A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis

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Contents lists available at ScienceDirect

Journal of Safety Research journal homepage: www.elsevier.com/locate/jsr 5 6

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A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis

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Rahim Dabbagh ⇑, Samuel Yousefi

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Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran

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10 9 1 2 2 6 13 14 15 16 17 18 19 20 21 22 23 24 25

a r t i c l e

i n f o

Article history: Received 7 March 2019 Received in revised form 25 June 2019 Accepted 27 September 2019 Available online xxxx Keywords: Risk assessment Occupational health and safety Failure mode and effect analysis Fuzzy cognitive map Multi-objective optimization on the basis of ratio analysis

a b s t r a c t Introduction: With the development of industries and increased diversity of their associated hazards, the importance of identifying these hazards and controlling the Occupational Health and Safety (OHS) risks has also dramatically augmented. Currently, there is a serious need for a risk management system to identify and prioritize risks with the aim of providing corrective/preventive measures to minimize the negative consequences of OHS risks. In fact, this system can help the protection of employees’ health and reduction of organizational costs. Method: The present study proposes a hybrid decision-making approach based on the Failure Mode and Effect Analysis (FMEA), Fuzzy Cognitive Map (FCM), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) for assessing and prioritizing OHS risks. After identifying the risks and determining the values of the risk assessment criteria via the FMEA technique, the attempt is made to determine the weights of criteria based on their causal relationships through FCM and the hybrid learning algorithm. Then, the risk prioritization is carried out using the MOORA method based on the decision matrix (the output of the FMEA) and the weights of the criteria (the output of the FCM). Results: The results from the implementation of the proposed approach in a manufacturing company reveal that the score at issue can overcome some of the drawbacks of the traditional Risk Priority Number (RPN) in the conventional FMEA, including lack of assignment the different relative importance to the assessment criteria, inability to take into account other important management criteria, lack of consideration of causal relationships among criteria, and high dependence of the prioritization on the experts’ opinions, which finally provides a full and distinct risk prioritization. Ó 2019 National Safety Council and Elsevier Ltd. All rights reserved.

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1. Introduction

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Despite the increasing growth of technology and industrial development, which have led to increased productivity and economic prosperity, new challenges have emerged in the areas of health, safety, and environment. Therefore, various accidents are likely to happen in the workplace that may cause workers’ fatalities, injuries, or illnesses; financial losses; production loss; and even the reduced credibility of the organization (MoatariKazerouni, Chinniah, & Agard, 2015). Such accidents are typically due to the lack of identification of the existing potential risks. According to the International Labor Organization (ILO) report, there are approximately 317 million occupational accidents worldwide and more than 2.3 million victims of such consequences annually (ILO, 2017). Also, the statistical report provided by the

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⇑ Corresponding author. E-mail addresses: [email protected] (R. Dabbagh), [email protected] (S. Yousefi).

Iranian Legal Medicine Organization reveals that during the 2008–2017 period, 15,997 people died due to work-related accidents (ILMO, 2018). These statistics indicate that despite the widespread development in a broad range of industries, lack of attention to the management of Occupational Health and Safety (OHS) and identification of the potential workplace risks may cause irremediable losses. Indeed, the occurrence of accidents at the workplace and the injuries caused by them have a significant economic and social burden (Smith et al., 2015). In this regard, OHS management as an organized system in high-risk industries can lead to workforce health protection and create a coherent process to make an economically persistent improvement. The continuity of the production and provision of services as well as the avoidance of extortionate costs and different compensation required to be paid for losses are other benefits of this system (Yousefi, Alizadeh, Hayati, & Baghery, 2018). Identifying and assessing workplace risks are considered the essential parts of the OHS management because as the very bases for planning and providing control solutions, they can prevent a plethora of accidents.

https://doi.org/10.1016/j.jsr.2019.09.021 0022-4375/Ó 2019 National Safety Council and Elsevier Ltd. All rights reserved.

Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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There are various methods for risk assessment, but an efficient risk assessment method should be tailored to the nature of the activities, processes, and other characteristics of the organization. One of the most widely used methods in the field of safety is Failure Mode and Effect Analysis (FMEA). The FMEA intends to plan and implement actions to eliminate or reduce the occurrence of risks by predicting them and determining the probability of their occurrence. In most studies using the FMEA technique, the traditional Risk Priority Number (RPN) score was applied for risk prioritization. However, this score has some shortcomings that need to be taken into account to achieve reliable results (Liu, Liu, & Liu, 2013). Some of these drawbacks include mere focus of improvement efforts upon a risk with higher RPN, which may have lower severity compared with other risks having lower RPN (Baghery, Yousefi, & Rezaee, 2018); lack of assignment the different relative importance to criteria (Zhang & Chu, 2011); the inability to distinguish failure modes with similar RPNs but different severities or lack of a distinct or full prioritization (Ghoushchi, Yousefi, & Khazaeili, 2019; Rezaee, Yousefi, Valipour, & Dehdar, 2018); questionable mathematical formula for calculating RPN (Liu, Liu, Liu, & Mao, 2012); lack of consideration of the costs resulting from any risk occurrence (Rezaee, Salimi, & Yousefi, 2017a); and lack of consideration of other important criteria in risk assessment (Yousefi et al., 2018). Many researchers have offered hybrid approaches based on Multi-Criteria Decision-Making (MCDM) methods due to of their flexibility on the judgment of the decision-maker(s) to overcome some of the mentioned shortcomings of traditional RPN (Gul, 2018; Liu et al., 2013). In the following, a review of some investigations carried out in this area is presented. Liu and Tsai (2012) offered a fuzzy approach to identify and assess the most crucial risks and detect their causes with the aim of improving or preventing the occupational hazards in the construction industry. They implemented the proposed approach in a telecom engineering company located in Southern Taiwan. In this approach, they used the fuzzy Analytic Network Process (ANP) method along with Quality Function Deployment (QFD) and FMEA. Silvestri, De Felice, and Petrillo (2012) introduced a new risk prioritization score to improve safety in production systems. The proposed score was based on the improved RPN and ANP and sought to integrate the usual aspects of Failure Modes, Effects, and Criticality Analysis (FMECA) with economic considerations to address risk and minimize the overall safety costs. Kuo, Wu, and Hsu (2012) integrated fuzzy set theory and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the failure risks in healthcare. Bakhtavar and Yousefi (2018) presented a hybrid approach to assess risks associated with workplace accidents in underground coal mines based on the analysis of causal relationships among the risks and system objectives. In this approach, the TOPSIS method was used to analyze the weight sensitivity of the objectives to increase the safety of the work environment. Abbasgholizadeh Rahimi, Jamshidi, Ait-Kadi, and Ruiz (2015) proposed an integrated approach combines fuzzy cost-based service-specific FMEA (FCS-FMEA), Grey Relational Analysis (GRA), and profitability theory to prioritize service failures at a hospital in South Korea. In this approach, the cost is considered an important issue, and the purpose of using profitability theory is to take into account the costs of corrective measures. Liu, Li, Song, and Su (2017) used cloud model theory and PROMETHEE method to analyze the healthcare risks. The approach suggested in this study was introduced as a powerful and flexible decisionmaking method for group behavior management in the FMEA. Mandal et al. (2015) conducted the human error identification and risk prioritization in overhead crane operations using an approach combining Hierarchical Task Analysis (HTA), Systematic Human Error Reduction and Prediction Approach (SHERPA), and fuzzy VIKOR method. This approach helps decision-makers for

optimal allocation of critical safety resources to reduce risk. In an attempt to improve the safety risk management, Ardeshir, Mohajeri, and Amiri (2016) analyzed the safety risks in two different types of large-scale construction projects using a combination of fuzzy logic and FMEA, Fault Tree Analysis (FTA), and Analytical Hierarchy Process-Data Envelopment Analysis (AHP-DEA). Likewise, Bao, Johansson, and Zhang (2017) used the improved AHP model and FMEA to provide an approach for analyzing, controlling, and preventing occupational health hazards and ensuring the reliability of weights in the mineral industries in the Southwestern Hubei Province. Ozdemir, Gul, and Celik (2017) suggested a novel approach incorporating the 5S methodology, FMEA, IntervalType-Two Fuzzy Sets (IT2FSs), AHP, and VIKOR to assess occupational hazards and their associated risks in the chemical laboratory of the observed university. Gul, Ak, and Guneri (2017) offered a two-stage multi-criteria decision-making approach based on Fuzzy AHP and VIKOR methods to assess OHS risks and rank them in different units of a hospital in Turkey. Ilbahar, Karasßan, Cebi, and Kahraman (2018) presented a new approach for assessing the OHS hazards posed by the drilling process. This approach is an integration of FMEA, Pythagorean fuzzy AHP, and fuzzy inference system. Fattahi and Khalilzadeh (2018) carried out a risk assessment in the Kerman Steel Industry Company using a new hybrid approach based on the FMEA, extended MULTIMOORA, and AHP methods in a fuzzy environment. Mete (2018) proposed an extended FMEA based on the AHP-MOORA integrated approach under Pythagorean fuzzy sets. This approach used for assessing OHS risks in a natural gas pipeline construction project. As it is shown, most of the studies sought to overcome some of the shortcomings of the traditional RPN in the conventional FMEA approach. However, it should be highlighted that the approaches suggested in these studies fail to be sufficiently effective because of using MCDM methods. High dependence on personal views and opinions of the experts in MCDM methods, particularly the methods used for weighting the evaluation criteria, can be regarded as a major drawback in the proposed approaches (Gul, 2018). The purpose of this study is to provide a new decision-making approach to overcome most of the shortcomings of the traditional RPN score and MCDM methods in the evaluation criteria weighting process. The proposed approach is implemented in three phases using FMEA, Fuzzy Cognitive Map (FCM), and Multi-Objective Optimization on the basis of ratio analysis (MOORA). After identifying the risks using the FMEA technique in the first phase, the FCM method is used for weighting the assessment criteria in the second phase. The proposed approach outperforms the MCDM methods (e.g., AHP) due to several reasons such as the power to consider the causal relationships among criteria, the ability to solve problems with a large number of criteria and causal relationships, the lack of total dependence of the results on the experts’ opinions, and its intelligence as a result of using the learning algorithm (Papageorgiou, Stylios, & Groumpos, 2004; Rezaee & Yousefi, 2018). In the third phase, the output of the two previous phases is converted to a score so as to prioritize the risks distinctively and fully via the MOORA method. In this method, the risks identified by the FMEA are regarded as the decision-making alternatives, and the criteria scored using FCM, namely severity (S), occurrence (O), detection (D), cost (C), and time (T), are considered the assessment criteria. Some advantages of the MOORA method that turn it into a good option include the power to solve decision-making problems with unknown data, fewer mathematical calculations and less computational time, and more simplicity and stability compared with some MCDM techniques such as AHP, TOPSIS, ELECTRE, VIKOR, and PROMETHEE (Akkaya, Turanog˘lu, & Öztasß, 2015). In general, the score derived from the proposed approach has noteworthy advantages including allocation of different relative importance to assessment criteria based on their causal

Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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relationships, the power to make distinction between risk modes with similar RPNs but different severities (full prioritization), consideration of costs and system stoppage as the result of risk occurrence, and reduced dependence of the prioritization process on the experts’ opinions, which finally overcomes the shortcomings of the traditional RPN score and the approaches integrating MCDM and FMEA. In this regard, the prioritization of OHS risks in one of the main departments of a manufacturing company is carried out by the proposed score to evaluate the presented approach, and then its results are compared with the findings of the traditional methods. The present study is organized as follows. Section 2 is dedicated to the methodology containing further descriptions of FMEA, FCM, and MOORA methods. The proposed approach is introduced in Section 3. The implementation results of the proposed approach are described in Section 4. Finally, Section 5 presents the conclusion and recommended management measures according to studied problem.

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2. Methodology

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In this research, the proposed approach for risk prioritization is an integration of FMEA, FCM, and MOORA methods. In the following, further descriptions about the mentioned methods are presented, and then the proposed approach is introduced.

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2.1. FMEA technique

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FMEA is a systematic, proactive method that initially identifies the deficiencies and potential failures of the system and determines their risk factors. Then, it defines and implements measures to eliminate or minimize the negative consequences of these risks. The FMEA technique reduces the extortionate costs by optimizing the processes and system through preventative actions rather than taking measures after risk occurrence because, in the event of a terrible event, it is often costly to eliminate the failures and negative consequences. In this technique, after defining the risk assessment scope, FMEA team is selected, and the process/function is defined as well. Afterward, the risk modes and effects are identified, and the value of the assessment criteria for each risk is determined by the respective team. All these criteria are ranked from 1 to 10 (10 represents a highly hazardous, definitely likely, and undetectable risk in terms of severity, occurrence, and detection, respectively) (Liu et al., 2013). It should be noted that rating criteria should be conducted in line with the requirements of an organization. In the present study, the risk assessment criteria were ranked according to Table 1. In the next step, the RPN score of each risk is calculated by multiplying the values of its associated criteria, and then risk prioritization is done based on this score. This prioritization is conducted

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in such a way that risks with higher RPNs are considered more critical and given higher priority. Then, corrective/preventive actions are defined and implemented for critical risks. After taking these actions, their impacts on the removal or mitigation of the negative consequences of the risks are assessed through periodic evaluation and, if necessary, further actions are planned to be taken. As mentioned earlier, the risk prioritization in this technique is carried out based on the RPN score; nevertheless, some previous studies thoroughly investigating this score reported some of its shortcomings (Liu et al., 2013). Consequently, eliminating these drawbacks by employing other techniques can provide more realistic and applied results.

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2.2. FCM method

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In the real world, factors have complex relationships with one another, such that they constantly both influence and are influenced by each other. The FCM is one of the methods to show these relationships. The FCM is a cognitive map using the relationships among the components of a mental landscape to compute the strength of causal relationships with fuzzy numbers (i.e., numbers in the interval of [0, 1] or [
1, 1]; Kosko, 1986). According to the previous studies, some of the advantages of FCM over other existing methods include: the ability to model complex systems with limited data compared with the dynamic systems modeling (Özesmi & Özesmi, 2003); the absence of system convergence problem compared with structural equation modeling (Papageorgiou et al., 2004); the ability to model a system when data are inaccessible or data collection is costly (Rezaee, Yousefi, & Babaei, 2017b); the ability to illustrate what is happening in the system considering the causal relationships and the initial state of the system (Rezaee et al., 2017b); the ability to deal with sophisticated systems constituting a large number of concepts and causal relationships (Papageorgiou et al., 2004); lack of total dependence on experts’ opinions; and its intelligence due to using learning algorithm in comparison with most MCDM methods (Rezaee & Yousefi, 2018). The main components of the FCM include nodes, arcs/edges between nodes, and the signs on the arcs. The nodes represent the concepts describing the system, the arcs denote the causal relationships between the concepts, and the sign on the arcs expresses the kind of causality between the concepts (Papageorgiou, Stylios, & Groumpos, 2006). Fig. 1 depicts a sample of FCM and its components. In Fig. 1, Ci represents the nodes or concepts that are interconnected via weighted arcs. Each relationship between Ci and Cj has a weight expressed by Wij, which represents the degree of casualty and the type of relationship between the two respective concepts. Accordingly, Wij > 0 indicates a positive causal relationship, Wij < 0 denotes a negative causal relationship, and Wij = 0 shows lack of a

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Table 1 Scoring the OHS risks’ assessment criteria. Rating

1 2

Very low Low

3

Medium

4

High

5

Very high

Description Severity (S)

Occurrence (O)

Detection (D)

Cost (C)

Time (T)

Minor injuries such as scratches, bruises

Rarely or never occurs May occur

Almost certainly detect potential risk by existing controls. It is very likely tracing potential risk and detect by existing control In half of the cases, it is likely that trace and detect potential risk by existing controls. There is very little probability to trace and detect risk by existing controls. There is very partial probability to trace and detect risk by existing controls.

Less than 5,000,000 Rial 5,000,000 to 10,000,000 Rial 10,000,000 to 15,000,000 Rial 15,000,000 to 20,000,000 Rial More than 20,000,000 Rial

Less than 1 week 1 to 2 weeks 2 to 4 weeks 5 to 8 weeks More than 8 weeks

Injuries and damage that requires medical intervention Temporary inability and bone fractures, lacerations, severe surgery organ Very serious and hard injuries, amputations, chronic diseases and long Death or permanent disability

Can occur randomly It happens a lot Occurs constantly

Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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Fig. 1. Fuzzy cognitive map sample.

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causal relationship between the two concepts. To draw such a map, time series data and comments made by experts can be used. In the computation-based FCM approach, time series data can be used as inputs, and the neural network can be applied to approximate the map weights and relationships between variables. The computational methods can be categorized into two groups, namely automatic and semi- automatic. In the most widely used semiautomatic approach, a set of inputs obtained from the knowledge and experience of an expert in the given field of study is required to establish the FCM, and then concepts and causal relationships between concepts can be plotted as well. In the automatic approach of FCM, numeric vectors are converted to fuzzy sets, and the degree of similarity between the vectors and the type of relationship (direct and indirect) between them are determined via fuzzy logic (Schneider, Shnaider, Kandel, & Chew, 1998). In this approach, the method introduced by Schneider et al. (1998) is used to determine the strength of relationships (i.e., the similarity between the vectors vi and vj). Here, v1 and v2 are vectors belonging
 to concepts 1 and 2, and x1 v j and x2 ðv j Þ represent the membership degrees of j in these vectors. Undoubtedly, different calculations are needed for vectors depending on their relationships, whether direct or indirect (Schneider et al., 1998). For example, if v1 and v2 are directly related, in the first step, the value of dj which represents the distance between the two j elements of the vector is calculated by Eq. (1) (Meade & Sarkis, 1999). Then, the mean distance between v1 and v2, i.e., AD, is calculated using Eq. (2). In Eq. (2), m denotes the number of vector elements. Finally, the similarity between the two vectors is obtained from Eq. (3). In this regard, S = 1 shows a complete similarity, and S = 0 indicates lack of complete similarity. When v1 and v2 have an inverse relationship, the similarity calculation method remains the same as before, with the exception of dj, which is obtained from Eq. (4).

  dj ¼ x1 ðv j Þ
x2 ðv j Þ

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ð1Þ

j¼1

m

S ¼ 1
AD   dj ¼ x1 ðv j Þ
ð1
x2 ðv j ÞÞ

ðkþ1Þ

Step 3. Calculate Aj

ðkþ1Þ

ð2Þ

ð4Þ

Now, the experts’ opinions are required to determine variables with causal relationships, because all the variables obtained from the weighted matrix are not necessarily related. For example, there may be two variables in this matrix with a high degree of similar-

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þ gAi

ðk
1Þ

    ðk
1Þ ðk
1Þ ðk
1Þ ðk
1Þ Aj
sgn W ji W ji Ai

399

400

ð6Þ

Step 5. Calculate the termination functions. Step 6. Until the termination condition are met. Step 7. Return the final weights to the Procedure 2. The second procedure: Differential Evolutionary (DE) Step 1. Initialize the DE population in the neighborhood of ðkþ1Þ

ð3Þ

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398

Step 4. Update the weight wkij according to Eq. (6): ðk
1Þ

359

ð5Þ

j¼1

ðkÞ

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396

C B N C B X B ðkÞ ðkÞ ðkÞ C Aj :wji C ¼ f B Ai þ C B A @ j–i

W ji ¼ cW ji

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395

1

0 Ai

according to Eq. (5):

ðkþ1Þ W NHL

m   P dj 

AD ¼

ity, while they may fail to have a causal relationship with one another in the real world. Therefore, experts’ opinions can help easily identify and eliminate redundant and irrelevant relationships (Meade & Sarkis, 1999). It should be noted that in formulating a good FCM, it is required to integrate knowledge, experience, and scenario planning to achieve the desired result. In other words, developing a FCM entails a six-step process: identifying the concepts influencing the system and the relationships between these concepts, allocating weights to the relationships and concepts based on Eqs. (1) to (4), applying the comments made by the experts, choosing the calculation approach, measuring the result of interactions among concepts in each cycle, and continuing this process until the termination conditions are met. As stated, after drawing the FCM, it is chiefly important to accurately estimate the map weights relying on the experts’ opinions. In recent years, learning algorithms have been used to promote the accuracy of weight estimation, improve the map structure, and reduce dependence on the experts’ opinions. These algorithms are classified under several groups: Hebbian learning algorithms, metaheuristic algorithms, hybrid algorithms, and other algorithms (Salmeron, Mansouri, Moghadam, & Mardani, 2019). Among these categories, the hybrid learning algorithms (an integration of Hebbian and meta-heuristic algorithms) are well suited for the weight correction of the maps created via a combination of time series data and experts’ opinions. In this study, hybrid learning algorithm is a combination of non-linear Hebbian learning (NHL) and differential evolutionary (DE) algorithms can be used because they update the non-zero weights in different iterations and maintain the relationships between the concepts that were defined in the original map (Papageorgiou & Salmeron, 2014). Besides, the DE is a robust search algorithm for solving optimization problems over continuous spaces. In general, the DE algorithm is used to overcome the main drawback of the genetic algorithm, i.e., lack of local searches. The steps of this hybrid algorithm are mentioned in the following. The first procedure: Non-linear Hebbian Learning (NHL): Step 1. Read input concept state A0 and initial weight matrix W0. Step 2. Repeat for each iteration k.

W NHL and within the suggested weight constraints. Step 2. Repeat for each input concept state (k). Step 3. For i = 1 to NP (number of population) do step 4 to step 7. Repeat 4 to 6 for each population. ðkÞ

Step 4. Mutation W j

to obtain mutation vector.

Step 5. Crossover mutation vector to obtain trial vector.   ðkÞ Step 6. If FðTrial VectorÞ 6 F W i , accept Trial_Vector for the next generation.

Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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Step 7. Until the termination condition are met (Papageorgiou et al., 2006): o A stable state, that is, until A(k+1) is equal to A(k) or have a negligible difference. o Exhibits limited cycle behavior, with the concept values falling in a loop of numerical values under a specific time period. o Exhibits a chaotic behavior, with each value reaching a variety of numerical values in a non-deterministic, random way.

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In the above steps, A0 represents the matrix of initial state of the system; W0 denotes the initial weight matrix among the concepts; A(k
1), A(k) and A(k+1) refer to the new values of the concepts at the iterations k
1, k, and k + 1, respectively; g and c denote the positive ðkÞ W ji

429

and very small numbers (learning rate);

430

the updated values of the weights between the concepts Ci and Cj

431

435

at the iterations k and k
1, respectively; W NHL and W i refer to the final weight matrix of the causal relationships among the concepts in the first stage and the state matrix of the concept Ci at the kth iteration; sgn denotes the sign function; NP refers to the number of population; and f represents the transformation function.

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2.3. MOORA method

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MOORA is a multi-criteria decision-making method with a high potential for comprehensive evaluation of alternatives confronting considerable diversity and multiplicity of effective factors. The MOORA method was put forth by Brauers and Zavadskas (2006) as one of the multi-objective optimization methods for effectively solving complex decision-making problems. This method seeks to select the best alternative considering a set of commonly conflicting criteria. In other words, this method simultaneously examines favorable and unfavorable criteria (Chakraborty, 2011; Karande & Chakraborty, 2012). Several advantages identified for the MOORA method over some of the available decision-making methods include fewer mathematical computations, less computational time, more simplicity, and more stability compared with some MCDM techniques such as AHP, TOPSIS, ELECTRE, VIKOR, and PROMETHEE. (Akkaya et al., 2015). The MOORA method consists of four main steps as follows: Step 1: Create the decision matrix X with m alternatives and n criteria according to Eq. (7), where xij denotes the performance measure of the ith alternative on the jth criteria.

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456

ðkþ1Þ

2

X ¼ ½xij mn

x11 6x 6 21 ¼6 6 .. 4 .

x12 x22 .. .

... ... .. .

xm1

xm2

. . . xmn

458

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470

472

x1n x2n .. .

represent ðkÞ

3 7 7 7 7 5

ð7Þ

Step 2: Normalize the decision matrix X via Eq. (8),

459

460

and

ðk
1Þ W ji

xij xij ¼ sffiffiffiffiffiffiffiffiffiffiffi ; m P x2ij

8 i; j

ð8Þ

j¼1

Step 3: Calculate the normalized evaluation value for each alternative considering all the existing alternatives. In fact, the final score of each alternative is obtained using Eq. (9). In this equation, yi represents the MOORA score for the ith alternative.j ¼ 1; 2; 3; :::; t and j ¼ t þ 1; t þ 2; :::; n refer to the objectives that must be maximized (beneficial criteria) and minimized (non-beneficial criteria), respectively.

yi

¼

t X j¼1

xij




n X j¼tþ1

xij ;

8i

ð9Þ

5

Step 4: In some cases, objectives (criteria) may vary in terms of the degree of importance from the points of view of decisionmakers. Therefore, the importance of criteria is determined by significant coefficients (weights). When the objective weight is taken into account in the computations, the MOORA score for each alternative is measured using Eq. (10).

yi

¼

t X j¼1

wj xij




n X

wj xij

473 474 475 476 477 478

479

ð10Þ

j¼tþ1

481

In this equation, wj refers to the jth objective (criterion) weight. It should be noted that depending on the number and the value of the beneficial and non-beneficial criteria, yi can be either positive or negative. In the final ranking, alternatives with higher scores are considered desirable. Put it another way, the alternative with the highest yi score is considered the best option, and the alternative with the lowest yi score is viewed as the worst one. However, as this study aims to identify the most critical risks, and as the assessment criteria are of a negative nature (see Table 1),
yi is the proposed score in this study. In other words, a risk with a higher proposed score is given the first priority and considered the most critical risk.

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3. Proposed approach

494

This section addresses the proposed approach integrating the FMEA, FCM, and MOORA methods to assess the OHS risks and prioritize them. Given the further explanations provided in the previous section about the mentioned methods, the proposed approach can be defined in three phases. In the first phase, the FMEA technique is performed by the FMEA team to identify risks within the scope of risk assessment. Then, the values of the assessment criteria are determined for each identified risk according to Table 1. A point that should be clarified is that the respective team can include management representative; production manager or his representative; the company health, safety, and environment (HSE) expert; process control expert; supervisor of the department under study; worker’s representative; and a general practitioner (to determine the criteria associated with the cost and length of the treatment). The output of this phase leads to the formation of a decision matrix in which each row indicates the risk and each column represents the risk assessment criterion. In the second phase, the FCM method is used to assign different importance to assessment criteria based on the causal relationships among them and reduce the dependence on experts’ opinions. To this end, the causal relationships among criteria and the weight of these relationships are first determined using the calculations in the FCM automatic construction method and the decision matrix, i.e., the first phase output. In fact, one of the main problems studied in this phase is the weight of causal relationships between factors, which can be determined by considering the two approaches—automatic or semi-automatic. In the semi-automatic approach, with respect to the gathering of experts’ opinions, we deal with whether or not causal relationships exist between factors, type of relationships (whether it is direct or inverse), and allocation of weight to the relationships. But in the automatic approach, the weights of causal relationships are determined using Eqs. (1) to (4) and according to the existing dataset with respect to the factors, and the expert can only make comment about whether there is a relationship or not and its type. The feature of automatic approach in comparison with semi-automatic is that the weights derived by automatic approach are less dependent on personal and subjective ideas of experts, particularly in complicated decision-making problems. Therefore, the automatic FCM approach was used in this study, such that the available dataset for each factor are the same values determined for the factor with

495

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respect to the risks identified by FMEA technique (output of first phase). After calculating the weights of the causal relationships between the factors, we needed to apply making scenario technique and learning algorithm simultaneously in order to determine the weight of each risk factor. In fact, using the learning algorithms can help to raise the precision of weight estimation and lower the dependency on experts’ opinions (Papageorgiou & Salmeron, 2014). To use the hybrid learning algorithm, we were required to

define a scenario and run the learning algorithm with respect to the weight matrix of the causal relationships between factors and the studied scenario. In each scenario, it is assumed that only one of the assessment criteria is considered in the decision-making process (the value of the corresponding node equals 1). Then, according to the weighted matrix and the scenario defined, the hybrid learning algorithm is implemented in MATLAB software, and its output for each node is considered to be the effect of the cri-

Fig. 2. The proposed approach for risk prioritization.

Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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terion under study on the decision-making process. It should be noted that the pseudocode of the hybrid algorithm has been presented at the end of Section 2.2. The output of running the learning algorithm for the scenario have the same influence value of risk factor in the process of decision making (in this case risk evaluation) with respect to the satisfaction of the stopping conditions of algorithm (presented in Section 2.2) and a sustainable structure obtained by the system. After implementing all scenarios and identifying the influence of each criterion, these values are normalized, and the weights or the importance of the criteria are determined as well. In the third phase of the proposed approach, the MOORA method is carried out with the aim of distinctively prioritizing the identified risks with respect to the different importance of the assessment criteria. The inputs of the MOORA method include the output of the first phase, i.e., the decision matrix, and the output of the second phase, i.e., the set of weights of the assessment criteria. According to the methodology of the MOORA, after developing the decision matrix, its normalization is performed, in such a way that each entry of the matrix is exactly the normalized value of each assessment criterion. Then, by applying the weights of criteria in the normalized matrix, a weighted normalized matrix is obtained. Using Eq. (10), the score of each alternative (in this study, OHS risk) is calculated, and the alternatives prioritization is conducted using the proposed score. It should be noted that the proposed score in this study is considered to be
yi regarding the nature of the assessment criteria and the type of the problem at issue. In this case, the risk prioritization is carried out in such a way that the risk with the higher proposed score than the other risks is given the first priority and is known as the most critical risk. Fig. 2 depicts the implementation process of the proposed approach.

584

4. Analysis of the results

585

To investigate the capability of proposed approach, the present study seeks to prioritize the OHS risks in a company active in manufacturing automotive spare parts based on this approach. Therefore, this section is dedicated to the examination of the results yielded from implementing the proposed approach to evaluate and prioritize OHS risks in the department of mold making of the company under study. This company has six major departments, one of the most important of which is mold making given the current process of production. It is because this department is the main entrance to the production line of parts in the company, and in the occurrence of any identified risk, it can bring the production line to a long-run halt or increase the likelihood of failures in the final products of the company, and hence customer’s dissatisfaction. In other words, the occurrence of OHS risk in this department can cause the company to face the serious problems. According to the first phase of this approach, OHS risks are identified by the FMEA team, and then the values of the assessment criteria for each risk are determined, as shown in Table 2. It should be noted in order to form the part this table, required information has been extracted from Yousefi et al. (2018) study. According to the methodology of the FMEA technique, after the formation of Table 2, the RPN score is calculated by multiplying the values of assessment criteria to prioritize the risks. In calculating the traditional RPN score, only three criteria, namely S, O, and D, are considered. Thus, irrespective of other important criteria in light of the type of problem under study, we can prioritize the risks according to RPN3 = S  O  D. The prioritization obtained from RPN3 is illustrated in Fig. 3. As shown in Fig. 3, the existing risks are prioritized only in seven distinct priorities. It is mainly due to the shortcomings of

586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614

7

this traditional RPN score including the lack of assignment the different weight to criteria, and the inability to take into account the causal relationships, and other drawbacks highlighted in the introduction section. Considering the limited organizational resources, this non-distinctive risk prioritization can rarely lead management to the detection of risks that removing their negative consequences can provide considerable improvement in the system. If other important criteria, including the cost and the duration of treatment (cost and time)-in the event of risk occurrence- can be considered in the decision-making process, risk prioritization is conducted according to the RPN5 = S  O  D  C  T. The prioritization obtained from RPN5 is demonstrated in Fig. 4. It should be noted that in the occurrence of an OHS risk, the period of time needed for an injured person to return to workplace (the length of treatment or machine timeout or time of training a substituted person), and for a possible damage to machinery and equipment to be fixed, are considered the value of this factor with respect to the related risk. The costs sustained by the occurrence of the risk, by considering the injured person (cost of his/her treatment and cost of training a substituted person if needed) or cost of possible repairing of machines and equipment, are considered as the value of this factor with respect to the related risk. As shown in Fig. 4, the existing risks are prioritized by considering two additional important indicators in the 10 distinct priorities. In this prioritization, critical risks are that sort of risks imposing exorbitant costs on the system, which fail to be taken into account in the traditional RPN3 score. Despite the improvement of initial prioritization, the RPN5 prioritization is still non-distinctive and needs to be further improved. This prioritization can be promoted by removing some of the drawbacks of the traditional score including estimation procedure, lack of consideration of causal relationships among criteria in the decision-making process, and inability to assign different weights to the assessment criteria. Accordingly, the second phase of the proposed approach aims at assigning different weights to the assessment criteria according to their causal relationships. At the beginning of this phase, the FCM concepts are defined, and the causal relationships among criteria are determined according to FCM calculations. Assessment criteria are considered FCM concepts. The decision matrix values (see Table 2) are normalized in order to determine the weight of the causal relationships among the criteria, and then the weighted matrix of the causal relationships among the criteria is determined using the Eqs. (1) to (4) and the explanations presented in the introduction of the FCM method (see Table 3). For instance, according to Table 2, the dataset for Severity (S) and Cost (C) are equal to {4,5,5,4,4,4,5,4,4,4,4,4} and {1,1,1,1,5,1,1,1,3,5,4,4}, as we have after normalization:

S ¼ f0:8; 1; 1; 0:8; 0:8; 0:8; 1; 0:8; 0:8; 0:8; 0:8; 0:8g; C ¼ f0:2; 0:2; 0:2; 0:2; 1; 0:2; 0:2; 0:2; 0:6; 1; 0:8; 0:8g Now according to Table 3, the value of the weight of the causal relationship between the factor S and C (i.e., 0.55) is computed by using Eqs. (1) to (3). Given the direct relationship between the two factor C and S, we have;

  dj ¼ x1 ðv j Þ
x2 ðv j Þ ¼ ðj0:8
0:2j þ j1
0:2j þ j1
0:2j þ j0:8
0:2j

615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661

662 664 665 666 667 668

669

þj0:8
1j þ j0:8
0:2j þ j1
0:2j þ j0:8
0:2j þj0:8
0:6j þ j0:8
1j þ j0:8
0:8j þ j0:8
0:8jÞ ¼ 5:4

672

m   P dj 

AD ¼

j¼1

m

671

¼

5:4 ¼ 0:45 12

S ¼ 1
AD ¼ 1
0:45 ¼ 0:55

Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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Table 2 The OHS risks and values of the assessment criteria. Symbol

Risk name

Cause of risk

Risk effects

S

O

D

C

T

R01 R02

Falling part of mold when using a CNC machine Burr/chip contacts with eyes when hand machining of metals Burr/chip contact with eyes while grinding Slipping in molding department Electricity shocks during fixing switchboard Burr/chip contacts with eyes while turning Unpleasant smell in molding department

Carelessness in manual handling of mold parts Burr/chip projected while working

Severe injury Eyes damage

4 5

3 3

2 2

1 1

4 1

Burr/chip projected while working Gasoline overflow on the floor Contact with electricity Burr/chip projected while working Not air conditioning – Gasoline use in molding department Not ergonomic/adjustable tables and chairs Lack of shield – Personnel carelessness

Eyes damage Injury Electrical burn Injury Respiratory problemsheadaches Musculoskeletal problems Injury – severe injury

5 4 4 4 5

3 2 2 3 2

3 3 2 2 4

1 1 5 1 1

1 2 4 1 1

4 4

3 4

4 3

1 3

1 4

Failure of the drill holder lock Personnel carelessness – Inappropriate shield Lack of shield – Personnel carelessness

Amputation – severe injury Severe harm Severe harm

4 4 4

4 2 3

2 3 2

5 4 4

5 5 5

R03 R04 R05 R06 R07 R08 R09 R10 R11 R12

Ergonomic problems when using the computer Finger contact with grinding blades when using grinding machine Falling down radial drilling holder Dealing with fan when using and shifting fan Hand contact with milling blades

Fig. 3. The prioritization of OHS risks based on the RPN3 score.

Fig. 4. The prioritization of OHS risks based on the RPN5 score.

Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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R. Dabbagh, S. Yousefi / Journal of Safety Research xxx (xxxx) xxx Table 3 The output values of the implementation of the hybrid learning algorithm. Scenario

Review Review Review Review Review Total

678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715

the the the the the

Initial state (S, O, D, C, T)

criterion criterion criterion criterion criterion

S O D C T

Weighted matrix

(1,0,0,0,0) (0,1,0,0,0) (0,0,1,0,0) (0,0,0,1,0) (0,0,0,0,1)

S

O

D

C

T

0 0.79 0 0 0

0 0
0.63 0 0

0 0 0 0 0

0.55 0 0 0 0.87

0.62 0 0 0 0

Now, the hybrid NHL-DE learning algorithm is implemented to identify the effect of each criterion on the risk prioritization process according to the weighted matrix of the causal relationships among the defined criteria and scenarios. Notably, in each scenario, one criterion is considered to be the only active node (see Table 3). For example, to derive the weight of the factor S, the scenario is presented as [S O D C T] = [1 0 0 0 0]. By implementing the learning algorithm in MATLAB software, computations are done in such a way that the system achieves a stable structure. Indeed, in addition to determining the weight of each criterion kept under scrutiny, this algorithm reduces the level of dependency on experts’ opinions and adds intelligibility aspect to the decision-making system. For instance, the influence value of factor S is equal to 0.7957 according to Table 3. At this point, having determined the level of the influence of all factors, the normalized weight of each factor is derived in order to be used with MOORA method, and the weight of factor S is equal to 0.2700 (output for factor S / total outputs = 0.7957 / 2.9465 = 0.2700). The output values resulting from the implementation of the learning algorithm and the weight of each criterion are presented in Table 3. As it is observed in Table 3, S and C criteria with the weight of 0.2700 and 0.2271, respectively, are the most important risk assessment criteria in this study. After determining the weights of the assessment criteria, these weights should be considered in the process of risk assessment and prioritization. Finally, in the third phase, the MOORA method is implemented to provide a distinct and full prioritization based on the weights determined by FCM. After creating the decision matrix (Table 2), the normalization of this matrix is carried out using Eq. (8), as shown in Table 4. Then, the weights calculated by the FCM are applied to the normalized decision matrix, and the weighted normalized matrix is expressed as Table 5. Using Eq. (10), yi is calculated for each risk; then, according to the nature of the problem at issue, the proposed score is presented in Table 6. According to Table 6, risks with the higher proposed score are given the initial priority. In other words, the risks that are given the top priorities are critical, and the management team needs to take corrective/preventive actions against such risks. As depicted

Output

Weight

0.7957 0.4677 0.4997 0.6691 0.5143 2.9465

0.2700 0.1587 0.1696 0.2271 0.1746 1

in Fig. 5, with covering some of the main shortcomings of the traditional RPN score, the proposed approach can prioritize risks in 12 distinct priorities (full prioritization), unlike RPN3 and RPN5. Simultaneous comparison of the risk prioritization based on the mentioned score in Table 7, it can be concluded that the advantages of the proposed approach over the traditional RPN score include: (a) considering other important assessment criteria in the risk prioritization; (b) assigning different weights to assessment criteria; (c) taking into account the causal relationships among criteria; (d) reducing the dependence on the experts’ opinions due to the use of the learning algorithm; and (e) performing a full and distinct risk prioritization. A further investigation of Table 7 reveals that according to RPN3 score, R08 and R09 with RPN3 = 48 were placed in the first priority. According to RPN5 score, R08 with RPN5 = 48 falls to the sixth place and is replaced by R10 with RPN5 = 800. Examining the values of C and T criteria associated with these two risks can simply explain the reason for this change. According to investigations, in R10, C = T = 5, and in R08, C = T = 1; thus, as in the latter, multiplication by 1 has no effect on the product, this risk (R08) falls to a lower place even if it has the highest RPN3, indicating a defect in the computation process of the traditional RPN score. Put it another way, R08, in the event, may impose lower cost and the duration of treatment on the affected person. It is also observed that R09, unlike R08, has an insignificant fall in prioritization and ranks as the second top risk according to RPN5. The reason for this is the high values of C and T criteria (3 and 4, respectively) for the given risk. The simultaneous placement of R11 and R12 in the third priority based on RPN5 can perplex the decision-maker in defining and implementing corrective/preventive actions in view of the resource constraints (given the possible removal of three critical risks). Notably, the proposed score removes the stated problem in the prioritization, in such a way that R10, R11, and R12 with the scores of 0.3619, 0.3252, and 0.3232, respectively, rank as the third top risks. Further investigations shed light on the reason for R09 priority shift from the second to the fourth position despite having a higher RPN5 than R11 and R12 according to the proposed score. Table 2 indicates that for three stated risks, S (the most significant criterion

Table 4 The normalized decision matrix in the MOORA method. Risk

C1

C2

C3

C4

C5

R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12

0.2703 0.3379 0.3379 0.2703 0.2703 0.2703 0.3379 0.2703 0.2703 0.2703 0.2703 0.2703

0.2970 0.2970 0.2970 0.1980 0.1980 0.2970 0.1980 0.2970 0.3961 0.3961 0.1980 0.2970

0.2085 0.2085 0.3128 0.3128 0.2085 0.2085 0.4170 0.4170 0.3128 0.2085 0.3128 0.2085

0.1010 0.1010 0.1010 0.1010 0.5051 0.1010 0.1010 0.1010 0.3030 0.5051 0.4041 0.4041

0.3482 0.0870 0.0870 0.1741 0.3482 0.0870 0.0870 0.0870 0.3482 0.4352 0.4352 0.4352

Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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Table 5 The weighted normalized decision matrix in the MOORA Method. Risk

C1

C2

C3

C4

C5

R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12

0.0730 0.0912 0.0912 0.0730 0.0730 0.0730 0.0912 0.0730 0.0730 0.0730 0.0730 0.0730

0.0472 0.0472 0.0472 0.0314 0.0314 0.0472 0.0314 0.0472 0.0629 0.0629 0.0314 0.0472

0.0354 0.0354 0.0530 0.0530 0.0354 0.0354 0.0707 0.0707 0.0530 0.0354 0.0530 0.0354

0.0229 0.0229 0.0229 0.0229 0.1147 0.0229 0.0229 0.0229 0.0688 0.1147 0.0918 0.0918

0.0608 0.0152 0.0152 0.0304 0.0608 0.0152 0.0152 0.0152 0.0608 0.0760 0.0760 0.0760

Table 6 Risk prioritization based on the proposed score.

754 755 756 757 758 759 760 761 762 763 764 765

Risk

Proposed score (¼
yi )

Priority

R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12

0.2392 0.2119 0.2296 0.2108 0.3153 0.1936 0.2315 0.2290 0.3185 0.3619 0.3252 0.3232

6 10 8 11 5 12 7 9 4 1 2 3

based on the FCM output) equals 4. However, considering C (the second most important criterion) and T (the third most important criterion) are equal to 3 and 4, respectively, for R09, while C and T criteria for R11 and R12 are equal to 4 and 5, respectively. Therefore, the priority of R11 and R12 over R09 in terms of significant criteria in the decision-making process has placed them in the second to fourth position, respectively. The comparison of risk prioritization in terms of the type of prioritization performed (whether full or non-full) is also shown in Fig. 6. Prioritization of OHS risks was performed with regard to some of different weight cases and by focusing on the importance of each of the five criteria, and its results were presented in Table 8. In each

case, it is assumed that 0.2 is added to the weight of the test criterion and 0.05 is deducted from the weight of other criteria. So that Cases 1 to 5 in this table respectively are (WS = 0.4700, WO = 0.1087, WD = 0.1196, WC = 0.1771, WT = 0.1246), (WS = 0.2200, WO = 0.3587, WD = 0.1196, WC = 0.1771, WT = 0.1246), (WS = 0.2200, WO = 0.1087, WD = 0.3696, WC = 0.1771, WT = 0.1246), (WS = 0.2200, WO = 0.1087, WD = 0.1196, WC = 0.4271, WT = 0.1246) and (WS = 0.2200, WO = 0.1087, WD = 0.1196, WC = 0.1771, WT = 0.3746). Due to Table 8, it is observed that priority changes in risks which have priority in the original state (R10, R09, R11, R12, R05) are such that their rank merely have had a slight change among the first to fifth priorities. In other words, given the increasing importance of each criterion in comparison to the original state, such risk is in higher priorities, which has more value due to important criteria. By examining risks that have not priority (other risks), it can be understood that their ranks have been changed among the 6th to 12th priorities, but with the difference that these changes are more tangible than risks which have priority. For example, the R04 in Case 2 is at 12th priority, while this risk in Case 5 is in the 7th priority. Regarding the risks which have priority, it is also clear that the risk of R10 in the cases studied has been ranked in first place, and the reason is that this risk has a high value in its own risk factors (S = O = 4, C = T = 5). In other words, this risk is considered as a critical risk so, by a relative increase of other criteria’s importance. By a further review of this risk it is observed since the value of factor D for R10 is equal to 2, the further increase in the importance of this factor leads to a

Fig. 5. The prioritization of OHS risks based on the proposed score.

Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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R. Dabbagh, S. Yousefi / Journal of Safety Research xxx (xxxx) xxx Table 7 The comparison of risk prioritization based on the proposed score via the traditional score. Risk

RPN3 score

Priority

RPN5 score

Priority

Proposed score

Priority

R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12

24 30 45 24 16 24 40 48 48 32 24 24

6 5 2 6 7 6 3 1 1 4 6 6

96 30 45 48 320 24 40 48 576 800 480 480

5 9 7 6 4 10 8 6 2 1 3 3

0.2392 0.2119 0.2296 0.2108 0.3153 0.1936 0.2315 0.2290 0.3185 0.3619 0.3252 0.3232

6 10 8 11 5 12 7 9 4 1 2 3

Fig. 6. The full prioritization of risks based on the proposed score compared with traditional scores.

Table 8 The sensitivity analysis based on the proposed score.

793 794 795 796 797

Risk

Original

Case 1

Case 2

Case 3

Case 4

Case 5

R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12

6 11 9 10 5 12 8 7 2 1 3 4

8 9 7 11 4 12 6 10 5 1 2 3

6 9 7 12 5 11 10 8 2 1 4 3

10 11 8 9 5 11 6 7 3 1 2 4

6 10 8 11 2 12 7 9 5 1 3 4

6 11 9 7 4 12 8 10 5 1 2 3

change in priority of R10 and the placing other risks in the first priority. For instance, assuming that we increase the weight of the factor D by 0.22 instead of 0.2 and decrease the weight of other criteria by 0.055, the R11 is placed in the first priority and the risk of R10 falls to the second priority.

Overall, this study tried to present a hybrid approach based on FMEA, FCM and MOORA methods to address some of the deficiencies of the conventional FMEA technique that is presently used in many manufacturing units active in different fields. But despite the scope of using this technique, many researchers believe that

Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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FMEA technique cannot provide a decision-maker or organization management with reliable results (Liu et al., 2013). For instance, lack of a distinct or full prioritization (i.e., similar priorities for quite many risks) in FMEA technique can plunge the decisionmaker into confusion, given resource constraints in organization on the application of corrective/preventive actions. In addition to this, allocating equal weight to risk factors can disregard the importance of certain critical criteria in the prioritization of risks, which can end up with an unreliable prioritization. Because it is possible, a risk is viewed as a prioritized risk and organization’s efforts are concentrated on it; and, if this happens, it will be less intense and impose minimal costs on the organization, while critical risks are ignored. On the other hand, the complete dependency of results on experts’ opinions, if some of these people have presented non-experts’ opinions, may cause organization’s efforts to stray into wrong path. For this reason, an attempt was made in the proposed approach to reduce the effect of the errors of some of the experts on the output result as much as possible by using certain intelligent methods (say, FCM learning algorithms). For this reason, the distinct prioritization of risks and the placement of risks with greater values for more important risk factors in critical ranks can acknowledge the improvement of the accuracy of the results yielded by the proposed approach in comparison with conventional FMEA technique.

827

5. Discussion and conclusions

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Today, given the importance of the workforce health issue in all countries, there is an increasing need to develop fully-fledged methods for identifying and assessing OHS risks and further improving the working conditions in manufacturing systems. Managers of industries have figured out that there is a crucial need for serious attention to safety in order to improve productivity. This issue is also economically important because accidents can cause excessive costs (e.g., damage to equipment and products, unemployment during times of change, time-consuming investigations, and expenses required for training substitutes). Therefore, managers of industries need to use decision-making approaches and safety management systems in their factories to avoid wasting capital. Hence, this study seeks to present a hybrid decisionmaking approach for OHS risks analysis using FMEA, FCM, and MOORA methods in three phases. The reason to integrate these methods in the proposed approach was to overcome some shortcomings of the traditional RPN as the conventional FMEA technique score. To that end, the FMMA technique was used to identify the risks and determine the values of the assessment criteria, the FCM and the hybrid learning algorithm were deployed to assign different weights to the assessment criteria in view of the causal relationships among them and reduce the dependence on the experts’ opinions, and MOORA method was applied to consider the weights of the criteria in the risk prioritization process and make distinction among the priorities assigned to the respective risks. Implementing the proposed approach for investigating the OHS risks of the mold making department as a main part of a company active in manufacturing automotive spare parts and comparing its findings with the results yielded from the traditional RPN score indicate that the risk prioritization according to the proposed score is closer to reality and provides a distinct prioritization to the decision-maker. In other words, the proposed approach enables the decision-maker to identify the critical risks based on the existing distinction and plan the corrective/preventive actions in line with the resource constraints, and then re-evaluate the risks to investigate the effectiveness of the respective actions. Because there are some constraints on resources, time, and budget in orga-

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nization, this makes it impossible to eliminate all risks. However, it is expected that organizations start reducing critical risks (including major and intolerable risks) (Kumar Dadsena, Sarmah, & Naikan, 2019). In this regard, the existing risks can be classified in four categories, namely: Extreme (not acceptable), High (generally not acceptable), Medium (generally acceptable), and Low (acceptable), according to their proposed scores and experts’ opinions. Since the range of these values is equal to 0.1683, the length of each category is equal to 0.0421, assuming that there are four categories. Therefore, R06, R04, R02, R08, R03, and R07 are placed in the ‘‘Low” risk category [0.1936, 0.2357], R01 is placed in the ‘‘Medium” risk category [0.2357, 0.2778], R05 and R09 are put into the ‘‘High” risk category [0.2778, 0.3198], and R12, R11, and R10 are grouped in the ‘‘Extreme” risk category [0.3198, 0.3619]. Needless to say, it is imperative that corrective/preventive actions are required, planned, and implemented for risks placed in the ‘‘High” and ‘‘Extreme” categories. In this regard, measures required to be taken for R10 (i.e., falling down radial drilling holder), R11 (i.e., dealing with fan when using and shifting fan), R12 (i.e., hand contact with milling blades), R09 (i.e., finger contact with grinding blades when using grinding machine), and R05 (i.e., electricity shocks during fixing switchboard) are repairing the drills holder lock and periodically checking the device shields, inserting shields for fan and turning off the fan during the movements, installing appropriate shield and training personnel on how to safely use milling machine, installing safety shield and training personnel on how to use the machine properly and safely, and putting the floors insulation and installing earth system, respectively. The present study also has limitations such as lack of consideration of uncertainty in determining the values of assessment criteria and prioritizing risks in a fuzzy environment, which can be overcome using fuzzy logic and fuzzy MOORA, respectively, in the future works.

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Please cite this article as: R. Dabbagh and S. Yousefi, A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.09.021

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