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Journal of Air Transport Management 12 (2006) 191–196

www.elsevier.com/locate/jairtraman

Prioritizing management issues of moving dangerous goodsby air transport

Yu-Hern Changa, Chung-Hsing Yehb,�, Yi-Lin Liua

aDepartment of Transportation and Communications Management Science, National Cheng Kung University, Tainan, 701 Taiwan, ROCbSchool 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 dynamicfreight market have increased the demand to transportpotentially dangerous goods by air (International AirTransport Association, 2005). To ensure safety andsecurity standards for the air transport of dangerousgoods, the International Civil Aviation Organization(ICAO) (2001) has promulgated its Technical Instructionsfor the Safe Transport of Dangerous Goods by Air as legalrequirements for its member states.

Aviation safety and security has always been the statedpriority of Taiwan’s civil aviation authority, the CivilAeronautics Administration (CAA). With its Category 1rating in complying with ICAO safety standards (Buttonet al., 2004), the CAA has continued to enhance its safety-related programs including a comprehensive safety oversightassessment program for meeting its international obligations(Chang and Yeh, 2004). In dealing with the shipment ofdangerous goods, the CAA set up an advisory committee on

e front matter r 2006 Elsevier Ltd. All rights reserved.

irtraman.2006.01.007

ing author. Tel.: +613 9905 5808; fax: +61 3 9905 5159.

ess: ChungHsing.Yeh@infotech.monash.edu.au

dangerous goods transportation in 2001. Subsequently,Taiwan’s National Freight Transportation Policy statedthe need to specifically establish an administrative structuresfor managing the transport of dangerous goods (Chang,2002). The CAA consequently set up a safety and securityunit in its Air Transport Division in 2003 and published thedangerous goods inspector handbook for use in conjunctionwith ICAO and IATA documents.With Taiwan aiming at conforming to international

requirements, it is important to identify and address safety-related management issues at all points in the transporta-tion chain to effectively meet international safety oversightrequirements, as set out in ICAO’s Strategic Action Plan(Abeyratne, 1998). With scarce resources available (Mote-valli and Stough, 2004), it is of strategic importance for thegovernment to evaluate identified management issues so asto set up effective-management priorities for action.

2. Management of dangerous goods air movements in

Taiwan

To identify the management issues inherent in thetransportation of dangerous goods, 31 middle level or

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above managers were selected with at least 5 years ofrelevant experience in the field. They also had to bepersonally willing to participate in a two-stage surveyprocess, that inevitably introduced a degree of unavoidablebias. The first stage aims at identifying the managementissues, and the second at evaluating the identified manage-ment issues.

The experts included 6 government aviation officials(including 4 from the CAA and 2 from the airport andaviation police sector), 8 academic researchers fromdifferent institutions, and 17 practitioners (including 8from 3 airline operators, 6 from 3 freight forwarders, and 3from 3 shippers’ associations). After initial contacts,fieldwork and individual interviews were conducted be-tween September and November 2004. The deficiencies andshortcomings of the security systems that they identifiedwere combined into 20 management issues. To ensure theexperts’ consensus on these issues, we conducted the firststage survey between January and February 2005 by askingthe experts their opinion about the inclusion of these issuesin the study. As a result, three issues were excluded becausethey could not be addressed under current practice (such aslow inspection rates) or should be combined with otherissues (such as lack of storage regulations and no penaltyrules for incompliance with regulatory procedures). Theother 17 issues were supported by at least 90% of theexperts. Table 1 shows these 17 issues grouped into 6management sectors (functional areas), each associatedwith the responsibility area of a corresponding governmentorganization or industry sector.

To identify key deficiencies in transport of dangerousgoods by air, the 17 management issues identified are

Table 1

Management issues of dangerous goods in Taiwan’s air transport sectors

Management sector (responsible organization)

A1 Policies and regulations (Ministry of Transportation and

Communications)

A2 Security oversight audit (Civil Aeronautics Administration)

A3 Loading and stowage (airline operators)

A4 Freight handling (freight forwarders)

A5 Surveillance and inspection (Aviation Police Bureau)

A6 Shipper awareness (shippers)

evaluated in terms of their importance, urgency, achiev-ability, and effectiveness. The importance criterion is usedto reflect the significance and contribution of the issue tothe safe transport of dangerous goods if fully addressed, ascompared to other issues. The urgency criterion is used toindicate if and to what degree the nature of the issuedemands urgent attention. The achievability criterion isconcerned with the degree to which issues can be easilyaddressed within the current political, economic andtechnical settings. The effectiveness criterion is used toindicate the degree to which the deficiency or shortcomingcan be rectified or improved if appropriate actions aretaken.The four evaluation criteria are independent of each

other. As such, the evaluation of the 17 issues can beformulated as a multiattribute decision-making (MADM)problem, from which a cardinal priority value can begenerated. MADM provides a formal framework formodeling multiattribute decision problems, in particularwhen the nature of the problem demands a systematicalanalysis, such as the complexity of the decision, theregularity 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 comparablealternatives (management issues), multiple attributes (eva-luation criteria) for comparison among alternatives, non-commensurable units for measuring performance rating ofalternatives on each attribute, attribute weights forrepresenting the relative importance of each attribute. Togive each alternative an overall preference value as anindication of the decision maker’s preference for the

Management issue (deficiency or shortcoming)

A11 Insufficient laws and regulations

A12 Difficulties in identification of the responsible parties

A13 Lack of Chinese version of up-to-date IATA regulatory

documentation

A21 Inadequate training programs

A22 Insufficient qualified inspectors

A23 Lack of designated certification agents

A31 Unfamiliarity of regulatory procedures

A32 Insufficiency in detection of hidden or undeclared dangerous

goods

A33 Insufficient regulatory materials and technical instructions

A41 Insufficient packaging

A42 Inadequate labeling and marking of dangerous goods

information

A43 Unawareness of carriers, forwarders and handling agents

A51 Insufficient or inadequate equipment

A52 Lack of qualified and experienced inspection personnel

A53 Unawareness of untrained customs personnel

A61 Shippers’ incompliance with regulatory security procedures

A62 Shippers’ insufficient knowledge of dangerous goods

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alterative, the performance ratings of alternatives are to beaggregated with the attribute weights by using MADM.The resultant overall preference values provide a cardinalpriority ranking of the alternatives.

As a newly formulated evaluation problem in a newdecision setting, the assessment data required for theMADM evaluation problem requires expert surveys. Assuch, we conducted the second stage survey between Marchand April 2005 asking the same experts as before to assessthe weight of the four evaluation criteria, the relativeimportance of 6 management sectors and of the associatedissues within each sector, and the achievability, urgency,and effectiveness of the 17 issues. The first two survey itemsrequire the experts’ comparative judgment, while the thirdsurvey item needs the experts to use their absolutejudgment. Thirty-one questionnaire forms were distributedand 4 government aviation officials, 7 academic research-ers, 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 impor-tance of the criteria, each is compared with all othercriteria. As there are limitations to the amount ofinformation that humans can effectively handle, a pairwisecomparison approach used to help the experts makecomparative judgment. The concept of pairwise compar-isons 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 isused to compare two alternatives (criteria or issues) forindicating the strength of their relative preference. Apply-ing this procedure to all m alternatives results in a positivem�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 inan intrinsically imprecise manner. To reflect the impreci-sion involved in this process, the ratio value given by theexperts is represented by a corresponding triangular fuzzynumber (a1, a2, a3), where a2 is the most possible value, anda1 and a3 are the lower and upper bounds, respectively usedto reflect the fuzziness of the survey assessment. With theuse of triangular fuzzy numbers, the arithmetic operationson fuzzy numbers are based on interval arithmetic(Kaufmann and Gupta, 1991).

Table 2 illustrates how a triangular fuzzy number isgenerated to represent the imprecise assessment given by an

Table 2

Ratio value fuzzification of pairwise comparisons

Equally important Moderately more important Strongly more importa

1 2 3 4 5

a1 a2

expert using a numeric ratio value. If the ratio value givenis 5 (‘‘strongly more important’’), the fuzzy assessmentrepresented as a triangular fuzzy number is (3, 5, 7). Thisimplies that the assessment is ‘‘about 5’’ for reflecting thevagueness of the subjective judgment. If the ratio valuegiven is 9, the fuzzy assessment is (7, 9, 9). This fuzzyrepresentation of a crisp value facilitates the experts’subjective assessment in the survey, as they require noknowledge of fuzzy numbers for making fuzzy (imprecise)assessment. The experts give the most possible ratio valuefor each comparison of two alternatives that matches thecorresponding qualitative assessment term, shown as in thefirst row of Table 2. This fuzzy representation is similar inessence to the use of a set of linguistic terms characterizedby given triangular fuzzy numbers, but it provides theexperts with more options (9 possible fuzzy numbersinstead of 5 for the 5 terms given in the table) for theirqualitative judgments.In solving a fuzzy positive reciprocal matrix resulting

from pairwise comparisons using fuzzy ratios, Buckley(1985) uses the geometric mean method to calculate thefuzzy relative importance values for all the alternatives.This method possesses a number of properties and can beeasily applied to situations where multiple experts areinvolved in the assessment process. Given a fuzzy positivereciprocal matrix R ¼ ½xij� (i ¼ 1; 2; . . . ;m; j ¼ 1; 2; . . . ;m),the method first calculates the geometric mean of eachrow as

ri ¼Ymj¼1

xij

!1=m

. (1)

The fuzzy relative importance values wi for m alter-natives Ai (i ¼ 1; 2; . . . ;m) are then computed as

wi ¼ ri

,Xm

j¼1

rj. (2)

The pairwise comparison process with fuzzy ratios hasbeen applied to help the experts assess the weight of thefour evaluation criteria. Table 3 shows the fuzzy weights ofthe four evaluation criteria. Criteria weights are usuallynormalized to sum to unity, to allow the weight value to beinterpreted as the percentage of the total importanceweight (Belton and Stewart, 2002).It would be tedious for the experts to apply the above

pairwise comparison process directly to all the issues. Tofacilitate the comparison process, we develop a hierarchicalapproach, where the issues are grouped and compared within

nt Very strongly more important Extremely more important

6 7 8 9

a3

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Table 3

Fuzzy weights of evaluation criteria

Criteria Fuzzy weight

Importance (0.22, 0.35, 0.55)

Urgency (0.14, 0.21, 0.34)

Achievability (0.13, 0.21, 0.34)

Effectiveness (0.14, 0.23, 0.36)

Y.-H. Chang et al. / Journal of Air Transport Management 12 (2006) 191–196194

their corresponding management sector (see Table 1 again).Instead of comparing between all the 17 issues, the pairwisecomparison process is conducted across six managementsectors, and between issues within each management sector.As such, only 31 ( ¼ 15+3+3+3+3+3+1) comparisonsare required. It is noteworthy that when comparing themanagement sectors in pairs, their associated issues arepresented and explained to the experts.

To obtain the fuzzy relative importance values of all theissues across six management sectors, the fuzzy relativeimportance values of the issues under each managementsector are normalized by taking the relative importancevalue of the corresponding sector as their mean value.Taking the fuzzy relative importance value of a manage-ment sector Ai (i ¼ 1; 2; . . . ; 6) is vAi

and the fuzzy relativeimportance values of its Ni associated issues (Ai1, Ai2, y,AiNi

) within the sector Ai are viAih

(h ¼ 1; 2; . . . ;Ni), then thefuzzy relative importance values of these Ni issues amongall issues are

vAih¼ vAi

� viAih� Ni

,XNi

h¼1

viAih

!. (3)

4. Assessment of urgency, achievability, and effectiveness of

management issues

A five-point Likert-type scale is used for rating the 17issues with respect to their urgency, achievability, andeffectiveness. The rating values in the scale are derived byusing five linguistic terms {Very Low, Low, Medium, High,Very High}, that are associated with a corresponding set ofnumbers {1, 2, 3, 4, 5}.

The experts’ assessment on the performance rating ofnewly established issues against vaguely defined qualitativeevaluation criteria is intrinsically imprecise. As such, theassessment results given by all experts are aggregated andrepresented as a triangular fuzzy number. The fuzzynumber (a1, a2, a3) for representing the fuzzy rating of anissue on a criterion assessed by all q experts is given as

a1 ¼ minfr1; r2; . . . ; rkg; k ¼ 1; 2; . . . ; q,

a2 ¼Yq

k¼1

rk

!1=q

,

a3 ¼ maxfr1; r2; . . . ; rkg; k ¼ 1; 2; . . . ; q, ð4Þ

where r1, r2, y, rk are the performance ratings of the issuegiven by experts k (k ¼ 1; 2; . . . ; q), respectively.

5. Prioritizing management issues with fuzzy survey

assessments

With the fuzzy weights of the four criteria and the fuzzyperformance ratings of the issues obtained, an MADMmethod can be used to normalize and aggregated to obtainan overall priority value for each issue. Research inMADM suggests the use of simple and understandableapproaches for solving practical MADM problems. Thesimple additive weighting (SAW) method, also known asthe weighted sum method, is probably the best-knownmethod (Hwang and Yoon, 1981). The use of an additivevalue function in SAW can be intuitively appealing to thedecision-maker in practical applications, and can bejustified theoretically and empirically (Yeh, 2003).The basic logic of the SAW method is to obtain a

weighted sum of the performance ratings of each alternativeover all criteria. If the performance ratings are assessedbased on different scales, a normalization process isrequired to transform all the ratings of different units to acomparable scale, so that the inter-criteria comparisons canbe made:

yij ¼ xij

, ffiffiffiffiffiffiffiffiffiffiffiffiffiXm

i¼1

x2ij

s; i ¼ 1; 2; . . . ;m; j ¼ 1; 2; . . . ; n, (5)

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 ¼Xn

j¼1

wjyij ; i ¼ 1; 2; . . . ;m, (6)

where wj are the weights of criteria Cj (j ¼ 1; 2; . . . ; n) andyij are the normalized performance ratings of alter-natives Ai (i ¼ 1; 2; . . . ;m) with respect to criteria Cj (j ¼ 1;2; . . . ; n). The greater the value (Pi), the higher priority thealternative (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 thatcontains all the values with a membership degree of at least a(where 0pap1). By using a a-cut on a triangular fuzzynumber, a value interval [xa

l , xar ] is derived. The value of a can

be used to represent the decision maker’s degree of confidencein the fuzzy assessments made by the experts. A larger a-valueindicates that the decision maker is more confident, as theinterval is smaller and has a higher possibility.To reflect the decision maker’s relative preference

between xal and xa

r , an attitude index l in the range of 0and 1 can be incorporated. As a result, a crisp value can beobtained as

xla ¼ lxa

r þ ð1� lÞxal ; 0plp1. (7)

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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 (0.04, 0.27, 1.44) 0.584 (2) 0.554 (1)

A12 (0.04, 0.25, 1.38) 0.555 (4)

A13 (0.03, 0.24, 1.30) 0.523 (10)

A2 A21 (0.05, 0.24, 1.26) 0.517 (12) 0.535 (4)

A22 (0.05, 0.24, 1.32) 0.537 (7)

A23 (0.05, 0.25, 1.35) 0.551 (5)

A3 A31 (0.05, 0.23, 1.17) 0.482 (14) 0.536 (3)

A32 (0.05, 0.27, 1.45) 0.592 (1)

A33 (0.05, 0.25, 1.30) 0.533 (8)

A4 A41 (0.05, 0.22, 1.16) 0.476 (16) 0.493 (5)

A42 (0.05, 0.21, 1.10) 0.455 (17)

A43 (0.05, 0.24, 1.35) 0.549 (6)

A5 A51 (0.05, 0.23, 1.28) 0.521 (11) 0.543 (2)

A52 (0.05, 0.26, 1.42) 0.576 (3)

A53 (0.04, 0.24, 1.32) 0.532 (9)

A6 A61 (0.04, 0.23, 1.23) 0.501 (13) 0.490 (6)

A62 (0.04, 0.22, 1.18) 0.480 (15)

Y.-H. Chang et al. / Journal of Air Transport Management 12 (2006) 191–196 195

The value of l can be used to reflect the decision maker’sattitude towards risk (Yeh and Kuo, 2003). In actualdecision settings, l ¼ 1, 0.5, or 0 can be used to indicatethat the decision maker has an optimistic, moderate, orpessimistic view respectively on fuzzy assessment results.

Table 4 shows the normalized performance ratings of the17 issues and their corresponding ranking order withrespect to each evaluation criterion, with a ¼ 0 andl ¼ 0:5. With the data in Tables 3 and 4, an overall fuzzypriority value for each issue can be obtained by applyingEq. (6). Column 3 of Table 5 shows the result. To rank theissues, we apply Eq. (7) with a ¼ 0 and l ¼ 0:5. Column 4of Table 5 shows the result and the corresponding rankingorder. As each of these issues is associated with amanagement sector, we can obtain the priority value foreach sector by averaging the priority values of itsassociated issues. Column 5 of Table 5 shows the resultand the corresponding ranking order. The priority-rankingorder of the 6 sectors remains the same if we aggregate thepriority values within each sector instead of averaging.

The settings used for a and l reflect no particularpreference for the fuzzy assessments made by the experts.Further, a ¼ 0 implies that we use the mean value of afuzzy number and l ¼ 0:5 indicates that we weights all thevalues derived from fuzzy assessments equally. In actualdecision settings, the decision-maker may have specificpreferences on experts’ fuzzy assessments. To examine howthe decision-maker’s preference may affect the evaluationresult, a sensitivity analysis process was carried out bychanging the values of a and l. The result shows that mostissues maintain similar priority rankings under differentdecision settings. This implies that the decision-maker’spreference on the handling of the uncertainty associatedwith the fuzzy assessments in this study has no significantinfluence on the evaluation result in terms of relative

Table 4

Normalized performance ratings and rankings of 17 issues on four evaluation

Issue Importance Urgency

Rating (ranking) Rating (ranking)

A11 0.578 (1) 0.314 (15)

A12 0.528 (4) 0.310 (16)

A13 0.464 (9) 0.305 (17)

A21 0.425 (12) 0.335 (4)

A22 0.471 (8) 0.329 (10)

A23 0.492 (7) 0.334 (5)

A31 0.358 (15) 0.323 (13)

A32 0.577 (2) 0.337 (2)

A33 0.457 (10) 0.333 (6)

A41 0.346 (16) 0.326 (11)

A42 0.304 (17) 0.323 (14)

A43 0.495 (6) 0.331 (8)

A51 0.437 (11) 0.333 (7)

A52 0.546 (3) 0.331 (9)

A53 0.508 (5) 0.324 (12)

A61 0.400 (13) 0.335 (3)

A62 0.363 (14) 0.338 (1)

priority rankings. This would give the decision-maker areasonable assurance of the priority rankings of the 17issues and 6 sectors presented in Tables 4 and 5.The results would help the authorities concerned develop

effective-management priorities in dealing with key defi-ciencies and shortcomings that would best ensure the safetransport of dangerous goods in Taiwan’s air transportsectors. The result in Table 4 suggests that no single issuedominates others in all criteria. For example, althoughissue A11 (insufficient laws and regulations) is the mostimportant, it has relatively low priority in other criteria.

criteria

Achievability Effectiveness

Rating (ranking) Rating (ranking)

0.336 (13) 0.326 (14)

0.328 (14) 0.320 (15)

0.337 (12) 0.318 (16)

0.353 (4) 0.343 (8)

0.347 (7) 0.339 (12)

0.351 (6) 0.348 (1)

0.357 (3) 0.345 (4)

0.345 (9) 0.346 (6)

0.359 (1) 0.347 (3)

0.352 (5) 0.343 (9)

0.357 (2) 0.345 (5)

0.343 (11) 0.341 (10)

0.345 (10) 0.340 (11)

0.346 (8) 0.344 (7)

0.326 (15) 0.273 (17)

0.324 (17) 0.348 (2)

0.325 (16) 0.328 (13)

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This is because this issue may not be dealt with timely andefficiently, mainly due to the lengthy legislative processes.As indicated in Table 4, all issues have a relatively highpriority with respect to at least one criterion. This seems tosuggest that we cannot develop effective-managementpriorities by considering only one criterion. When con-sidering all criteria, as shown in Table 5, issue A32

(insufficiency in detection of hidden or undeclared danger-ous goods) has the highest overall priority. As for themanagement sectors, sector A1 (Taiwan’s Ministry ofTransportation and Communications) should take thehighest priority to provide sufficient safety structures andregulations for managing other sectors in the dangerousgoods transportation chain.

6. Conclusion

Prioritizing safety-related management issues at links inthe transportation chain is of strategic importance for thegovernment in setting up an action plan to ensure the safetransport of dangerous goods by air. This prioritizationproblem inevitably requires the involvement of experts toidentify and evaluate the management issues with respectto multiple criteria. In this paper we have presented anexpert survey-based approach to addressing this problem.The survey-assessment techniques used can help the expertsmake comparative and absolute judgments under anintrinsically imprecise environment. The multiattributedecision-making method used will generate an overallpriority value for each management issue, with which themanagement priorities for the action plan can be devel-oped. The approach has general application in evaluatingmanagement issues or policy alternatives with respectto multiple criteria based on experts’ comparative andabsolute judgments.

Acknowledgments

This research was supported by the National ScienceCouncil of Taiwan (Grant NSC94-2811-H-006-001). We

are grateful to Taiwan’s aviation safety and securityexperts who provided assistance in problem formulationand data collection. We also thank the Editor-in-Chief, andtwo anonymous referees for their valuable comments.

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