this study has no significant influence on the evaluation result in terms of relative
195
priority rankings. This would give the decision-maker a reasonable assurance of the priority rankings of the 17 issues and 6 sectors presented in Tables 4 and 5. The results would help the authorities concerned develop effective-management priorities in dealing with key deficiencies and shortcomings that would best ensure the safe transport of dangerous goods in Taiwan’s air transport sectors. The result in Table 4 suggests that no single issue dominates others in all criteria. For example, although issue A11 (insufficient laws and regulations) is the most important, it has relatively low priority in other criteria. Table 5 Overall priority value and ranking of management issues and associated sectors Sector
Issue
Fuzzy priority value
Priority value (ranking)
Sector priority value (ranking)
A1
A11 A12 A13
(0.04, 0.27, 1.44) (0.04, 0.25, 1.38) (0.03, 0.24, 1.30)
0.584 (2) 0.555 (4) 0.523 (10)
0.554 (1)
A2
A21 A22 A23
(0.05, 0.24, 1.26) (0.05, 0.24, 1.32) (0.05, 0.25, 1.35)
0.517 (12) 0.537 (7) 0.551 (5)
0.535 (4)
A3
A31 A32 A33
(0.05, 0.23, 1.17) (0.05, 0.27, 1.45) (0.05, 0.25, 1.30)
0.482 (14) 0.592 (1) 0.533 (8)
0.536 (3)
A4
A41 A42 A43
(0.05, 0.22, 1.16) (0.05, 0.21, 1.10) (0.05, 0.24, 1.35)
0.476 (16) 0.455 (17) 0.549 (6)
0.493 (5)
A5
A51 A52 A53
(0.05, 0.23, 1.28) (0.05, 0.26, 1.42) (0.04, 0.24, 1.32)
0.521 (11) 0.576 (3) 0.532 (9)
0.543 (2)
A6
A61 A62
(0.04, 0.23, 1.23) (0.04, 0.22, 1.18)
0.501 (13) 0.480 (15)
0.490 (6)
Table 4 Normalized performance ratings and rankings of 17 issues on four evaluation criteria Issue
A11 A12 A13 A21 A22 A23 A31 A32 A33 A41 A42 A43 A51 A52 A53 A61 A62
Importance
Urgency
Achievability
Effectiveness
Rating
(ranking)
Rating
(ranking)
Rating
(ranking)
Rating
(ranking)
0.578 0.528 0.464 0.425 0.471 0.492 0.358 0.577 0.457 0.346 0.304 0.495 0.437 0.546 0.508 0.400 0.363
(1) (4) (9) (12) (8) (7) (15) (2) (10) (16) (17) (6) (11) (3) (5) (13) (14)
0.314 0.310 0.305 0.335 0.329 0.334 0.323 0.337 0.333 0.326 0.323 0.331 0.333 0.331 0.324 0.335 0.338
(15) (16) (17) (4) (10) (5) (13) (2) (6) (11) (14) (8) (7) (9) (12) (3) (1)
0.336 0.328 0.337 0.353 0.347 0.351 0.357 0.345 0.359 0.352 0.357 0.343 0.345 0.346 0.326 0.324 0.325
(13) (14) (12) (4) (7) (6) (3) (9) (1) (5) (2) (11) (10) (8) (15) (17) (16)
0.326 0.320 0.318 0.343 0.339 0.348 0.345 0.346 0.347 0.343 0.345 0.341 0.340 0.344 0.273 0.348 0.328
(14) (15) (16) (8) (12) (1) (4) (6) (3) (9) (5) (10) (11) (7) (17) (2) (13)
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This is because this issue may not be dealt with timely and efficiently, mainly due to the lengthy legislative processes. As indicated in Table 4, all issues have a relatively high priority with respect to at least one criterion. This seems to suggest that we cannot develop effective-management priorities by considering only one criterion. When considering all criteria, as shown in Table 5, issue A32 (insufficiency in detection of hidden or undeclared dangerous goods) has the highest overall priority. As for the management sectors, sector A1 (Taiwan’s Ministry of Transportation and Communications) should take the highest priority to provide sufficient safety structures and regulations for managing other sectors in the dangerous goods transportation chain. 6. Conclusion Prioritizing safety-related management issues at links in the transportation chain is of strategic importance for the government in setting up an action plan to ensure the safe transport of dangerous goods by air. This prioritization problem inevitably requires the involvement of experts to identify and evaluate the management issues with respect to multiple criteria. In this paper we have presented an expert survey-based approach to addressing this problem. The survey-assessment techniques used can help the experts make comparative and absolute judgments under an intrinsically imprecise environment. The multiattribute decision-making method used will generate an overall priority value for each management issue, with which the management priorities for the action plan can be developed. The approach has general application in evaluating management issues or policy alternatives with respect to multiple criteria based on experts’ comparative and absolute judgments. Acknowledgments This research was supported by the National Science Council of Taiwan (Grant NSC94-2811-H-006-001). We
are grateful to Taiwan’s aviation safety and security experts who provided assistance in problem formulation and data collection. We also thank the Editor-in-Chief, and two anonymous referees for their valuable comments.
References Abeyratne, R.I.R., 1998. The regulatory management of safety in air transport. Journal of Air Transport Management 4, 25–37. Belton, V., Stewart, T.J., 2002. Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer, Boston. Buckley, J.J., 1985. Fuzzy hierarchical analysis. Fuzzy Sets and Systems 17, 233–247. Button, K., Clarke, A., Palubinskas, G., Stough, R., Thibault, M., 2004. Conforming with ICAO safety oversight standards. Journal of Air Transport Management 10, 251–257. Chang, Y.-H., 2002. White Book on National Freight Transportation Policy—Air Cargo. Institute of Transportation, Ministry of Transportation and Communications, Taipei. Chang, Y.-H., Yeh, C.-H., 2004. A new airline safety index. Transportation Research Part B: Methodological 38, 369–383. Hwang, C.L., Yoon, K., 1981. Multiple Attribute Decision Making— Methods and Applications. Springer, New York. International Air Transport Association, 2005. Dangerous Goods Regulations, 47th ed. /http://www.iata.org/cargo/dgS. International Civil Aviation Organization, 2001. Technical Instructions for the Safe Transport of Dangerous Goods by Air (Doc 9284). ICAO, Montreal. Kaufmann, A., Gupta, M.M., 1991. Introduction to Fuzzy Arithmetic, Theory and Applications. International Thomson Computer Press, Boston. Klir, G.R., Yuan, B., 1995. Fuzzy Sets and Fuzzy Logic Theory and Applications. Prentice-Hall, Upper Saddle River. Motevalli, V., Stough, R., 2004. Aviation safety and security; reaching beyond borders. Journal of Air Transport Management 10, 225–226. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York. Thurstone, L.L., 1927. The method of paired comparisons for social values. Journal of Abnormal Psychology 21, 384–400. Yeh, C.-H., 2003. The selection of multiattribute decision making methods for scholarship student selection. International Journal of Selection and Assessment 11, 289–296. Yeh, C.-H., Kuo, Y.-L., 2003. Evaluating passenger services of AsiaPacific international airports. Transportation Research Part E: Logistics and Transportation Review 39, 35–48.
Journal of Air Transport Management 12 (2006) 191–196 www.elsevier.com/locate/jairtraman
Prioritizing management issues of moving dangerous goods by air transport Yu-Hern Changa, Chung-Hsing Yehb,, Yi-Lin Liua a
Department of Transportation and Communications Management Science, National Cheng Kung University, Tainan, 701 Taiwan, ROC b School of Business Systems, Monash University, Clayton, Victoria 3800, Australia
Abstract This paper presents an expert survey-based approach for prioritizing management issues of dangerous goods transportation faced by air-transport-related sectors in Taiwan. A two-stage survey process is used to ask the experts to first identify and then evaluate the management issues. To develop effective-management priorities, these issues are evaluated in terms of their importance, urgency, achievability, and effectiveness, based on experts’ comparative and absolute judgments. A pairwise comparison process is used to help the experts make comparative judgments, while a linguistic rating method is used for absolute judgments. To reflect the inherent imprecision involved in the survey process, experts’ assessments are represented by triangular fuzzy numbers. To obtain an overall priority value, a fuzzy multiattribute decision-making method is used. r 2006 Elsevier Ltd. All rights reserved. Keywords: Dangerous goods; Multiattribute decision-making; Fuzzy numbers
1. Introduction The increasingly integrated world economy and dynamic freight market have increased the demand to transport potentially dangerous goods by air (International Air Transport Association, 2005). To ensure safety and security standards for the air transport of dangerous goods, the International Civil Aviation Organization (ICAO) (2001) has promulgated its Technical Instructions for the Safe Transport of Dangerous Goods by Air as legal requirements for its member states. Aviation safety and security has always been the stated priority of Taiwan’s civil aviation authority, the Civil Aeronautics Administration (CAA). With its Category 1 rating in complying with ICAO safety standards (Button et al., 2004), the CAA has continued to enhance its safetyrelated programs including a comprehensive safety oversight assessment program for meeting its international obligations (Chang and Yeh, 2004). In dealing with the shipment of dangerous goods, the CAA set up an advisory committee on Corresponding author. Tel.: +61 3 9905 5808; fax: +61 3 9905 5159.
E-mail address: [email protected] (C.-H. Yeh). 0969-6997/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2006.01.007
dangerous goods transportation in 2001. Subsequently, Taiwan’s National Freight Transportation Policy stated the need to specifically establish an administrative structures for managing the transport of dangerous goods (Chang, 2002). The CAA consequently set up a safety and security unit in its Air Transport Division in 2003 and published the dangerous goods inspector handbook for use in conjunction with ICAO and IATA documents. With Taiwan aiming at conforming to international requirements, it is important to identify and address safetyrelated management issues at all points in the transportation chain to effectively meet international safety oversight requirements, as set out in ICAO’s Strategic Action Plan (Abeyratne, 1998). With scarce resources available (Motevalli and Stough, 2004), it is of strategic importance for the government to evaluate identified management issues so as to set up effective-management priorities for action.
2. Management of dangerous goods air movements in Taiwan To identify the management issues inherent in the transportation of dangerous goods, 31 middle level or
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Y.-H. Chang et al. / Journal of Air Transport Management 12 (2006) 191–196
above managers were selected with at least 5 years of relevant experience in the field. They also had to be personally willing to participate in a two-stage survey process, that inevitably introduced a degree of unavoidable bias. The first stage aims at identifying the management issues, and the second at evaluating the identified management issues. The experts included 6 government aviation officials (including 4 from the CAA and 2 from the airport and aviation police sector), 8 academic researchers from different institutions, and 17 practitioners (including 8 from 3 airline operators, 6 from 3 freight forwarders, and 3 from 3 shippers’ associations). After initial contacts, fieldwork and individual interviews were conducted between September and November 2004. The deficiencies and shortcomings of the security systems that they identified were combined into 20 management issues. To ensure the experts’ consensus on these issues, we conducted the first stage survey between January and February 2005 by asking the experts their opinion about the inclusion of these issues in the study. As a result, three issues were excluded because they could not be addressed under current practice (such as low inspection rates) or should be combined with other issues (such as lack of storage regulations and no penalty rules for incompliance with regulatory procedures). The other 17 issues were supported by at least 90% of the experts. Table 1 shows these 17 issues grouped into 6 management sectors (functional areas), each associated with the responsibility area of a corresponding government organization or industry sector. To identify key deficiencies in transport of dangerous goods by air, the 17 management issues identified are
evaluated in terms of their importance, urgency, achievability, and effectiveness. The importance criterion is used to reflect the significance and contribution of the issue to the safe transport of dangerous goods if fully addressed, as compared to other issues. The urgency criterion is used to indicate if and to what degree the nature of the issue demands urgent attention. The achievability criterion is concerned with the degree to which issues can be easily addressed within the current political, economic and technical settings. The effectiveness criterion is used to indicate the degree to which the deficiency or shortcoming can be rectified or improved if appropriate actions are taken. The four evaluation criteria are independent of each other. As such, the evaluation of the 17 issues can be formulated as a multiattribute decision-making (MADM) problem, from which a cardinal priority value can be generated. MADM provides a formal framework for modeling multiattribute decision problems, in particular when the nature of the problem demands a systematical analysis, such as the complexity of the decision, the regularity of the decision, the significant consequences, and the need for accountability (Belton and Stewart, 2002). Despite their diversity, MADM problems share several common characteristics: a finite number of comparable alternatives (management issues), multiple attributes (evaluation criteria) for comparison among alternatives, noncommensurable units for measuring performance rating of alternatives on each attribute, attribute weights for representing the relative importance of each attribute. To give each alternative an overall preference value as an indication of the decision maker’s preference for the
Table 1 Management issues of dangerous goods in Taiwan’s air transport sectors Management sector (responsible organization)
Management issue (deficiency or shortcoming)
A1
Policies and regulations (Ministry of Transportation and Communications)
A11 A12 A13
Insufficient laws and regulations Difficulties in identification of the responsible parties Lack of Chinese version of up-to-date IATA regulatory documentation
A2
Security oversight audit (Civil Aeronautics Administration)
A21 A22 A23
Inadequate training programs Insufficient qualified inspectors Lack of designated certification agents
A3
Loading and stowage (airline operators)
A31 A32
Unfamiliarity of regulatory procedures Insufficiency in detection of hidden or undeclared dangerous goods Insufficient regulatory materials and technical instructions
A33 A4
Freight handling (freight forwarders)
A41 A42 A43
Insufficient packaging Inadequate labeling and marking of dangerous goods information Unawareness of carriers, forwarders and handling agents
A5
Surveillance and inspection (Aviation Police Bureau)
A51 A52 A53
Insufficient or inadequate equipment Lack of qualified and experienced inspection personnel Unawareness of untrained customs personnel
A6
Shipper awareness (shippers)
A61 A62
Shippers’ incompliance with regulatory security procedures Shippers’ insufficient knowledge of dangerous goods
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alterative, the performance ratings of alternatives are to be aggregated with the attribute weights by using MADM. The resultant overall preference values provide a cardinal priority ranking of the alternatives. As a newly formulated evaluation problem in a new decision setting, the assessment data required for the MADM evaluation problem requires expert surveys. As such, we conducted the second stage survey between March and April 2005 asking the same experts as before to assess the weight of the four evaluation criteria, the relative importance of 6 management sectors and of the associated issues within each sector, and the achievability, urgency, and effectiveness of the 17 issues. The first two survey items require the experts’ comparative judgment, while the third survey item needs the experts to use their absolute judgment. Thirty-one questionnaire forms were distributed and 4 government aviation officials, 7 academic researchers, and 17 air cargo practitioners replied. 3. Criteria weights and relative importance of management issues by comparative judgment To make comparative judgment on the relative importance of the criteria, each is compared with all other criteria. As there are limitations to the amount of information that humans can effectively handle, a pairwise comparison approach used to help the experts make comparative judgment. The concept of pairwise comparisons has been known since the work of Thurstone (1927) and has been implemented in the analytic hierarchy process (AHP) of Saaty (1980). In the AHP, a 1–9 ratio scale is used to compare two alternatives (criteria or issues) for indicating the strength of their relative preference. Applying this procedure to all m alternatives results in a positive m m reciprocal matrix with all its elements xij ¼ 1=xji (i ¼ 1; 2; . . . ; m; j ¼ 1; 2; . . . ; m). Since the issues identified and the evaluation criteria may be vaguely defined, the survey assessment is conducted in an intrinsically imprecise manner. To reflect the imprecision involved in this process, the ratio value given by the experts is represented by a corresponding triangular fuzzy number (a1 , a2 , a3 ), where a2 is the most possible value, and a1 and a3 are the lower and upper bounds, respectively used to reflect the fuzziness of the survey assessment. With the use of triangular fuzzy numbers, the arithmetic operations on fuzzy numbers are based on interval arithmetic (Kaufmann and Gupta, 1991). Table 2 illustrates how a triangular fuzzy number is generated to represent the imprecise assessment given by an
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expert using a numeric ratio value. If the ratio value given is 5 (‘‘strongly more important’’), the fuzzy assessment represented as a triangular fuzzy number is (3, 5, 7). This implies that the assessment is ‘‘about 5’’ for reflecting the vagueness of the subjective judgment. If the ratio value given is 9, the fuzzy assessment is (7, 9, 9). This fuzzy representation of a crisp value facilitates the experts’ subjective assessment in the survey, as they require no knowledge of fuzzy numbers for making fuzzy (imprecise) assessment. The experts give the most possible ratio value for each comparison of two alternatives that matches the corresponding qualitative assessment term, shown as in the first row of Table 2. This fuzzy representation is similar in essence to the use of a set of linguistic terms characterized by given triangular fuzzy numbers, but it provides the experts with more options (9 possible fuzzy numbers instead of 5 for the 5 terms given in the table) for their qualitative judgments. In solving a fuzzy positive reciprocal matrix resulting from pairwise comparisons using fuzzy ratios, Buckley (1985) uses the geometric mean method to calculate the fuzzy relative importance values for all the alternatives. This method possesses a number of properties and can be easily applied to situations where multiple experts are involved in the assessment process. Given a fuzzy positive reciprocal matrix R ¼ ½xij (i ¼ 1; 2; . . . ; m; j ¼ 1; 2; . . . ; m), the method first calculates the geometric mean of each row as !1=m m Y ri ¼ xij . (1) j¼1
The fuzzy relative importance values wi for m alternatives Ai (i ¼ 1; 2; . . . ; m) are then computed as , m X wi ¼ ri rj . (2) j¼1
The pairwise comparison process with fuzzy ratios has been applied to help the experts assess the weight of the four evaluation criteria. Table 3 shows the fuzzy weights of the four evaluation criteria. Criteria weights are usually normalized to sum to unity, to allow the weight value to be interpreted as the percentage of the total importance weight (Belton and Stewart, 2002). It would be tedious for the experts to apply the above pairwise comparison process directly to all the issues. To facilitate the comparison process, we develop a hierarchical approach, where the issues are grouped and compared within
Table 2 Ratio value fuzzification of pairwise comparisons Equally important
Moderately more important
Strongly more important
Very strongly more important
Extremely more important
1
2
4
6
8
3 a1
5 a2
7 a3
9
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where r1 , r2 , y, rk are the performance ratings of the issue given by experts k (k ¼ 1; 2; . . . ; q), respectively.
Table 3 Fuzzy weights of evaluation criteria Criteria
Fuzzy weight
Importance Urgency Achievability Effectiveness
(0.22, (0.14, (0.13, (0.14,
0.35, 0.21, 0.21, 0.23,
5. Prioritizing management issues with fuzzy survey assessments
0.55) 0.34) 0.34) 0.36)
their corresponding management sector (see Table 1 again). Instead of comparing between all the 17 issues, the pairwise comparison process is conducted across six management sectors, and between issues within each management sector. As such, only 31 ( ¼ 15+3+3+3+3+3+1) comparisons are required. It is noteworthy that when comparing the management sectors in pairs, their associated issues are presented and explained to the experts. To obtain the fuzzy relative importance values of all the issues across six management sectors, the fuzzy relative importance values of the issues under each management sector are normalized by taking the relative importance value of the corresponding sector as their mean value. Taking the fuzzy relative importance value of a management sector Ai (i ¼ 1; 2; . . . ; 6) is vAi and the fuzzy relative importance values of its Ni associated issues (Ai1, Ai2, y, AiN i ) within the sector Ai are viAih (h ¼ 1; 2; . . . ; N i ), then the fuzzy relative importance values of these Ni issues among all issues are , ! Ni X i i vAih . vAih ¼ vAi vAih N i (3) h¼1
With the fuzzy weights of the four criteria and the fuzzy performance ratings of the issues obtained, an MADM method can be used to normalize and aggregated to obtain an overall priority value for each issue. Research in MADM suggests the use of simple and understandable approaches for solving practical MADM problems. The simple additive weighting (SAW) method, also known as the weighted sum method, is probably the best-known method (Hwang and Yoon, 1981). The use of an additive value function in SAW can be intuitively appealing to the decision-maker in practical applications, and can be justified theoretically and empirically (Yeh, 2003). The basic logic of the SAW method is to obtain a weighted sum of the performance ratings of each alternative over all criteria. If the performance ratings are assessed based on different scales, a normalization process is required to transform all the ratings of different units to a comparable scale, so that the inter-criteria comparisons can be made: ,sffiffiffiffiffiffiffiffiffiffiffiffiffi m X yij ¼ xij x2ij ; i ¼ 1; 2; . . . ; m; j ¼ 1; 2; . . . ; n, (5) i¼1
where xij are the performance ratings of alternatives Ai (i ¼ 1; 2; . . . ; m) with respect to criteria Cj (j ¼ 1; 2; . . . ; n). With the SAW method, the overall preference or priority value (Pi) of alternatives Ai (i ¼ 1; 2; . . . ; m) is obtained as Pi ¼
4. Assessment of urgency, achievability, and effectiveness of management issues A five-point Likert-type scale is used for rating the 17 issues with respect to their urgency, achievability, and effectiveness. The rating values in the scale are derived by using five linguistic terms {Very Low, Low, Medium, High, Very High}, that are associated with a corresponding set of numbers {1, 2, 3, 4, 5}. The experts’ assessment on the performance rating of newly established issues against vaguely defined qualitative evaluation criteria is intrinsically imprecise. As such, the assessment results given by all experts are aggregated and represented as a triangular fuzzy number. The fuzzy number (a1 , a2 , a3 ) for representing the fuzzy rating of an issue on a criterion assessed by all q experts is given as a1 ¼ minfr1 ; r2 ; . . . ; rk g; !1=q q Y a2 ¼ rk ,
k ¼ 1; 2; . . . ; q,
k¼1
a3 ¼ maxfr1 ; r2 ; . . . ; rk g;
k ¼ 1; 2; . . . ; q,
ð4Þ
n X
wj yij ;
i ¼ 1; 2; . . . ; m,
(6)
j¼1
where wj are the weights of criteria Cj (j ¼ 1; 2; . . . ; n) and yij are the normalized performance ratings of alternatives Ai (i ¼ 1; 2; . . . ; m) with respect to criteria Cj (j ¼ 1; 2; . . . ; n). The greater the value (Pi), the higher priority the alternative (Ai). To compare the overall fuzzy priority value of the various issues, we use the a-cut in fuzzy set theory (Klir and Yuan, 1995). The a-cut of a fuzzy set is the ordinary set that contains all the values with a membership degree of at least a (where 0pap1). By using a a-cut on a triangular fuzzy number, a value interval [xal , xar ] is derived. The value of a can be used to represent the decision maker’s degree of confidence in the fuzzy assessments made by the experts. A larger a-value indicates that the decision maker is more confident, as the interval is smaller and has a higher possibility. To reflect the decision maker’s relative preference between xal and xar , an attitude index l in the range of 0 and 1 can be incorporated. As a result, a crisp value can be obtained as xla ¼ lxar þ ð1 lÞxal ; 0plp1.
(7)
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The value of l can be used to reflect the decision maker’s attitude towards risk (Yeh and Kuo, 2003). In actual decision settings, l ¼ 1, 0.5, or 0 can be used to indicate that the decision maker has an optimistic, moderate, or pessimistic view respectively on fuzzy assessment results. Table 4 shows the normalized performance ratings of the 17 issues and their corresponding ranking order with respect to each evaluation criterion, with a ¼ 0 and l ¼ 0:5. With the data in Tables 3 and 4, an overall fuzzy priority value for each issue can be obtained by applying Eq. (6). Column 3 of Table 5 shows the result. To rank the issues, we apply Eq. (7) with a ¼ 0 and l ¼ 0:5. Column 4 of Table 5 shows the result and the corresponding ranking order. As each of these issues is associated with a management sector, we can obtain the priority value for each sector by averaging the priority values of its associated issues. Column 5 of Table 5 shows the result and the corresponding ranking order. The priority-ranking order of the 6 sectors remains the same if we aggregate the priority values within each sector instead of averaging. The settings used for a and l reflect no particular preference for the fuzzy assessments made by the experts. Further, a ¼ 0 implies that we use the mean value of a fuzzy number and l ¼ 0:5 indicates that we weights all the values derived from fuzzy assessments equally. In actual decision settings, the decision-maker may have specific preferences on experts’ fuzzy assessments. To examine how the decision-maker’s preference may affect the evaluation result, a sensitivity analysis process was carried out by changing the values of a and l. The result shows that most issues maintain similar priority rankings under different decision settings. This implies that the decision-maker’s preference on the handling of the uncertainty associated with the fuzzy assessments in this study has no significant influence on the evaluation result in terms of relative
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priority rankings. This would give the decision-maker a reasonable assurance of the priority rankings of the 17 issues and 6 sectors presented in Tables 4 and 5. The results would help the authorities concerned develop effective-management priorities in dealing with key deficiencies and shortcomings that would best ensure the safe transport of dangerous goods in Taiwan’s air transport sectors. The result in Table 4 suggests that no single issue dominates others in all criteria. For example, although issue A11 (insufficient laws and regulations) is the most important, it has relatively low priority in other criteria. Table 5 Overall priority value and ranking of management issues and associated sectors Sector
Issue
Fuzzy priority value
Priority value (ranking)
Sector priority value (ranking)
A1
A11 A12 A13
(0.04, 0.27, 1.44) (0.04, 0.25, 1.38) (0.03, 0.24, 1.30)
0.584 (2) 0.555 (4) 0.523 (10)
0.554 (1)
A2
A21 A22 A23
(0.05, 0.24, 1.26) (0.05, 0.24, 1.32) (0.05, 0.25, 1.35)
0.517 (12) 0.537 (7) 0.551 (5)
0.535 (4)
A3
A31 A32 A33
(0.05, 0.23, 1.17) (0.05, 0.27, 1.45) (0.05, 0.25, 1.30)
0.482 (14) 0.592 (1) 0.533 (8)
0.536 (3)
A4
A41 A42 A43
(0.05, 0.22, 1.16) (0.05, 0.21, 1.10) (0.05, 0.24, 1.35)
0.476 (16) 0.455 (17) 0.549 (6)
0.493 (5)
A5
A51 A52 A53
(0.05, 0.23, 1.28) (0.05, 0.26, 1.42) (0.04, 0.24, 1.32)
0.521 (11) 0.576 (3) 0.532 (9)
0.543 (2)
A6
A61 A62
(0.04, 0.23, 1.23) (0.04, 0.22, 1.18)
0.501 (13) 0.480 (15)
0.490 (6)
Table 4 Normalized performance ratings and rankings of 17 issues on four evaluation criteria Issue
A11 A12 A13 A21 A22 A23 A31 A32 A33 A41 A42 A43 A51 A52 A53 A61 A62
Importance
Urgency
Achievability
Effectiveness
Rating
(ranking)
Rating
(ranking)
Rating
(ranking)
Rating
(ranking)
0.578 0.528 0.464 0.425 0.471 0.492 0.358 0.577 0.457 0.346 0.304 0.495 0.437 0.546 0.508 0.400 0.363
(1) (4) (9) (12) (8) (7) (15) (2) (10) (16) (17) (6) (11) (3) (5) (13) (14)
0.314 0.310 0.305 0.335 0.329 0.334 0.323 0.337 0.333 0.326 0.323 0.331 0.333 0.331 0.324 0.335 0.338
(15) (16) (17) (4) (10) (5) (13) (2) (6) (11) (14) (8) (7) (9) (12) (3) (1)
0.336 0.328 0.337 0.353 0.347 0.351 0.357 0.345 0.359 0.352 0.357 0.343 0.345 0.346 0.326 0.324 0.325
(13) (14) (12) (4) (7) (6) (3) (9) (1) (5) (2) (11) (10) (8) (15) (17) (16)
0.326 0.320 0.318 0.343 0.339 0.348 0.345 0.346 0.347 0.343 0.345 0.341 0.340 0.344 0.273 0.348 0.328
(14) (15) (16) (8) (12) (1) (4) (6) (3) (9) (5) (10) (11) (7) (17) (2) (13)
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This is because this issue may not be dealt with timely and efficiently, mainly due to the lengthy legislative processes. As indicated in Table 4, all issues have a relatively high priority with respect to at least one criterion. This seems to suggest that we cannot develop effective-management priorities by considering only one criterion. When considering all criteria, as shown in Table 5, issue A32 (insufficiency in detection of hidden or undeclared dangerous goods) has the highest overall priority. As for the management sectors, sector A1 (Taiwan’s Ministry of Transportation and Communications) should take the highest priority to provide sufficient safety structures and regulations for managing other sectors in the dangerous goods transportation chain. 6. Conclusion Prioritizing safety-related management issues at links in the transportation chain is of strategic importance for the government in setting up an action plan to ensure the safe transport of dangerous goods by air. This prioritization problem inevitably requires the involvement of experts to identify and evaluate the management issues with respect to multiple criteria. In this paper we have presented an expert survey-based approach to addressing this problem. The survey-assessment techniques used can help the experts make comparative and absolute judgments under an intrinsically imprecise environment. The multiattribute decision-making method used will generate an overall priority value for each management issue, with which the management priorities for the action plan can be developed. The approach has general application in evaluating management issues or policy alternatives with respect to multiple criteria based on experts’ comparative and absolute judgments. Acknowledgments This research was supported by the National Science Council of Taiwan (Grant NSC94-2811-H-006-001). We
are grateful to Taiwan’s aviation safety and security experts who provided assistance in problem formulation and data collection. We also thank the Editor-in-Chief, and two anonymous referees for their valuable comments.
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