A decision support model for risk management of hazardous materials road transportation based on quality function deployment

Transportation Research Part D 74 (2019) 154–173

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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

A decision support model for risk management of hazardous materials road transportation based on quality function deployment

T

Yan-Lai Lia,b, Qiang Yanga,b, , Kwai-Sang Chinc ⁎

a

School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 611756, People’s Republic of China National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan 611756, People’s Republic of China c Department of Systems Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, People’s Republic of China b

ARTICLE INFO

ABSTRACT

Keywords: Hazardous materials Quality function deployment Failure mode and effect analysis Nonlinear goal programming Trapezoidal fuzzy number Fuzzy analytic hierarchy process

Risk management of hazardous materials (hazmats) road transportation has long been a concern because of the potential hazards that poses to society and the environment. In this work, a systematic and semi-quantitative decision support framework for risk management of hazmats road transportation based on the combination of quality function deployment (QFD), fuzzy analytic hierarchy process (F-AHP), fuzzy failure mode and effect analysis (F-FMEA), and nonlinear goal programming is proposed. The QFD is used innovatively to construct the overall framework, which contains three main components of general risk management: risk identification, risk assessment, and risk control. The F-AHP is used to build a hierarchical risk assessment system and determine the importance rating of each risk factor. The F-FMEA is used to evaluate the potential risks of risk control measures and determine the risk adjustment coefficient of each risk measure, which is used subsequently to modify the fulfillment level of risk measure in the nonlinear goal programming model. To address the inherent vagueness and uncertainty contained in the risk management process, the fuzzy set theory is introduced as an effective tool. An empirical case on risk management of a hazmats transportation company is presented to demonstrate the effectiveness and feasibility of the proposed methodology. Some managerial implications on risk management of hazmats road transportation are provided based on the obtained findings.

1. Introduction The US Department of Transportation (US DOT) defines hazardous materials (hazmats) as those substances or materials that have physical and chemical characteristics that may be harmful to human life, property, and environment (US DOT, 2004). Nevertheless, hazmats still play an essential role in our daily lives and in industrial production. With the rapid development of the global economy, especially the industry, the production and consumption of hazmats have also increased rapidly. Generally, the production of hazmats is inconsistent with the consumption in terms of time and space, and therefore, hazmats transportation is a necessary means to

⁎ Corresponding author at: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 611756, People’s Republic of China. E-mail address: [email protected] (Q. Yang).

https://doi.org/10.1016/j.trd.2019.07.026

1361-9209/ © 2019 Elsevier Ltd. All rights reserved.

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overcome this inconsistency. Road transportation is a major means of transporting hazmats because of its flexibility, convenience, and “door-to-door” characteristic. According to statistics, the total amount of hazmats transported by road is more than 80% in China (Zhong et al., 2018) and more than 90% in USA (Panwhar et al., 2000). The expansion of hazmats transportation on roads has led to an increase in transportation accidents involving hazmats. Yang et al. (2010) conducted a survey on the accidents of hazmats transportation by road in China from 2000 to 2008. Their research findings revealed that the accident frequencies increased significantly with the growth of GDP. According to the study of Ditta et al. (2018) that based on an incident report, the number of incidents increased from 14,816 to 17,459 from 2009 to 2012 in USA. Despite the probability of hazmats transportation accidents being relatively small, the consequences in terms of human life safety, environmental damage, social impact, and economic loss can be catastrophic. One example is an accident that occurred in July 19, 2014 in China. A truck carrying ethanol collided with a large passenger car on Shanghai-Kunming highway, causing a large amount of ethanol and burn instantly, ultimately causing 54 fatalities, 6 injuries (four of them died due to excessive injuries), and more than 53 million RMB (approximately equal to 7,783,358 USD) in terms of direct economic loss. With the awareness of the management requirements, the road transportation of hazmats has become a significant concern for the governments, the public, and enterprises because of the high risk involved in the road transportation industry, the potential risk of hazmats themselves, and the amount of harm that hazmats transportation accidents can cause (Fan et al., 2015; Yang et al., 2010). Therefore, the reduction of the risk and loss has become a significant and practical research focus. For risk mitigation of hazmats transportation, scholars and practitioners have conducted substantial amounts of research over the past decades. Chiou (2016, 2017) proposed two signal control policy-based models to regulate the risk and to minimize the cost of hazmats transportation by road. Different models and techniques, such as decision support systems (Pradhananga et al. 2014; Zografos and Androutsopoulos, 2008) and Geographical Information System (GIS) (Bonvicini and Spadoni, 2008; Verter and Kara, 2001) have also been developed and applied. Erkut et al. (2007) and Ditta et al. (2018) conducted a classical literature review from the 1980s to 2007 and the latest literature review from 2008 to 2016, respectively. Based on the review and analysis of previous studies, gaps in the hazmats transportation field were observed in terms of research object and research methodology. From the aspect of the research object, most studies focus only on one or two segments of the hazmats transportation process, which results in lacking systematic and universal applicability. To our best knowledge, only Yang et al. (2018) proposed an overall framework for risk management of hazmats transportation based on quality function deployment (QFD). Nevertheless, their research only determined the importance of risk control measures and did not consider the effectiveness of each measure nor did they provide suggestions on resources allocation for maximizing risk management effects. In terms of research methodology, the majority of the previous studies used data-dependent quantitative methods. However, two practical situations constrained their application. On the one hand, collecting data that would fit perfectly with the models can be difficult, especially in China, which has no specialized database to record the previous accidents information systematically. On the other hand, the risk management problems of hazmats transportation contain a mixture of quantitative and qualitative indicators and therefore, full-quantitative techniques are inadequate to address the problem. In addition, the previous methods for risk assessment contain complicated mathematical models that are difficult to understand and use for general managers. More details on the limitations of existing studies are discussed further in the “Literature review” section. To bridge the abovementioned gaps, this paper proposed a systematic framework for risk management of hazmats road transportation, which is consistent with the process of general risk management composed of three steps: risk identification, risk assessment, and risk control (Zhao et al., 2014). The present study proposed a semi-quantitative methodology that makes full use of the experts’ experience and knowledge when historical data are not available. Fuzzy linguistic variables and trapezoidal fuzzy numbers (TpFNs) were introduced to ensure convenience in the evaluation and computation of information given by experts. House of quality (HOQ), the core tool of QFD, was introduced to formulate the main body of the proposed framework, identify the risk factors, derive the risk control measures, and to represent the relationship between risk factors and risk control measures and the correlation among risk control measures. Fuzzy analytical hierarchy process (F-AHP) was developed to determine the importance rating of risk factors. Fuzzy failure mode and effect analysis (F-FMEA) was used to evaluate the potential failure risk of each measure and derive the risk adjustment coefficient. Finally, a nonlinear goal programming mathematic model was formulated to determine the optimized fulfillment level of each risk measure and maximize the effectiveness of the overall risk control. The structure of this study is as follows. Section 2 briefly reviews the literature on risk management of hazmats road transportation, points out the inadequacies of existing research, and offers solutions to fill these gaps. In Section 3, the proposed systematic and semi-quantitative methodology for risk management of hazmats road transportation, which is based on QFD, F-AHP, FMEA, and nonlinear goal programming, is explained in detail. Section 4 provides an empirical case to demonstrate the feasibility and effectiveness of the proposed methodology. A discussion of our research findings is also presented in this section. Section 6 summarizes the study and points out the future research directions. 2. Literature review In this section, we mainly reviewed the literature on hazmats road transportation. After referring to three classical and recent literature reviews on hazmats transportation by Erkut et al. (2007), Ditta et al. (2018), and Holeczek (2019), we could clearly see that the previous literature on hazmats road transportation can be grouped into four major categories in terms of the type of problem as follows: risk assessment, routing/scheduling, facility location and routing, network design. Regardless of the research subjects, the common purpose is to reduce the risks of hazmats transportation. Further, considering the main purpose of the present study, we mainly reviewed the studies on risk assessment of hazmats road transportation, while reviewed the other three aspects briefly. Risk assessment is one of the most popular topics related to hazmats road transportation, and serves as the basis for risk 155

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mitigation. Abkowitz and Cheng (1989) reviewed several alternative methods developed for assessing the risk of hazmats transportation using limited empirical data with poor quality. Their findings showed that a formidable challenge in developing and selecting an appropriate approach for risk estimation according to practical situations remain. Verter and Kara (2001) proposed a Geographical Information System (GIS)-based risk assessment methodology for hazmats transportation in the setting of multi-commodity and multiple origin-destination pairs on a network. Clark and Besterfield-Sacre (2009) developed a probability and statisticsbased Bayesian network decision model, which fully utilized the historical incident data. In this study, the quality and completeness of collected data have an obvious direct influence on the efficiency of the proposed model. Qiao et al. (2009) developed an accident frequency estimation model for hazmats transportation based on empirical databases and fuzzy sets. In this study, Negative binomial regression was used to derive the basic accident frequency of route-dependent variables, while fuzzy logic was used to model the effects of route-independent variables. Zhao et al. (2012) applied Bayesian networks to rate the factors that influence hazmats transportation risk. Ambituuni et al. (2015) focused on developing a risk assessment framework for improving the regulations for hazmats transportation in developing country, such as Nigeria. Routing and scheduling are another primary theme and means of risk mitigation of hazmats transportation. Jia et al. (2011) proposed a fuzzy-stochastic constraint programming model for route design that considered the natural accident risk and terrorist threaten simultaneously for minimizing the risks of hazmats transportation. Toumazis and Kwon (2013) proposed a dynamic conditional value-at-risk (CVaR) model to determine the optimal departure time and optimal route of hazmats transportation on timedependent networks to minimize transportation risks. However, complicated and challenging issues on data uncertainty, inadequacy and inaccuracy remain. Zero et al. (2019) proposed two extended algorithms to solve the bi-objective shortest path problem with a fuzzy objective for hazmats transportation. In this study, fuzzy logic was used to deal with the risk exposure and uncertainty of performance criterion. Hazmats transportation could be a hazmat collection or distribution problem. Therefore, the location of the origin, destination or even a stop between origin and destination has considerable influence on the transportation risks. Particularly, many studies focused on the combination of location and routing for transportation risk mitigation. Xie et al. (2012) presented a multi-objective and multimodal location and routing model using a mixed integer linear programming for optimizing the transfer yard locations and hazmats transportation routes simultaneously under the constraints of transportation risk and cost. It is the first study to optimize facility location and routing of multimodal hazmats transportation, which includes two or more shipment modes such as air, sea, and railway. Meiyi et al. (2015) studied the location-scheduling problem of hazmats transportation under fuzzy random environment. An improved particle swarm optimization algorithm was defined to solve the proposed model. However, the numerical experiment and data used to demonstrate the efficiency of the proposed approach are simulated. Network design is another effective way of planning hazmats transportation from an overall perspective. Kara and Verter (2004) presented a bi-level programming methodology for designing a road network for hazmats transportation, which can mitigate the risk as much as possible. The authors also pointed out that a path-based approach is another appropriate choice for designing the network (Verter and Kara, 2008). Erkut and Alp (2007) proposed a two-stage framework for designing a road network and selecting routes in a densely populated center. Bianco et al. (2009) presented a linear bilevel programming framework for designing a hazmat transportation network that considers the total transportation risk and risk equity (i.e., fairly spreading the risk to any area that embedded in the transportation network). This is the first attempt in a network design problem that considers the jurisdictional differences among different authorities. In summary, although the final goal of all research is risk mitigation of hazmats transportation, two main problems remain. One, the previous studies have focused on only one or two components of risk management, thereby resulting in a lack of systemicity and comprehensiveness to the problem. Typically, a comprehensive risk management decision on hazmats transportation would involve the simultaneous consideration of three main components of risk management, that is risk identification, risk assessment, and risk control/mitigation. To overcome this gap, we proposed a QFD and nonlinear programming-based framework for risk management of hazmats transportation comprehensively. Second, the majority of existing research is mathematical model-based quantitative approaches, which depend considerably on the availability and accuracy of input data. Unfortunately, the needed data are not always available or complete especially in developing countries, such as China, which have no professional database to record and store historical data. Therefore, the data dependent approaches may not be effective under this condition. To overcome this disadvantage, we proposed a semi-quantitative method that uses the knowledge and experience of field experts. We also introduced fuzzy theory into our proposed framework considering the inherent vagueness and uncertainty of evaluation information during the risk management process. The utilization of fuzzy theory in this paper is different from the previous studies, which mainly use fuzzy logic to deal with the uncertain probability of accident frequency. Furthermore, the previously proposed quantitative methods and solving algorithms that based on probability and statistics like Bayesian network, GIS or accident database for risk assessment are relatively complicated. Therefore, these methods may be difficult to understand and apply for general managers who have no professional skills and knowledge in the certain disciplines. To solve this issue, a more understandable, applicable, and automatic method is urgently needed. 3. Proposed methodology and case study In this section, the proposed QFD-based framework that used for risk management of hazmats road transportation is introduced in detail. The proposed framework consists of three phases and seven components in total according to the structure of QFD and the general process of risk management. Simultaneously, to verify the applicability and effectiveness of the proposed QFD-based three-phase risk management framework, 156

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an empirical case was provided, in which all the application steps were explained in detail. M company (anonymity because of privacy requirements) is a modern logistics and transportation enterprise with independent legal person transportation qualification for hazmats road transportation in Sichuan province, China. The hazmats that allowed to be transported by the company include fireworks, industrial gases, flammable liquids, peroxides, drugs, corrosives and miscellaneous. Up to now, the company has more than three hundred professional hazmats transport vehicles, 69 safety management personnel, and more than seven hundred qualified drivers. The company addresses the principle of “safety first and customer first” so that high-quality service could be provided to the target customers. However, over the past three years, the market share of the company in local marketing has lost approximately eight percent. To ensure the safety of hazmats road transportation and allocate the limited enterprise resources to improve transportation service quality and enhance the market competitiveness of enterprises, the company decided to adopt the methodology proposed in this study. The specific application is described as below. 3.1. Construction of the QFD-based framework Quality function deployment (QFD), initially introduced by Akao et al. (1990), is a customer requirement-oriented method for product and service design or improvement. The definition of QFD by Akao et al. (1990) is “a method to transform customer requirements into design quality, to deploy the functions forming quality, and to deploy methods for achieving the design quality into subsystems and component parts and ultimately to specific elements of the manufacturing process”. Obviously, QFD is not just a tool but a planning process that emphasizes a transformation from initial customer requirements to design quality of product development and quality management. Over the past few decades, QFD has been widely recognized and successfully applied to many fields such as logistics, education, and transportation (Lam, 2015; McCowan, 2018; Wu et al., 2017). In particular, QFD has recently been demonstrated to be suitable for risk management (Bas, 2014; Lam and Bai, 2016; Sadeghi et al., 2016). Therefore, we use the QFD methodology in our study to formulate the overall framework for risk management of hazmats road transportation. The core tool of the QFD methodology is the so-called house of quality (HOQ), whose components are customer requirements (also called “WHATs”) and its importance rating, competition analysis of “WHATs”, engineering characteristics (also called “HOWs”), the relationship matrix between “WHATs” and “HOWs”, the correlation matrix among “HOWs”, and the importance rating of “HOWs”. The general structure of HOQ is shown in Fig. 1 (a). In our case study, the basic structure can be perfectly used after an appropriate adjustment. The risk factors are regarded as “WHATs” and the risk control measures are regarded as “HOWs”. Moreover, we added potential risk evaluation of risk measures using failure mode and effect analysis (FMEA) and nonlinear goal programming to determine the optimized fulfillment level of each risk measure. The proposed risk management framework based on QFD is shown in Fig. 1 (b).

Correlation Matrix among HOWs

Correlation Matrix among HOWs

Engineering Characteristics (HOWs)

Importance Rating of HOWs

Relationship Matrix between WHATs and HOWs

FMEA Coefficient of HOWs

(a) HOQ for product development

Fulfillment Level of HOWs

(b) HOQ for risk management Fig. 1. Structure of HOQ. 157

Importance Rating of WHATs

Risk Factors (WHATs)

Competition Analysis of WHATs

Importance Rating of WHATs

Customer Requirements (WHATs)

Relationship Matrix between WHATs and HOWs

Risk Control Measures (HOWs)

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3.2. Three-phase risk management process of hazmats road transportation 3.2.1. Phase 1: Risk identification The first phase of the proposed risk management framework is to identify the potential risk factors that have influences on hazmats transportation accidents. The identified factors are considered the input for the whole process. Therefore, the identified risk factors should be placed on the left wall of the HOQ. It is also demonstrated that risk identification has a fundamental position in the risk management process of hazmats road transportation. Two steps are included in this phase. Step 1. Establish a cross-functional expert team and determine the weight of each expert. For collecting the related information as much as possible and considering as much stakeholders as possible, a cross-functional expert team consisting of members from government department, management department and practice department of hazmats transportation company should be constructed first. Moreover, because of the different degree of experience, knowledge, and discourse power of the team members, the weight of each expert should be different. Assume that the expert set is defined as E = {e1, e2, , et } , and the corresponding weight vector is = ( 1, 2, , t )T , where t represents the number of experts included in the cross-functional team. In the case study, a team consisting of four experts is established, in which one member is the top manager of the company (e1), one from the government department of hazmats road transportation (e2 ), and two truck drivers with more than ten years of experience in hazmats road transportation. In particular, one of the drivers belongs to the company (e3) and the other one is selected from the hazmats transportation market (e4 ). The weight vector is = (0.2, 0.2, 0.3, 0.3)T . Step 2. Identify the risk factors and formulate a hierarchy structure. There are many factors related to the risk of hazmats transportation, which can be collected by referring to the related research literature, suggestion from transportation enterprises, historical accident data, the operation law and standards of industry and customer feedbacks on hazmats road transportation. In particular, the “management regulation of hazmats road transportation” that promulgated by the Ministry of Transport of the People’s Republic of China provides an effective way to identify the risk factors. The file strictly defines the concept of hazmats and the necessary conditions of conducting hazmats road transportation business, which includes special vehicles and equipment, special parking lot, qualified practitioners and safety managers as well as a well-formulated management system for safety production. Contents that are more specific can refer to http://zizhan.mot.gov.cn/zfxxgk/bnssj/zcfgs/ 201604/t20160425_2018244.html. Further, it is worth noting that the initial risk factors obtained by the previous methods may mean the same or have relationship between each other; thus, these risk factors should be analyzed and classified according to their characteristics. The affinity diagram method proposed by Griffin and Hauser (1993) can be used to group the factors. Subsequently, all the obtained risk factors can be grouped into a hierarchical structure, which shows the relative relation among these identified risk factors. Finally, the processed risk factors should be placed on the left wall of the HOQ (i.e., the first part in Fig. 1 (b)). We denote the risk factors as ci, i = 1, 2, , n . For the case study, a two-layer structure with eleven risk factors related closely to hazmats road transportation risk is identified based on the systematic and comprehensive principles of index system construction. Details are shown in Table 1. 3.2.2. Phase 2: Risk assessment Risk assessment is the second phase of risk management. In this study, risk assessment refers to determining the influence degree of each risk factor on hazmats transportation accidents. That is to say, the importance rating of each risk factor should be determined at this moment. Keep up with the prior phase, one step that has three sub-steps is included in this phase. Step 3. Determine the importance rating of each risk factor using F-AHP. Table 1 Factors related to hazmats road transportation risk (sources: (Ambituuni et al., 2015; Brito and de Almeida, 2009; Clark and Besterfield-Sacre, 2009; Tong et al., 2006; Yang et al., 2010; Yang et al., 2018; Zhang and Zhao, 2007; Zhao et al., 2012). Factors

Sub-factors

Definition

Direct factors

Professional skills (D1) Physiological conditions (D2) Safety awareness (D3) Transportation equipment (D4) Protective equipment (D5)

Experience, knowledge and operation technique for hazmats transportation Health condition and age of the operators Psychological state established for risk prevention Transport vehicles, packaging equipment, loading, and unloading equipment, etc. Affiliated safety facilities used to isolate the damage such as flame inhibitor, firefighting installations, sunshade covering and the like Physical and chemical characteristics of the hazmat, such as volume, density, flammability, explosion, poison, and corrosion, etc. The physical geographic conditions of a road; The relevant facilities along a road, such as protective facilities, rest areas, lighting facilities, and others; The human environment along the road. Atmospheric phenomena, such as sunny, heavy rain, snow, and fog, etc. Procurement, audit, and daily maintenance of the relevant transportation and protective equipment Development, training, and perfection degree of an emergency plan before the accident occurred

Nature of the hazmats (D6) Indirect factors

Road conditions (I1) Weather conditions (I2) Equipment supervision (I3) Emergency plan management (I4) Law and system management (I5)

Disciplinary, regulations, and laws for hazmats operation

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In order to facilitate the expression of experts, to avoid the information loss of initial evaluation and to deal with the vagueness and uncertainty associated with the evaluation data in the decision-making process, we introduced the fuzzy set theory (Zadeh, 1965). Trapezoidal fuzzy number (TpFN), which is usually represented by M = (a, b, c, d ) , is a special form of a fuzzy set. It can deal with the fuzzy conditions in more accurate and general ways (Sadi-Nezhad and Damghani, 2010). We used the TpFN as a decision tool in this study to deal with the uncertainty and fuzziness of evaluation information during the risk management process of hazmats road transportation. The related definition and operation of a positive TpFN can refer to Bodjanova (2005). Particularly, to facilitate the comparison between different TpFNs in an intuitive manner, the TpFNs are usually converted into crisp values. The transformation method proposed by Lin and Lee (2006) is introduced into our study as follows.

Md =

(a + 2b + 2c + d ) 6

(1)

where Md is a defuzzified crisp value corresponding to M . Moreover, a linguistic variable is a general tool used in a fuzzy and qualitative decision-making environment because it is more natural to human thinking and expression (Zadeh, 1975). For example, when evaluating the risk degree of one factor related to hazmats transportation, experts are more willing to use words or expressions, such as “very high”, “moderate”, and “low”. Linguistic variables are usually represented by corresponding predefined fuzzy numbers to accomplish quantitative computation. The analytic hierarchy process (AHP) was initially proposed by Satty (1980). Based on the combination of fuzzy set theory and the AHP method, we introduced a TpFN-valued fuzzy analytic hierarchy process (F-AHP). The F-AHP can be used for determining the priority or importance of multiple alternatives or criteria. In this study, the F-AHP method is used to determine the importance rating of risk factors. Compared with the traditional crisp value-based AHP, the F-AHP method has an advantage in dealing with vagueness and uncertainty associated with data. The process of using F-AHP is shown as follows (Zheng et al., 2012). Step 3.1. Construct the fuzzy judgment matrix Similar to traditional AHP, pairwise comparison is required to show the relative importance between two selected risk factors. Traditional AHP method uses a crisp-valued nine-point scale to represent the comparison, while the F-AHP in our study uses fuzzy linguistic, which then be transformed into TpFN. In this study, the scale used to measure the comparison and the corresponding TpFNs are shown in Table 2 (Zheng et al., 2012). A fuzzy judgment matrix, Pt , is formulated based on the evaluation of expert et .

Pt

=

t p11

p1tn

pnt1

t pnn

(2)

where pijt represents the rating score of risk factor ci comparing with cj provided by expert et . In particular, pijt is a TpFN represented by

piji = (aijt , bijt , cijt, dijt ) and pjit =

1 pijt

=

1 1 1 1 , , , dijt cijt bijt aijt

.

Step 3.2. Check the consistency of the fuzzy judgment matrix The consistency of each fuzzy judgment matrix should be tested first before calculating the importance rating of each risk factor. Only the matrices that satisfy the consistency check can be used to calculate the weights of risk factors. On the contrary, the matrices that dissatisfy the consistency check should be adjusted or abandoned. This action is used to guarantee the accuracy of evaluation compared with the direct scoring method. To simplify the consistency check process of F-AHP, we assume that the fuzzy judgment matrix, Pt , is consistent if the corresponding matrix, Ptd , that is represented by crisp value is consistent. A consistency check method, named the largest eigenvalue method, is adopted to accomplish this step. The specific steps can refer to Shapiro and Koissi (2017). Usually, the concept of consistency ratio (CR) is used to represent the consistency of a judgment matrix. CR is determined by CR = CI /RI , where CI = ( max n)/(n 1) represents the consistency index and RI represents the random index shown in Table 3 (Satty, 1980). max represents the largest eigenvalue of a matrix. Only when CR is less than 0.1 (i.e., CR < 0.1) can the consistency of the matrix be acceptable and vice versa. Step 3.3. Calculate the importance rating of risk factor. Based on the consistency-satisfied fuzzy judgment matrix, the importance rating of each risk factor can be calculated using the following formulations. Table 2 Scale of importance rating used to conduct pairwise comparison. Crisp valued scale

Equivalent TpFN valued scale

Linguistic variable

1 3 5 7 9 2, 4, 6, 8

(1.0, 1.0, 1.0, 1.0) (2.0, 2.5, 3.5, 4.0) (4.0, 4.5, 5.5, 6.0) (6.0, 6.5, 7.5, 8.0) (8.0, 8.5, 9.0, 9.0) (x 1, x 0.5, x + 0.5, x + 1), x = 2, 4, 6, 8

Equally important (EI) Moderately important (MI) Strongly important (SI) Very strongly important (VI) Absolutely important (AI) Between the adjacent assessment value

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Table 3 The random index (RI) used in AHP. n

1

2

3

4

5

6

7

8

9

RI

0

0

0.58

0.90

1.12

1.24

1.32

1.41

1.46

t i

=

n

n

aijt ,

j=1

n t

=

n

n

j=1

n t i,

=

i

t

=

i=1

wit = ( it / t ,

i=1 t t i/ ,

bijt ,

=

i

n t i, t t i / ,

t

t

cijt ,

i

=

n

n

j=1

dijt

(3)

t i

=

i=1 t t i / ),

j=1

n t i ,

=

n

n

(4)

i=1

i = 1, 2,

(5)

,n

Finally, the integrated importance rating vector of risk factor is W = (w1, w2, wi, , wn , where wi = is a TpFN. The obtained importance rating vector of risk factor is the second component in Fig. 1 (b). In our case, an example to calculate the importance rating of risk factor is shown as follows. The linguistic variable presented and corresponding TpFN-valued comparison matrix among the first layer provided by e1 is described as in Table 4. According to the abovementioned steps, we can obtain the TpFN-valued weight of risk factors aswD1 = (0.366, 0.511, 0.851, 1.098) , 1 = (0.211, 0.264, 0.44, 0.518) . Similar processes are conducted to compute the local weights of sub-factors under and wID its upper layer and the specific input data is included in Appendix A. Then, the global weight of each sub-factor determined by e1 can be obtained by taking into account the weights of the upper layer and using multiplication of TpFN. Finally, considering the weight of each expert, the group integrated weight vector of the sub-factors is W = T (0.090, 0.158, 0.403, 0.650), (0.018, 0.032, 0.090, 0.162), (0.039, 0.073, 0.214, 0.375), (0.049, 0.092, 0.257, 0.437), (0.019, 0.035, 0.102, 0.183), (0.010, 0.017, 0.047, 0.083), (0.049, 0.085, 0.245, 0.380), (0.009, 0.014, 0.039, 0.065), (0.014, 0.023, 0.068, 0.113), (0.022, 0.037, 0.110, 0.184), (0.026, 0.044, 0.129, 0.205) Further, based on the defuzzification method in Eq. (1) and the normalization method, the crisp-valued weight vector of subfactors can be calculated, the result is Wd = (0.238, 0.054, 0.127, 0.151, 0.061, 0.028, 0.140, 0.023, 0.040, 0.064, 0.074)T . T t=1

)T

t i wi

3.2.3. Phase 3: Risk control The third phase of risk management is to put forward control measures and allocate limited resources to control the identified risks. It is the goal and the main content of risk management. In this phase, the risk control measures corresponding to identified risk factors and their basic importance rating would be determined first. Then, a fuzzy failure mode and effect analysis (F-FMEA) method is proposed to determine the risk priority number (RPN) and risk adjustment coefficient of each risk control measure. Finally, the optimized fulfillment of each risk control measure, which would be a reference for limited resources allocation would be computed. Specifically, five steps (i.e., step 4 – step 8) are proposed in this phase. Step 4. Derive the risk control measures. As previously stated that QFD is an effective tool to translate the customer requirements into design requirements, and many methods haven been developed to achieve this action. Similarly, the risk control measures corresponding to the identified risk factors can be derived by the cross-functional team. We denote the risk control measures as Mj , j = 1, 2, , m . The obtained risk measures are positioned on the third part in Fig. 1 (b). In the present study, the cross-functional expert team proposed eight risk control measures, which are shown in Table 5. Step 5. Compute the basic importance rating of each risk control measure. The basic importance rating of each risk control measure can be obtained based on the relationship evaluation between ci and Mj with the following steps. Step 5.1. Each expert provides the relationship evaluation results between risk factors and risk measures using fuzzy linguistic variables provided in Table 6. Table 4 Pairwise comparison matrix of the risk factors provided by e1

D ID

Linguistic variable represented matrix

TpFN-valued matrix

D

ID

D

ID

EI

[EI, MI] EI

(1, 1, 1, 1) (0.33, 0.4, 0.67, 1)

(1, 1.5, 2.5, 3) (1, 1, 1, 1)

Note: [EI, MI] means the evaluation value is between EI and MI. In the pairwise comparison matrix, the experts only need to fill in the blank with a 1 value bigger than one and the symmetric blank is determined automatically according to the relationship pji = p . ij

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Table 5 Identified risk control measures Symbol

Risk measure

Symbol

Risk measure

M1 M2 M3 M4

Skills training Practitioner qualification control Safety awareness education Equipment supervision and maintenance

M5 M6 M7 M8

Transportation route planning and scheduling Emergency plan formulation and drill Laws and regulations improvement Real-time monitoring and interaction

Table 6 Linguistic variables for relationship and correlation evaluation Linguistic variable

Equivalent TpFN valued scale

Very weak (VW) Weak (W) Moderate (M) Strong (S) Very strong (VS)

(0, (1, (3, (5, (7,

1, 2, 4, 6, 8,

2, 3, 5, 7, 9,

3) 4) 6) 8) 10)

Step 5.2. Construct the single relationship evaluation matrix by transforming the linguistic variables into corresponding TpFNs. We denote the TpFN-valued relationship matrix provided by et as

REt =

t R11

R1tm

Rnt1

t Rnm

(6)

Step 5.3. Obtain the group matrix of relationship evaluation by integrating all single matrices into one. The weights of experts are considered and the operational laws of TpFN are used in the integration process. We denote the group matrix of relationship evaluation as

R11

RE =

R1m

Rn1

(7)

Rnm

where Rij = is a TpFN. The group matrix is the fourth part in Fig. 1 (b). Step 5.4. Compute the basic importance rating of risk measures without considering the correlation among risk control measures. The basic importance rating of each risk control measure is co-determined by the weight of risk factor, wi , and the group relationship matrix, RE , between risk factors and risk measures. Thus, the basic importance rating of risk measures can be obtained by the following formulation. T t=1

t t Rij

n

wj =

wi Rij

(8)

i=1

where wj is a TpFN, and the weight vector of risk measures is W = (w1, w2, wj, , wm Then, the crisp-valued basic weight of risk measures can be obtained based on Eq. (1). Following this step, based on all the single relationship evaluation results provided by each expert (See Table B.1 in Appendix B), the integrated group matrix of relationship evaluation can be obtained as shown in Table B.2 in Appendix B. Then, by taking into consideration of the integrated weight vector of risk factors, the TpFN-valued basic importance rating of risk control measures can be (1.02, 2.311, 7.581, 14.87), (0.717, 1.738, 5.995, 12.2), (0.699, 1.821, 6.681, 13.89), calculated. The result is W = (0.815, 1.901, 6.612, 13.22), (0.899, 2.105, 7.032, 14.35), (1.027, 2.404, 8.475, 16.84), . Finally, after the (0.663, 1.723, 6.384, 13.23), (0.828, 1.999, 7.127, 14.3) defuzzification using Eq. (1) and the normalization process, crisp-valued importance rating vector of risk measures is Wd = (0.135, 0.108, 0.120, 0.118, 0.129, 0.150, 0.114, 0.126)T . Step 6. Evaluate the correlation among risk control measures. The correlation among risk control measures should be considered when acting one risk measure may affect others. For simplifying, assume that the correlation between two risk measures is symmetric in this study. The evaluation process is similar to that of obtaining the group matrix of relationship evaluation between risk factors and risk measures in step 5. Therefore, the detailed steps are omitted here. We use the symbol r jh to represent the correlation between Mj and Mh . It is the fifth part in Fig. 1 (b). In the case study, the experts use the linguistic variables provided in Table 6 to evaluate the correlation among the risk control measures. We assume that the correlation between two risk measures is symmetric. Subsequently, considering the weights of experts, all the single correlation evaluation matrices explained in Table C.1 of Appendix C can be integrated into a combined group evaluation matrix, which is shown in Table C.2 in Appendix C.

)T .

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Step 7. Compute the RPN and risk adjustment coefficient of each risk measure based on F-FMEA. It is worth noting that the derived risk control measures are not always effective because of failures may occur during the implementation process. Therefore, it is necessary to conduct an analysis on the failure influences of each risk measure. The failure mode and effects analysis (FMEA) is one of the most widely used risk evaluation methods and was initially developed by the United States Army and then applied to aerospace industry (Bowles and Peláez, 1995). The FMEA is used to identify and prioritize the potential or known failure modes for improving the reliability of certain systems (Cicek and Celik, 2013). Further, in this study, based on the combination of fuzzy set theory and traditional FMEA method, we proposed an extended fuzzy FMEA (F-FMEA) method to analyze the potential failure influences of each risk control measure. Step 7.1. Identify the potential failure modes, effects, and causes of risk measures. Risk measures are used to design the FMEA to evaluate the potential failure modes and effects for risk control failure analysis. Each risk control measure may face a failure, and the effects and causes should further be derived. To find out the failure modes, effects, and causes of risk measures is a critical step for computing their corresponding risk priority number (RPN). Step 7.2. Determine the RPN of each failure mode. In traditional FMEA, the concept of risk priority number (RPN) is proposed to determine the priority of each failure mode. RPN is further determined by multiplying three risk factors (i.e., occurrence (O), severity (S), and detection (D)), which exhibit a crisp value from 1 to 10. Mathematically, the RPN is represented as follows: (9)

RPN = O × S × D

where O represents the occurrence frequency of a certain failure mode, S represents the severity of a failure when it occurred, and D represents the probability to detect the failure before it occurred. Finally, the risk priority of each failure mode is determined according to the RPN. However, the traditional FMEA with crisp value has been criticized much because it overlooks the inherent vagueness and uncertainty during the evaluation process (Bowles and Peláez, 1995; Gargama and Chaturvedi, 2011; Wang et al., 2009). To overcome this disadvantage, fuzzy logic and fuzzy set theory are regarded as one of the most effective ways. Moreover, to facilitate the expression of evaluation information, linguistic variables are also widely used. Therefore, in this study, the fuzzy linguistics shown in Table 7 are used to evaluate of the severity, occurrence probability, and detection probability of each failure mode and then the linguistic variables are transformed into the corresponding TpFNs. The obtained fuzzy RPN is an effective reference to allocate limited resources. That is, to ensure the safety and reliability of a certain system, resources should be assigned preferentially to failure modes with higher RPN. Step 7.3. Determine the risk adjustment coefficient based on RPN. In order to use the obtained fuzzy RPN in the later nonlinear goal programming model of resources allocation, we proposed the concept of risk adjustment coefficient, f j , which is derived from RPN.

fj =

1000 1000 + (RPNj

(10)

k)

where RPNj represent the RPN value of the j - th failure mode corresponding to the j - th risk measure and k represents the lowest boundary that a failure mode does not need to be fixed. Particularly, if (RPNj k ) < 0 , then let (RPNj k ) = 0 . In this study, we set k = 100 . The specific meaning of k = 100 represents that the lowest boundary is up to 90%. The obtained risk adjustment coefficient, f j , should be positioned on the sixth part in Fig. 1 (b). Following the above steps, the expert team first identified the potential failure modes, effects, and causes corresponding to the risk control measures. The results are shown in Table 8. Then, each expert uses the linguistic variables provided in Table 6 to evaluate the severity, occurrence, and detection probability of each failure mode corresponding to the risk control measures. All the single evaluation matrix were explained in Table D.1 in Appendix D. Subsequently, considering the weights of experts, the combined group evaluation matrix can be obtained. Table D.2 in Appendix D shows the integrated group matrix of FMEA evaluation. Finally, based on Eqs. (9) and (10), we can obtain the normalized RPN vector for each failure mode: RPN = (0.170, 0.054, 0.204, 0.198, 0.090. 0.156, 0.084, 0.043)T and the risk adjustment coefficient vector for each failure mode: f = (0.758, 0.967, 0.712, 0.721, 0.891, 0.779, 0.903, 0.993)T . All previously obtained results can be placed in the appropriate spot in HOQ for risk management as shown in Fig. 2. Step 8. Determine the optimized fulfillment level of each risk measure. When using QFD for new product development, the satisfaction level of each customer requirement (WHAT) is determined by the fulfillment level of all engineering characteristics (HOWs) related to WHAT. Thus, the total satisfaction of a product can be obtained Table 7 Linguistic variables for relationship and correlation evaluation. TpFN-valued scale

Linguistic variable for severity (S) and occurrence (O)

Linguistic variable for detection (D)

(0, (1, (3, (5, (7,

Very low (VL) Low (L) Moderate (M) High (H) Very High (VH)

Very easy (VE) Easy (E) Moderate (M) Difficult (D) Very difficult (VD)

1, 2, 4, 6, 8,

2, 3, 5, 7, 9,

3) 4) 6) 8) 10)

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Table 8 Potential failure modes, effects, and causes corresponding to risk measures. Risk control measures (HOWs)

Potential failure mode

Potential effects of failure

Potential causes of failure

Skill training (M1)

Low level of professional skill (F1)

High operation error

Practitioner qualification control (M2)

Unqualified practitioners (F2)

Safety awareness education (M3)

Low level of safety awareness (F3)

Increased management difficulty Unsafe operation

Equipment supervision and maintenance (M4)

Equipment failure (F4)

Transportation accident

Transportation route planning and scheduling (M5)

Wrong or inappropriate transportation route (F5)

Increased transportation cost and risk

Emergency plan formulation and drill (M6)

Emergency plan failure (F6)

Inefficient accident handling

Improvement of laws and regulations (M7)

Inefficient laws and regulations (F7)

Violated operation

Real-time monitoring and interaction (M8)

Ineffective control (F8)

Low possibility of risk prevention

Limitations of skills training Low acceptability of skills training Information fraud or false report Insufficient qualification verification Limitations of skills training Low acceptability of skills training Lack of attention Insufficient degree of supervision Untimely repair of problematic equipment Undiscovered equipment problems Unreasonable planning and scheduling Inconsistent route selection with planning Unexpected road conditions Unreasonable formulation of an emergency plan Insufficient drill Inappropriate emergency handling in violation of the plan Incomplete consideration of laws and regulations Lack of execution Breakdown of the real-time system Transmission of incorrect information

2.30 1.05

0.75

3.70 4.70 3.50 0.75 2.40 2.70 2.40 5.50 Weights

I

WHATs

Risk factors

D

2.50 4.30

0.75

4.10

3.50

6.10

0.90

2.90

3.70

3.30

3.50

1.20

Correlations among HOWs

2.60

4.10

6.50

M1

M2

M3

M4

M5

M6

M7

M8

2.00

4.50

2.30

2.00

2.50

0.50

0.50

3.30

2.15

1.70

4.50

2.90

3.00

D1 0.238

8.50

6.10

3.10

D2 0.054

1.70

6.90

0.90

D3 0.127

3.10

3.50

8.50

3.90

D4 0.151

6.10

2.60

2.90

7.50

4.50

5.50

5.70

5.70

D5 0.061

3.00

1.70

4.50

6.50

2.30

7.50

4.50

1.80

D6 0.028

0.45

1.80

3.90

0.50

1.70

3.50

0.90

3.20

I1 0.140

1.35

0.90

8.50

4.90

0.60

6.50

I2 0.023

0.75

1.20

0.75

3.70

4.50

I3 0.040

1.00

3.50

8.50

5.30

4.10

3.90

I4 0.064

2.00

1.20

4.70

5.10

4.50

8.50

4.30

6.10

0.90

3.60

2.70

4.50

1.20

5.50

8.10

I5 0.074 FMEA coefficient

Relationships between WHATs and HOWs

5.30

0.758 0.967 0.712 0.721 0.891 0.779 0.903 0.993

Fig. 2. Integrated results of HOQ for risk management.

163

HOWs Risk control measures

Fulfillment level of HOWs

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by summing up all the satisfaction of all single customer requirements (Chen et al., 2017). Similarly, in our proposed QFD-based risk management framework for hazmats road transportation, the identified risk factors are regarded as WHATs, and the derived risk measures are regarded as the HOWs. Therefore, the total risk control effectiveness can be evaluated by summing up the control effectiveness of all single risk factors. We also proposed an extended F-FMEA to determine a risk adjustment coefficient, f j , to adjust further the fulfillment level of each risk measure. Specifically, we define the fulfillment level of each risk measure, denoted as x j , as the explanatory variable and the effectiveness level of each risk factor, denoted as yi , as the response variable. Based on the relationship between risk factors and risk measures, the correlation among risk measures, and the risk adjustment coefficient of each risk measure, we can compute the risk control effectiveness of each risk factor as follows: m

yi =

m

m

Rij f j x j + j=1

rjh f j xj fh xh

(11)

j = 1 h = 1, h j

where Rij is the relationship between the i - th risk factor and the j - th risk measure, f j is the risk adjustment coefficient of the j - th risk measure, which is obtained from F-FMEA method, x j is the fulfillment level of the j - th risk measure, and r jh is the correlation between the j - th risk measure and the h - th risk measure. The total risk control effectiveness can be further obtained by multiplying the identified importance rating of risk factors wi and single risk control effectiveness yi , as follows: n

Z=

n

wi yi =

m

m

wi

i=1

i=1

m

Rij f j xj + j=1

r jh f j xj fh xh

(12)

j = 1 h = 1, h j

Finally, a nonlinear goal-programming model can be formulated as follows based on the goal of maximizing the total risk control effectiveness. n

max

m

wi

m

m

Rij f j x j +

i=1

j=1

rjh f j xj fh xh

(13)

j = 1 h = 1, h j

s.t.

0 0

j

yi xj

1, j

i 1,

j

(14)

where the first constraint represents the risk control effectiveness of each single risk factor cannot exceed the unit and the second constraint represents the fulfillment level of each risk measure is between a lower limit, j , which is equal to or bigger than zero, and a upper limit, j , which is equal to or smaller than one. The specific value of j and j are determined according to the application conditions. The obtained fulfillment level of risk measure, x j , is regarded as the reference of limited resources allocation for maximizing the risk control effectiveness. For the present study, based on the previously obtained information shown in Fig. 2, we can get the following nonlinear goal programming model to determine the optimized fulfillment level of each risk measure. 11

Z = max i = 1 wi yi = 3.084x1 + 3.157x2 + 2.477x3 + 2.544x 4 + 3.415x5 + 3.41x 6 + 2.985x 7 + 3.704x8+ 2.514x1 x2 + 1.349x1 x3 + 2.423x1 x 4 + 0.486x1 x5 + 2.185x1 x 6 + 0.702x1 x 7 + 1.603x1 x8+ 1.922x2 x3 + 2.125x2 x 4 + 0.515x2 x5 + 1.442x2 x 6 + 3.273x2 x 7 + 0.521x2 x8 + 1.448x3 x 4+ 0.548x3 x5 + 3.383x3 x 6 + 1.696x3 x 7 + 2.291x3 x8 + 3.348x 4 x 6 + 1.886x 4 x 7 + 1.258x 4 x8+ 4.332x5 x 6 + 0.905x5 x 7 + 2.274x5 x 8 + 2.818x 6 x 7 + 1.862x 6 x8 s.t.

0 yi 10, i = 1, 2, , 10 0.15 xj 1, j = 1, 5, 8 0.10 xj 1, j = 2, 3, 4, 7 0.25 xj 1, j = 6 where the specific value of yi (i = 1, 2, , 10) can be derived from the obtained relationship matrix Rij between risk factors and risk measures, the correlation matrix r jh among risk measures, and the risk adjustment coefficient f j of each risk measure . The upper limit of yi in this model is 10 because of value interval of our TpFN is [0, 10]. If the value interval is [0, 1], then 10 in the constraints, should be changed into 1. In addition, the lower limit of x j is determined by the application conditions. In this study, the limit is determined Table 9 Outcomes of total risk control effectiveness and fulfillment level of each risk measure. Z

x1

x2

x3

x4

x5

x6

x7

x8

0.872

0.150

0.468

0.422

0.100

0.245

0.284

0.100

0.150

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Y.-L. Li, et al. Phase 1 Risk identification

Construct the global-view of QFDbased risk management framework

Phase 2 Risk assessment

Phase 3 Risk control

Determine the importance rating of each risk factor using the proposed F-AHP method

Derive the risk measures based on the identified risk factors

Each expert provides pairwise comparison results in the form of fuzzy linguistics

Compute the basic importance rating of risk measures without considering the correlation among risk measures

Each expert provides the relationship evaluation results between risk factors and risk measures using fuzzy linguistics

Construct the single fuzzy judgment matrix by transforming the fuzzy linguistics into corresponding TpFNs Establish a cross-functional expert team and determine the weight of each expert

Construct the single relationship evaluation matrix by transforming the linguistic variables into corresponding TpFNs

Check the consistency of each single fuzzy judgment matrix

Identify the risk factors and formulate a hierarchy structure

Calculate the importance rating of risk factor provided by single expert

Obtain the group matrix of relationship evaluation by integrating all single matrices into one by considering the weight of each expert

Calculate the integrated importance rating of risk factor by taking into account the weight of each expert

Compute the basic importance rating of risk measures based on the integration of importance rating of risk factors and group relationship matrix between risk factors and risk measures

Evaluate the correlation among risk measures using the similar method that obtaining group evaluation matrix of relationship between risk factors and risk control measures

Compute the RPN and risk adjustment coefficient of each risk measure based on F-FMEA Identify the potential failure modes, effects and causes of risk measures Each expert provides the evaluation results on the severity, occurrence probability, and detection probability of each failure mode using fuzzy linguistics Construct the single fuzzy judgment matrix by transforming the fuzzy linguistics into corresponding TpFNs Obtain the group FMEA matrix by integrating all single matrices into one by considering the weight of each expert Compute the RPN and then determine the risk adjustment coefficient Determine the optimized fulfillment level of each risk measure using nonlinear goal programming

Fig. 3. QFD-based three-phase risk management framework.

by the basic importance rating of risk measures and the requirements of the company. From the results of step 6, the risk measures can be grouped into three: x 6 is in the first group with the highest importance, x1, x5 , and x 8 are in the second group with middle importance between 0.12 and 0.15, and the rest of risk measures are in the third group. The above-formulated nonlinear goal programming model can be solved easily using of LINGO or MATLAB software. The outcome of the fulfillment level of each risk measure is shown in Table 9 and is positioned on the contents of the last row of Fig. 2. From an overall view, the QFD-based three-phase framework for risk management of hazmats road transportation is shown in Fig. 3.

Fig. 4. Results of the case study. 165

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4. Results analysis and discussion Using the results obtained from the above case, we discuss the following various aspects of risk management of hazmats road transportation. First, the risk factors with the corresponding importance rating were identified. Taking the advantages of QFD, the related risk factors that with a hierarchy structured were derived as the inputs of HOQ. Using the proposed F-AHP method, the results of the importance rating and priority of the risk factors were shown in Fig. 4 (a). The factors that have primary influence on hazmats transportation risk are professional skills (D1), transportation equipment (D4), and road conditions (I1), whose total weight is more than 50%. The most influential risk factor is professional skills. That, the inappropriate operation of humans is the major cause of hazmats transportation accidents. This conclusion is consistent with the previous research by Yang et al. (2010), Zhao et al. (2012), and Ambituuni et al. (2015), which also verify the effectiveness of our proposed method. Based on the findings, regulatory efforts by related management authorities and company departments should focus more attention on improving operational skills and limiting human errors. In addition, it can be seen that the risk factors nature of the hazmats (D6) and weather conditions (I2) have relatively low importance rating, which means that the impact on risk accidents is relatively weak. This phenomenon can be reasonably explained because these two factors are inherent and not easily affected by human actions. Properly understanding the characteristics of hazmats and choosing good weather for transportation can efficiently decrease such risks. Second, several risk measures used to control the hazmats road transportation risk were identified. The HOQ technique, which is the core tool of QFD, was introduced to derive the risk measures from the identified risk factors. Then, the basic importance rating of each risk measure was computed based on the obtained weights of risk factors and the relationship between risk factors and risk measures. With reference to the results shown in Fig. 4 (b), emergency plan formulation and drill (M6) has the highest weight in all risk measures, which is reasonable because the emergency plan relates nearly to all the risk factors, which resulted in high priority. When developing the emergency plan, the previously mentioned file “management regulation of hazmats road transportation” can provide effective references because it almost defines various related aspects of hazmats road transportation. Simultaneously, focus should also be given to skills training (M1), transportation route planning and scheduling (M5), and real-time monitoring and interaction (M8), which also have relatively high importance rating because of the direct relation to risk factors with high weights. Particularly speaking, for enhancing the skills training (M1), the basic knowledge and operation regulations of the hazmats as well as the operation method of equipment should be introduced to the practitioners. Practitioners should pass the qualification examination required by related regulations before they go to work. In addition, more pre-practices and appraisals should be conducted before and during the hazmats transportation process. For route planning and scheduling (M5), methods considering multiple objectives like risk, cost, time under different constraints need to be proposed. As Bianco et al. (2009) stated that consideration of the national and regional requirements and influences on risk equity and constraints are important in the route planning and scheduling methods development process. Moreover, it is suggested to develop more automatic and easy to use software for applying the proposed methods. In addition to the influences on route planning and scheduling, the national and regional requirements will greatly affect the market order and supervision of hazmats road transportation as well as the enterprises’ operation behaviors. All the mentioned factors are directly related to transportation risks. For real-time monitoring and interaction (M8), obtaining and transmitting the accurate information is important. Therefore, it is necessary to equip satellite-positioning device with driving record function with each vehicle and to guarantee the monitoring and interaction equipment keep worked even if under extreme conditions. The realtime monitoring involves two aspects. On the one hand, to correct the error operations of practitioners at the right time; on the other hand, to send the alert or solutions to practitioners when an emergency occurred. Overall, the management requirements and regulations of the nation and region both have direct influences on hazmats road transportation. Strict regulations will largely eliminate the potential risk factors and decrease the consequences of hazmats transportation accidents. Third, although the risk measures were identified and prioritized, the practical effects may not be so ideal. Therefore, FMEA is necessary to evaluate the risk control measures. Using the FMEA method is helpful for management authorities to understanding the risk control measures in-depth because the potential failure modes, effects and causes corresponding to risk control measures were identified. Moreover, the F-FMEA was proposed to address the uncertainty and vagueness during the decision process. Our research findings indicated three potential failure modes: low level of safety awareness (F3), equipment failure (F4), and low level of professional skill (F1) have relatively high RPN, which means there may be more uncertainty when carrying out these risk measures. Since the potential causes and effects corresponding to each potential failure mode have been derived, specific measures can be taken to decrease the occurrence possibility of failures. More resources should be also allocated to the risk measures with high RPN for easing the failures and ultimately controlling the risks. Furthermore, in this study, we provided a method to compute the risk adjustment coefficient based on the obtained RPN, which subsequently will be used to adjust the resources allocation in the later optimization model. Additionally, Fig. 4 (c) shows that larger the RPN, the smaller the risk adjustment coefficient, and vice versa. The results are reasonable because the smaller coefficient in the model requests more resources to corresponding risk measures with high RPN. Finally, optimizing the resources allocation to maximize the effectiveness of risk control with less input is necessary. In this study, we formulated a nonlinear goal programming model to determine the fulfillment level of each risk control measure. We also introduced a risk adjustment coefficient that derives from F-FMEA to modify the model to enhance further the effectiveness of risk control measures. The outcomes of risk control effectiveness and fulfillment level of each risk measure were shown in Table 9 and Fig. 4 (d). The optimized total effectiveness of risk control measures is 87.2%. To verify the necessity of considering the potential failure of risk measures, we also analyzed the optimized effectiveness and fulfillment level of each risk measure without concerning FMEA. The results were displayed in Fig. 4 (d). Fig. 4 (d) shows that the required fulfillment level that did not include FMEA is lower 166

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than the situation included FMEA. Furthermore, based on the solution of goal model that without FMEA, the total optimized effectiveness is 85.2%, which is lower than 87.2%. The result can be explained reasonably in terms of risk control. When considering the potential failures of risk measures, more resources are needed to control the potential risk, which requires higher fulfillment level of each risk measures. More importantly, the total optimized effectiveness of concerning FMEA is better than it without concerning FMEA. It is worth noting that the optimized effectiveness and required fulfillment level of each risk measure may be influenced by the limits of risk measures, which are changeable and determined based on the practical applications. Therefore, the proposed nonlinear goal programming model shows an advantage in terms of flexibility, which means it can be adjusted according to the real-time requirements of the company. In summary, the contributions of this study are as follows. (i) Compared with the previous works on risk management of hazmats road transportation, this study proposed a systematic framework that has three main components (i.e., risk identification, risk assessment, and risk control) of risk management and a semiquantitative decision support model based on the combination of QFD, F-AHP, F-FMEA, and nonlinear goal programming. The proposed systematic decision support model can be used by both related government management authorities and company departments to mitigate the risk during hazmats transportation and optimize the limited resources allocation. (ii) To address the inherent vagueness and uncertainty during the risk management, fuzzy set theory was introduced in the proposed methodology. The utilization of fuzzy linguistic variables and TpFNs not only facilitates the expression and computation of evaluation information but also avoids the information loss as much as possible. Moreover, the proposed semi-quantitative method that uses experts’ experience and knowledge fully is especially effective under a situation where input data are inadequate or incomplete. In addition, the proposed method is easy to understand because there are no complicated models and algorithms and the computation can be accomplished automatically by the software EXCEL and LINGO or MATLAB. Thus, the mentioned characteristics enhanced the availability and feasibility of the proposed methodology. (iii) In the proposed systematic framework, F-AHP based on fuzzy linguistic variables and TpFNs was used to evaluate the importance rating of risk factors. Compared with the traditional AHP method, the proposed F-AHP is superior in dealing with the fuzziness and uncertainty. At the same time, using TpFN is more likely to obtain accurate results because the information loss in the data transformation process is decreased as much as possible compared with triangular fuzzy numbers and crisp numbers. In addition, using the F-AHP method is beneficial in guaranteeing the consistency of evaluation compared with the directly scoring method. (iv) A nonlinear goal programming model was constructed to determine the fulfillment level of each risk measure to optimize the total risk control effectiveness. Particularly, F-FMEA method was proposed to analyze the potential failure of risk measures and determine a risk adjustment coefficient to modify the fulfillment level of each risk measure, which makes the results more reasonable. The outcomes of the model can provide useful suggestions on resources allocation to management authorities. (v) In terms of managerial implications, some practical suggestions can be provided based on the obtained results. First, more attention should be given to improving the professional skills and safety awareness of practitioners because the human operation errors were demonstrated as the main factor that influences the transportation risk. Second, formulating a comprehensive emergency plan, which is almost related to every aspect of transportation risk and improving the drill of an emergency plan to control the transportation risk effectively are necessary. Third, among all the risk measures, safety awareness education and equipment supervision and maintenance are the two most likely to fail. Thus, management of practitioners and equipment should be enhanced to decrease the possibility of failure of the risk measures. Finally, the comparative analysis of total risk control effectiveness between situations with FMEA and without FMEA illustrates that FMEA evaluation is necessary to the optimization of the total risk control effectiveness. 5. Conclusions Risk management is a primary and critical task of the managers in hazmats transportation company or government management department. General risk management entails understanding the risk factors that contribute to risks first, assessing the risk that will happen to each risk factor, and finally providing corresponding risk control measures and allocating resources to deal with the risks. Therefore, in this study, a systematic risk management framework, which is based on the combination of QFD, F-AHP, F-FMEA, and nonlinear goal programming, for hazmats road transportation was proposed under complicated situations in which the risk evaluation information is insufficient or incomplete. The QFD is used to construct the overall structure, which contains the risk factors and corresponding importance rating, risk control measures, the relationship between risk factors and risk measures, and the correlation among risk measures, risk adjustment coefficient, and fulfillment level of each risk measure. The fuzzy set theory is introduced to address the vagueness and uncertainty in the process of risk management. The F-AHP method is proposed to determine the weight of each risk factors. The F-FMEA presents the evaluation of the potential failure of the risk measures and determines the risk adjustment coefficient, which is subsequently used to modify the fulfillment level of each risk measure in nonlinear goal programming model for maximizing the total risk control effectiveness. Finally, an empirical case is used to illustrate how the proposed methodology works effectively and feasibly. The proposed framework also can be applied to other industry for general risk management through simple modification. Although the proposed framework exhibits superiority in systematic risk management of hazmats road transportation, some limitations could be studied in the future. First, the interrelationship among risk factors has not been considered in the study. For example, various dangerous properties have different requirements for the transportation and protective equipment. Therefore, the nature of the hazmats (D6) may be interrelated with transportation equipment (D4) and protective equipment (D5). The weather conditions (I2) also has influence on the nature of the hazmats (D6). Similarly, the safety awareness (D3) and Equipment supervision 167

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(I3) may be affected by Law and system management (I5) (Clark and Besterfield-Sacre, 2009; Tong et al., 2006; Zhao et al., 2012). To deal with the problem, the analytic network process (ANP) method may be an effective way to address this situation. Second, the cost has not been included in the proposed optimization model. Therefore, in future studies, the bi-objective optimization model that considers risk and cost can be formulated simultaneously. Third, the input data of the proposed semi-quantitative methodology are based mainly on the knowledge and experience of experts and an interesting research problem could be how reliable input data can be collected and subjectivity decreased. Some potential suggestions are to take the hesitance and psychological of experts into consideration by using hesitant fuzzy linguistic approach (Chen et al., 2018). Last but not least, the results of the case study are mainly based on a panel of four experts. Although the proposed framework has been demonstrated effective and the objective of the study has been satisfied, it is strongly suggested that more experts should be involved to derive more comprehensive results for further applications. Acknowledgement This study was supported by the National Natural Science Foundation of China (No. 71371156, 71872153, 71831006), and the Cultivation Program for the Excellent Doctoral Dissertation of Southwest Jiaotong University (D-YB201905). The authors wish to thank the Editor-in-Chief and the anonymous reviewers for their valuable comments and suggestions. Appendix A See Table A1. Table A1 Individual pairwise comparison matrix for determining weights of risk factors. Expert e1 R

D

ID

D

D1

D2

D3

D4

D5

D6

ID

I1

I2

I3

I4

I5

D

EI

[EI, MI]

D1 D2 D3 D4 D5 D6

EI

SI EI [EI, MI] MI

MI

[EI, MI]

I1 I2 I3 I4 I5

SI EI

[EI, MI]

[EI, MI] MI

MI EI EI [EI, MI] [EI, MI]

[EI, MI]

[EI, MI] EI

SI [EI, MI] SI SI [EI, MI] EI

EI

EI

[MI, SI] [EI, MI] MI [MI, SI] EI

EI

EI EI

ID

EI

Expert e2 R

D

ID

D

D1

D2

D3

D4

D5

D6

ID

I1

I2

I3

I4

I5

D

EI

MI

D1 D2 D3 D4 D5 D6

EI

[SI, VSI] EI [EI, MI] MI

[MI, SI]

MI

I1 I2 I3 I4 I5

[MI, SI] EI [EI, MI] [MI, SI] MI

MI

[EI, MI]

MI

[EI, MI] EI

VSI [EI, MI] SI SI [EI, MI] EI

EI

EI

SI EI MI MI EI

ID

EI

EI [EI, MI]

EI

EI [EI, MI]

EI

Expert e3 R

D

ID

D

D1

D2

D3

D4

D5

D6

ID

I1

I2

I3

I4

I5

D

EI

[EI, MI]

D1 D2 D3 D4 D5 D6

EI

[SI, VSI] EI MI MI [EI, MI]

MI

EI

SI

[EI, MI]

[MI, SI]

[EI, MI] MI EI

SI EI [EI, MI] SI MI

[MI, SI]

EI

I1 I2 I3 I4 I5

EI

EI [EI, MI]

[VSI, AI] MI [MI, SI] [SI, VSI] MI EI

EI [EI, MI]

EI EI [EI, MI]

EI

ID

EI

Expert e4 R

D

ID

D

D1

D2

D3

D4

D5

D6

ID

I1

I2

I3

I4

I5

D

EI

[EI, MI]

D1 D2 D3 D4 D5 D6

EI

[MI, SI] EI MI MI [EI, MI]

[EI, MI]

EI

MI

[EI, MI]

MI

MI [EI, MI] EI

SI EI [EI, MI] SI MI

[MI, SI]

EI

I1 I2 I3 I4 I5

EI

EI [EI, MI]

SI [MI, SI] SI MI [EI, MI] EI

EI [EI, MI] MI

EI MI

EI

ID

EI

168

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Appendix B See Tables B1 and B2. Table B1 Individual relationship evaluation matrix between risk factors and risk measures. Rij

Expert e1

Expert e2

M1

M2

M3

M4

M5

M6

M7

M8

M1

M2

M3

M4

M5

M6

M7

M8

D1 D2 D3 D4 D5 D6 I1 I2 I3 I4 I5

VS – W M M – – – W VW –

S VS M M W – – – – – S

W – VS VW M M – – M W VW

– – M VS VS W – – VS M W

M W – M W – VS W – M –

VW W M M S M S M M VS M

VW M W W M – VW – W S VS

W W – M VW – S VS M S –

VS W W S M – – – W W –

M S W W VW – – – – VW M

W – VS W M M – – W M VW

W – VW S M – – – VS M S

M – W M VW W VS W – M –

W – M S VS W M M W VS S

W M VW S M – VW – M M S

W M M M VW W S M M M –

Rij

Expert e3

D1 D2 D3 D4 D5 D6 I1 I2 I3 I4 I5

Expert e4

M1

M2

M3

M4

M5

M6

M7

M8

M1

M2

M3

M4

M5

M6

M7

M8

VS W M S W – – – – VW VW

S S W VW VW VW – – – VW M

M VW VS W M W VW W M S M

W – M S S – – – VS M M

M – W M W VW VS M – M VW

W – M S VS M M M S VS S

VW W M S M VW – – M M VS

W W M S VW M S M M S –

VS VW W S W VW M W – W VW

S S M W VW M – – – VW M

W VW VS W M M VW VW W M W

W – M VS S – – W VS S M

M – VW M W W VS M – M W

W – M M S W M M S VS M

W W W S M VW – – M W VS

W – W S W M S M W S –

169

M1

(7, 8, 9, 10) (0.5, 1.3, 2.1, 2.9) (4.6, 5.6, 6.6, 7.6) (1.6, 2.6, 3.6, 4.6) (1.5, 2.5, 3.5, 4.5) (0, 0.3, 0.6, 0.9) (0.9, 1.2, 1.5, 1.8) (0.3, 0.6, 0.9, 1.2) (0.4, 0.8, 1.2, 1.6) (0.5, 1.5, 2.5, 3.5) (0, 0.6, 1.2, 1.8)

Rij

D1 D2 D3 D4 D5 D6 I1 I2 I3 I4 I5

(4.6, 5.6, 6.6, 7.6) (5.4, 6.4, 7.4, 8.4) (2, 3, 4, 5) (1.1, 2.1, 3.1, 4.1) (0.2, 1.2, 2.2, 3.2) (0.9, 1.5, 2.1, 2.7) – – – (0, 0.8, 1.6, 2.4) (2.4, 3.2, 4, 4.8)

M2 (1.6, 2.6, 3.6, 4.6) (0, 0.6, 1.2, 1.8) (7, 8, 9, 10) (1.4, 2.4, 3.4, 4.4) (3, 4, 5, 6) (2.4, 3.4, 4.4, 5.4) (0, 0.6, 1.2, 1.8) (0.3, 0.9, 1.5, 2.1) (2, 3, 4, 5) (3.2, 4.2, 5.2, 6.2) (1.2, 2.2, 3.2, 4.2)

M3 (0.8, 1.6, 2.4, – (2.4, 3.4, 4.4, (6, 7, 8, 9) (5, 6, 7, 8) (0.2, 0.4, 0.6, – (0.3, 0.6, 0.9, (7, 8, 9, 10) (3.6, 4.6, 5.6, (3, 4, 5, 6)

M4

Table B2 TpFN-valued group matrix of relationship evaluation between risk factors and risk measures.

170 6.6)

1.2)

0.8)

5.4)

3.2)

(3, 4, 5, 6) (0.2, 0.4, 0.6, (0.5, 1.3, 2.1, (3, 4, 5, 6) (0.8, 1.8, 2.8, (0.5, 1.3, 2.1, (7, 8, 9, 10) (2.2, 3.2, 4.2, – (3, 4, 5, 6) (0.3, 0.9, 1.5,

M5

2.1)

5.2)

3.8) 2.9)

0.8) 2.9)

(0.8, 1.8, 2.8, (0.2, 0.4, 0.6, (3, 4, 5, 6) (4, 5, 6, 7) (6, 7, 8, 9) (2, 3, 4, 5) (3.4, 4.4, 5.4, (3, 4, 5, 6) (3.8, 4.8, 5.8, (7, 8, 9, 10) (5, 6, 7, 8)

M6

6.8)

6.4)

3.8) 0.8)

(0.5, 1.5, 2.5, 3.5) (1.8, 2.8, 3.8, 4.8) (1.4, 2.4, 3.4, 4.4) (4.2, 5.2, 6.2, 7.2) (3, 4, 5, 6) (0, 0.6, 1.2, 1.8) (0, 0.4, 0.8, 1.2) – (2.6, 3.6, 4.6, 5.6) (2.8, 3.8, 4.8, 5.8) (6.6, 7.6, 8.6, 9.6)

M7

(1, 2, 3, 4) (1.1, 1.8, 2.5, 3.2) (1.8, 2.6, 3.4, 4.2) (4.2, 5.2, 6.2, 7.2) (0.3, 1.3, 2.3, 3.3) (2, 2.8, 3.6, 4.4) (5, 6, 7, 8) (3.8, 4.8, 5.8, 6.8) (2.4, 3.4, 4.4, 5.4) (4.6, 5.6, 6.6, 7.6) –

M8

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Appendix C See Tables C1 and C2.

Table C1 Individual correlation evaluation matrix among risk measures. r jh

Expert e1 M1

M1 M2 M3 M4 M5 M6 M7 M8

r jh

Expert e2 M2

M3

M4

M5

M6

M7

M8

M

W M

S W W

VW – – –

W M M VW S

– W VW W – W

VW VW W – M M –

Expert e3 M1

M1 M2 M3 M4 M5 M6 M7 M8

M1

M2

M3

M4

M5

M6

M7

M8

S

W W

M M M

– – – –

W – S M S

VW M VW W VW M

W – M M W W –

M2

M3

M4

M5

M6

M7

M8

W

W W

M M M

VW – VW –

M W S W S

W S W M VW M

W VW M W W W –

Expert e4 M2

M3

M4

M5

M6

M7

M8

M

W M

S M W

– W VW –

M W S M S

– M M M VW M

W – W W W VW –

M1

Table C2 TpFN-valued group matrix of correlation evaluation among risk measures. r jh M1 M2 M3 M4 M5 M6 M7 M8

M1

M2

M3

M4

M5

M6

M7

M8

(2.8, 3.8, 4.8, 5.8)

(1, 2, 3, 4) (2, 3, 4, 5)

(4, 5, 6, 7) (2.6, 3.6, 4.6, 5.6) (2, 3, 4, 5)

(0, 0.5, 1, 1.5) (0.3, 0.6, 0.9, 1.2) (0, 0.6, 1.2, 1.8) –

(2.2, 3.2, 4.2, 5.2) (1.2, 2, 2.8, 3.6) (4.6, 5.6, 6.6, 7.6) (1.8, 2.8, 3.8, 4.8) (5, 6, 7, 8)

(0.3, 0.8, 1.3, 1.8) (3.2, 4.2, 5.2, 6.2) (1.2, 2.2, 3.2, 4.2) (2.2, 3.2, 4.2, 5.2) (0, 0.8, 1.6, 2.4) (2.6, 3.6, 4.6, 5.6)

(0.8, 1.8, 2.8, 3.8) (0, 0.5, 1, 1.5) (2, 3, 4, 5) (1.2, 2, 2.8, 3.6) (1.4, 2.4, 3.4, 4.4) (1.1, 2.1, 3.1, 4.1) –

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Appendix D See Tables D1 and D2.

Table D1 Individual FMEA evaluation matrix of risk measures. FM

Expert e1

Expert e2

Expert e3

Expert e4

S

O

D

S

O

D

S

O

D

S

O

D

F1

H

M

F4

VH

F5

H

F6

H

F7

M

F8

M

M VD E D E VD D D E D E VE D E D M VE VD M M

H M H L M M H M M L L VL L VL H M VL H L VL

M D VE D E D VD D E D E VE VD E D E E M M M

H

F3

H M H M M M H L M L L VL VL L M H VL M M VL

VH

L

M D VE VD E D VD D E D M VE VD E D M E D D M

H

F2

M L H M M L H M L L M VL VL L M H L M L VL

H M H L M L H L VL M M L VL VL H M VL H M VL

E VD VE VD E VD VD D VE VD D VE VD E VD E M D M D

L M VH H M M L

L H VH M M M L

M M VH H H M M

Table D2 TpFN-valued integrated group matrix of FMEA evaluation. Failure mode

Severity (S)

Occurrence (O)

Detection (D)

F1

(5.6, 6.6, 7.6, 8.6)

F2

(1.6, 2.6, 3.6, 4.6)

F3

(3.6, 4.6, 5.6, 6.6)

F4

(7, 8, 9, 10)

F5

(4.4, 5.4, 6.4, 7.4)

F6

(4, 5, 6, 7)

F7

(3, 4, 5, 6)

F8

(2, 3, 4, 5)

(4.6, 5.6, 6.6, (2.6, 3.6, 4.6, (5, 6, 7, 8) (1.8, 2.8, 3.8, (3, 4, 5, 6) (2, 3, 4, 5) (5, 6, 7, 8) (2, 3, 4, 5) (1.7, 2.7, 3.7, (1.6, 2.6, 3.6, (2, 3, 4, 5) (0.3, 1.3, 2.3, (0.3, 1.3, 2.3, (0.4, 1.4, 2.4, (4.2, 5.2, 6.2, (3.8, 4.8, 5.8, (0.2, 1.2, 2.2, (4.2, 5.2, 6.2, (2, 3, 4, 5) (0, 1, 2, 3)

(2.4, 3.4, 4.4, (6, 7, 8, 9) (0.2, 1.2, 2.2, (6, 7, 8, 9) (1, 2, 3, 4) (6, 7, 8, 9) (6.6, 7.6, 8.6, (5, 6, 7, 8) (0.7, 1.7, 2.7, (5.6, 6.6, 7.6, (2.6, 3.6, 4.6, (0, 1, 2, 3) (6.6, 7.6, 8.6, (1, 2, 3, 4) (5.6, 6.6, 7.6, (1.8, 2.8, 3.8, (1.4, 2.4, 3.4, (4.8, 5.8, 6.8, (3.4, 4.4, 5.4, (3.6, 4.6, 5.6,

7.6) 5.6) 4.8)

4.7) 4.7) 3.3) 3.3) 3.4) 7.2) 6.8) 3.2) 7.2)

5.4) 3.2)

9.6) 3.7) 8.6) 5.6) 9.6) 8.6) 4.8) 4.4) 7.8) 6.4) 6.6)

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