GENERAL ARTICLES

 

Fuzzy-logic-based ergonomic assessment in an automotive industry

 

 

V. Kamala^*; T. Paul Robert

Department of Industrial Engineering, Anna University, Tamil Nadu, India

 

 

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ABSTRACT

The objective of this study was to develop and evaluate a tool for measuring an organisation's ergonomie level. The study was carried out to fill a research gap in which no attempt had been made to design an index for evaluating a company's ergonomics. An ergonomic index measuring model with 15 criteria using a fuzzy logic technique was developed during this study. The fuzzy ergonomic index (FEI), which represents the ergonomic level of the company, and the fuzzy performance importance index, which assists in resolving ergonomics barriers, were then calculated. The findings show that the model is capable of analysing ergonomics adequately and has practical application. Application of the methodology presented in this paper will reveal the measures necessary to improve an organisation's ergonomie level.

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OPSOMMING

Die doel van hierdie studie was om 'n instrument te ontwikkel en te evalueer om 'n organisasie se ergonomiese vlak te meet. Die studie is uitgevoer om 'n navorsingsgaping te vul waarin geen poging aangewend is om 'n indeks vir die evaluering van 'n onderneming se ergonomie te ontwerp nie. 'n Ergonomiese indeksmeting model, gebaseer op 'n wasig logika tegniek, met 15 kriteria is tydens hierdie studie ontwikkel. Die wasige ergonomiese-indeks (WEI), wat die ergonomiese vlak van die maatskappy verteenwoordig, en die wasige werkverrigting-belangrikheidsindeks, wat help om ergonomiese hindernisse op te los, is toe bereken. Die bevindinge toon dat die model in staat is om ergonomie voldoende te analiseer en praktiese toepassing het. Toepassing van die metodologie wat in hierdie artikel aangebied word, sal die maatreëls bepaal wat nodig is om 'n organisasie se ergonomiese vlak te verbeter.

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1. INTRODUCTION

Man, machinery, and the environment all make a contribution to the manufacturing industry. In large-scale mass manufacturing, socio-technical difficulties developed as humans and machines worked together. In this context, ergonomics plays a vital role. Ergonomics' fundamental concepts would be used to improve workplace safety and productivity. The fundamental objective of ergonomics is to create and implement people's adaptation strategies for their job in effective and safe ways in order to improve their productivity and well-being [1]. Many studies have demonstrated that using ergonomics concepts in machine design, workplaces, job design, environmental design, occupational health and safety, and facility design has a beneficial impact [2]. Organisations' primary goals have been constantly to enhance efficiency and quality, resulting in increased profitability. Workstations can be designed to enhance performance and lower costs by integrating productivity and ergonomics [3]. There is a growing concern to improve productivity, safety, and quality in manufacturing industries [4]. With the growing age of the workforce, the average duration of illness increases [5]. To develop, design, and manage a new manufacturing technology, produce innovative goods, improve operational efficiency, and enhance health and safety, it is important to have scientific understanding and techniques in ergonomics. Even while manufacturing engineers and managers have traditionally been driven to build technologically automated systems to replace people, there is now a growing recognition that humans are still necessary for diverse tasks, even in fully automated production processes.

From this perspective, two significant research gaps can be identified. The first is that, while there are techniques for assessing a company's leanness, sustainability, and agility, there is no comparable model for ergonomics. The second gap is that there are few research articles on ergonomic assessment, and those that do exist are primarily concerned with conducting ergonomic evaluations of physiological, psychological, or environmental factors. The goal of this research was to develop a conceptual model for evaluating the ergonomics of manufacturing work environments while taking physiological, psychological, environmental, and safety factors into account. In today's production environment, quantifying ergonomics in an organisation has become increasingly important. In this research, the fuzzy logic approach was used.

 

2. BACKGROUND

The literature review was carried out on ergonomic dimensions and ergonomic assessment tools.

Parsons [6] reviewed the principles, methods, and models used in environmental ergonomics. The evaluation system considered environmental aspects such as heat and cold, vibration, noise, and light on the health, comfort, and performance of people. Grzybowski [7] contributed a method for ergonomic workplace evaluation that considered physical working environment factors (noise, vibration, microclimate, lighting, dust levels, toxicity, electromagnetic radiation), physical strain factors (energy consumption, static strain, repetitiveness of motion), psychological strain factors (information overload, monotony), and technological and organisational factors (factors related to workplace organisation and technical equipment). Ergonomic risks in the workplace can be determined by the movement of work and risks to posture [8]. Different tools have been developed to assess the exposure of risk, based on self-reporting, observational methods, and direct measurement [9]. Rating scales, questionnaires, checklists, and interviews are various forms of the self-reporting method. Initially, an effective rapid-screening instrument [10] was developed to identify cyclical jobs that expose workers to potentially harmful postures in order to determine the presence of the ergonomic risk factors associated with awkward postures of the lower extremities, the trunk, and the neck. Then a self-assessment software package (ErgoTech) [11] was developed to evaluate the ergonomic improvement potential of production systems to achieve excellence in the manufacturing industry. The application of this assessment tool revealed that production managers were able to successfully recognise ergonomic deficiencies on the shop floor. Later, the ErgoSAM tool [12] was used to assist in optimising the workplace in terms of production time and physical load on the operator by detecting high musculoskeletal loads early in the planning process.

Although some organisations use those approaches to ergonomic evaluation, the Occupational Safety and Health Administration (OSHA) checklist, and the Standard Nordic Musculoskeletal Disorders (MSDS) questionnaire [11], are most commonly used. The Standard Nordic MSDS questionnaire has been used in the manufacture of lCd screens and furniture [13]; and the OSHA checklist has been used in the semiconductor manufacturing assessment [14]. The Ovako Working Posture Analysing System (OWAS) [15], the Rapid Upper Limb Assessment (RULA) [16], the Rapid Entire Body Assessment (REBA) [17], the Strain Index [18], and the Occupational Repetitive Actions (OCRA) [19] are examples of observational methods that involve directly observing the worker and the tasks they perform. The RULA technique has been used in a variety of sectors, including garment manufacturing [20], drilling [21], pump manufacturing [22], plastic manufacturing [23], electronic components manufacturing [24], and brick production [25]. REBA has also been used in the analysis of manual picking processes [26] and the development of work rotation schedules [27]. Rosecrance, Paulsen and Murgia [28] used the Strain Index and OCRA to measure the risk index in cheese processing activities. Rapid Office Strain Assessment (ROSA) [29] is a new office risk assessment method used to estimate the hazards connected with computer work. Based on complaints of discomfort connected with office work, this technology would offer information to the user about the need for change. In a call centre workplace, Poochada and Chaiklieng [30] demonstrated the use of ROSA to assess the existence of risk factors for work-related musculoskeletal diseases (WRMSD). Borah [31] carried out a study to determine the drudgery of women cashew nut factory employees in respect of physiological reactions, work-related musculoskeletal problems, and health risk factors. For the evaluation of Advanced Manufacturing Technology (AMT), Macias, Alcaraz, Reyes and Hernández [32] established a list of ergonomic characteristics such as physical workspace compatibility, human skills and training compatibility, usability, physical workspace compatibility, equipment emission requirements, and equipment design organisational requirements. Ergonomic compatibility evaluation on the selection of AMT is done using a fuzzy axiomatic design approach. The Postural Ergonomic Risk Assessment (PERA) technique was introduced by Chander and Cavatorta [33] to assess the postural ergonomie risk of short cyclic assembly activity. Maman, Yazdi and Cavuoto [34] investigated the direct approach, which allows data to be collected directly from sensors connected to the worker's body. However, it is difficult to apply in real-world settings [35]. Kong [36] has presented a framework model for the ergonomic evaluation of workstations that include both physical and cognitive loads, which is important information for performance prediction, job assignment, operator selection and training, and work organisation.

Despite the fact that there are quantification techniques for ergonomics, according to the literature review, these tools have only been employed to analyse physiological or psychological variables. Ergonomic evaluation appears to make a limited research contribution. A systematic evaluation technique for evaluating workplace ergonomics is thus required.

2.1. Ergonomie assessment model

Table 1 shows the conceptual framework for ergonomic measurement that was used in this study. The ergonomic evaluation model contains 15 criteria, and is divided into three stages. There were four enablers in the first stage. The second stage comprises 15 ergonomic requirements, while the third stage contains 40 ergonomic features [37].

 

3. METHODOLOGY

Figure 1 shows the research methodology used in this study. A conceptual model was established, based on the literature review. It was organised into three levels: enablers, criteria, and attributes. Workers were asked to assess the performance of ergonomic characteristics and their significance weights using linguistic indicators. A triangular fuzzy number was used to approximate the linguistic variables. The fuzzy ergonomic index (FEI) was calculated after three stages of computation had been completed, using fuzzy procedures. Using the Euclidean distance technique, the ergonomic level of the organisation was assessed by matching the FEI with the linguistic terms. The fuzzy performance importance index FPII was used to determine obstacles to further improvement. The case organisation would take the necessary steps to resolve the issues.

 

 

4. CASE STUDY

4.1. About the case study

XYZ is a limited liability partnership (LLP) firm manufacturing brake and clutch shoes for two-wheelers. The company operates with three major units: a casting unit, a rubber liner unit, and an assembly unit, all meeting the demand for 70,000 units of brake shoes and clutches. Among the various brake shoe and clutch models offered, the ABC brake shoe was chosen for analysis, as it contributed 56 percent of the company's sales. The study was conducted in the casting unit, which involved the following process: weighing aluminium bars, melting aluminium bars, casting, fettling (the outer and side parts), fettling (the inner part), drilling, and shot blasting.

4.2. Application of fuzzy logic in ergonomic assessment

The fuzzy ergonomic index of an organisation is represented by 'FEI'.

Equation 1 for the ergonomic index is:

where Ri is the ergonomic capabilities performance index and Wi is the ergonomic capabilities importance weight.

The performance rating of the ergonomic capabilities and the important weights of the ergonomic capabilities were investigated by choosing the linguistic variables. A set of fuzzy integers was developed to estimate the values of the linguistic variables [42], as illustrated in Table 2. Workers used linguistic terms to score their performance and to estimate the significance of their ergonomic capabilities. The workers decided on the ratings and weights after a question-and-answer session. A median operation was used to aggregate the workers' assessments, since it is more consistent with a small sample [42]. Table 3 shows the results, the integrated performance ratings, and the integrated importance weights of the ergonomic capabilities that were examined using linguistic variables to assess the casting unit's aluminium bar process. The linguistic terms in Table 2 were then converted into fuzzy numbers using the connection between the linguistic words and the fuzzy numbers, as illustrated in Table 4.

4.2.1. Evaluation of primary assessment

Equation 2 was used to calculate the primary assessment EQj [41]:

where

• EC_ij is the ergonomic capabilities of j^th criterion in i^th enabler,

• Wijk is the importance weight of k^th attribute in j^th criterion in i^th enabler and

• R_ijk is the performance rating of k^th attribute in j^th criterion in i.

The following is the model calculation for the 'bio-mechanical aspects' criteria. Here, the values of i and j are 1, and k takes the value 1 to 5. EC11 Ergonomics capability of the first criterion in the first enabler:

Using the same principle, the index for the various ergonomic criteria was calculated, and is shown in Table 5.

4.2.2. Evaluation of secondary assessment

Equation 2 was used to determine the organisation's secondary assessment. Below is the model calculation for the 'physiological factor' enabler.

EC₁Ergonomic capability of the first enabler

The ergonomic indices for the other enablers were computed using the same method, and are listed in Table 5.

4.2.3. Evaluation of tertiary assessment

By applying Equation 2, the FEI of the organisation, representing the overall enterprise ergonomic level, was calculated.

The appropriate level had to be assigned to the FEI. There were several approaches to determining the ergonomic level; the Euclidean distance approach, piecewise decomposition, and successive approximation were the three main approaches [43]. The most often used distance approach is the Euclidean distance method, which is the geometric distance between two points in a three-dimensional space. The major benefit of the Euclidean technique over other methods is that the distance between any two items remains unchanged when additional objects are added to the analysis.

Using the Euclidean distance method, the natural-language expression set LL= {excellent [EE], very good [VG], good [G], fair [F], poor [P]} was selected for labelling [42]. Figure 2 shows the linguistic and associated membership functions. The Euclidean distance D from the FEI to each member in set LL was then computed as follows, using the Euclidean distance technique (Equation 3):

 

 

 

where:

• D(FEI,ELi) is the Euclidean distance between FEI and ELi,

• FEI is the fuzzy ergonomic index,

• LL_i is the corresponding fuzzy number for natural language expression,

• fFEI(x) is the FEI triangular fuzzy number,

• fLLI(x) is the LLi triangular fuzzy number and

• x is the lower, middle, and upper triangular numbers.

D (FEI, EE) = IMAGEMAQU

D(FEl,VG) = 2.23 D(FF/,G) = 1.33 D(FF/,F) = 4.75 D(FF/,P) = 7.36

 

5. RESULTS AND DISCUSSION

The ergonomics level was classified as 'good' by matching the linguistic variables with a minimal D. The approach presented above was used to determine not just the ergonomic level, but also the major barriers. The FPII of the ergonomic capability, which combined the performance rating and the important weight of each ergonomic element capability, provided an effect that would contribute to the ergonomic level of an organisation. The smaller a factor's FPII, the less it contributed [17]. The transformation [(1, 1, 1) - W_ijk] was low when W'_ijk was large. As a result, the FPII_ijk for each ergonomic element capability was defined as:

where:

• FPII_ijk is the FPII for the ijk^th attribute,

• Wijk is the complement of ijk^th attribute's importance weight,

• and W'_ijk = [(1, 1, 1)"W_ijk] where W_ijks are the fuzzy importance weight of the EC_ijk [41].

Then the FPIIs of each ergonomic element capability were computed using Equation 4. The model calculation for the FPII₁₁₁attribute is shown below:

The remaining attributes' FPIIs were determined using the same method and is presented in Table 6. Because fuzzy numbers may not always provide a completely ordered collection in the same way that real numbers do, all FPIIs must be rated [44,45]. The fuzzy number was ranked using the centroid technique for membership function (a, b, c) in Equation 5, where a, b, and c are the lower, middle, and upper values of the triangle fuzzy number respectively.

 

 

The model calculation for the FPII₁₁₁ attribute is shown below:

Table 6 gives the ranking score for four enablers, based on the above-mentioned concept for the remaining attributes. The management threshold of scale three was created to determine which major barriers needed to be improved in order to identify the important obstacles. After calculating the results, it was revealed that eight capabilities performed worse than the management threshold limit. Table 7 shows the overall fuzzy score for the process, and the result is shown in Figure 2.

 

 

Table 8 shows the problem that was identified and the suggested solutions. The case organisation has since taken appropriate steps to enhance the weaker capabilities. Ergonomic performance indicators such as the productivity rate are measured before and after the ergonomic assessment task is implemented, as shown in Table 9. The findings in Table 9 show a substantial improvement in ergonomics following the implementation of the identified changes.

 

6. CONCLUSION

Ergonomics is the study of how to design systems so that humans may interact with them in a comfortable way. Since the 1950s, the number of workers in secondary industries has increased dramatically, currently accounting for more than 60% of all workers. The need for ergonomics has evolved over time as the industrial structure has changed drastically. Ergonomics enhances output quality and productivity. A failure to use ergonomics in the workplace not only decreases productivity but also leaves employees' health and safety at risk. A conceptual model for ergonomic evaluation was created as a result of the research that was carried out. Manufacturing companies could use the ergonomic assessment to establish their score on the ergonomic scale, and to identify areas where improvements could be made. To address disadvantages such as vagueness, uncertainty, and ambiguity, a fuzzy logic method was used. The ergonomic level of the organisation was determined using the FEI and the Euclidean distance technique; FPII was used to determine the weaker characteristics in addition to FEI. Our study found eight out of 40 characteristics to be inadequate. The improvement measures are being implemented in the organisation. Also, before and after executing the ergonomic assessment exercise, improvements in performance indicators such as cycle time and production rate were measured. Following the implementation of the recommendations for improvement, there was a significant improvement. The 15-criteria ergonomic model was implemented in a single manufacturing company, indicating that similar techniques might be used in an industrial scenario. In the future, further research across many manufacturing companies might be done to increase the model's performance.

 

REFERENCES

[1] Resnick, M. L. & Zanotti, A. 1997. Using ergonomics to target productivity improvements. Computers & Industrial Engineering, 33(12), pp. 185-188.         [ Links ]

[2] Shikdar, A., Al-araimi, S. & Omurtag, B. 2002. Development of a software package for ergonomic assessment of manufacturing industry. Computers & Industrial Engineering, 43(3), pp. 485-493.         [ Links ]

[3] Labuttis, J. 2015. Ergonomics as element of process and production optimization. Procedia Manufacturing, 3, pp. 4168-4172.         [ Links ]

[4] Botti, L., Mora, C. & Regattieri, A. 2017. Integrating ergonomics and lean manufacturing principles in a hybrid assembly line. Computers & Industrial Engineering, 111, pp. 481-491.         [ Links ]

[5] Dos Santos, Z.G., Vieira, L. & Balbinotti, G. 2015. Lean manufacturing and ergonomic working conditions in the automotive industry. Procedia Manufacturing, 3, pp. 5947-5954.         [ Links ]

[6] Parsons, K.C. 2000. Environmental ergonomics: A review of principles, methods and models. Applied Ergonomics, 31(6), pp. 581-594.         [ Links ]

[7] Grzybowski, W. 2001. A method of ergonomic workplace evaluation for assessing occupational risks at workplaces. International Journal of Occupational Safety & Ergonomics, 7(2), pp. 223-237,         [ Links ]

[8] Vieira, E.R.& Kumar, S. 2004. Working postures: A literature review. Journal of Occupuational Rehabilation, 14(2), pp. 143-159.         [ Links ]

[9] O'Neill, D. 2005. The promotion of ergonomics in industrially developing countries. International Journal of Industrial Ergonomics, 35(2), pp. 163-168.         [ Links ]

[10] Keyserling, W.M., Brouwer, M. & Silverstein, B.A. 1992. A checklist for evaluating ergonomic risk factors resulting from awkward postures of the legs, trunk and neck. International Journal of Industrial Ergonomics, 9(4), pp. 283-301.         [ Links ]

[11] Kuorinka, I., Jonsson, B., Kilbom, A., Vinterberg, H., Biering-Sorensen, F., Andersson, G. & Jorgensen, K. 1987. Standardised Nordic questionnaires for the analysis of musculoskeletal symptoms. Applied Ergonomics, 18(3), pp. 233-237.         [ Links ]

[12] M., Kadefors, R. & Örtengren, R. 2005. ErgoSAM: A preproduction risk identification tool. Human Factors& Ergonomics in Manufacturing, 15(3), pp. 309-325.         [ Links ]

[13] Nejad, N.H., Choobineh, A., Rahimifard, H., Haidari, H.R. & Reza Tabatabaei, S.H. 2013. Musculoskeletal risk assessment in small furniture manufacturing workshops. International Journal of Occupational Safety and Ergonomics, 19(2), pp. 275-284.         [ Links ]

[14] Lin Y.H., Chen C.Y. & Pan Y.T. 2013. The suitability for the work-related musculoskeletal disorders checklist assessment in the semiconductor industry: A case study. Human Factors & Ergonomics in Manufacturing, 23(3), pp. 222-229.         [ Links ]

[15] Karhu, O., Kansi, P.& Kuorinka, I. 1977. Correcting working postures in industry: A practical method for analysis. Applied Ergonomics, 8(4), pp. 199-201. http://dx.doi.org/10.1016/0003-6870(77)90164-8        [ Links ]

[16] McAtamney, L. & Corlett, E.N. 1993. RULA: A survey method for the investigation of work-related upper limb disorders. Applied Ergonomics, 24, pp. 91 -99.         [ Links ]

[17] Hignett, S.&McAtamney, L. 2000. Rapid entire body assessment (REBA). Applied Ergonomics, 31 (2), pp. 201 -205.         [ Links ]

[18] Moore, J.S. & Garg, A. 1995. The strain index: A proposed method to analyze jobs for risk of distal upper extremity disorders. American Industrial Hygiene Association Journal, 56(5), pp. 443-458.         [ Links ]

[19] Oeehipinti, E. 1998. OCRA: A concise index for the assessment of exposure to repetitive movements of the upper limbs. Ergonomics, 41(9), pp. 1290-1311.         [ Links ]

[20] Shah, Z.A., Amjad, A., Ashraf, M., Mushtaq, F. & Sheikh, I.A. 2016. Prevalence of musculoskeletal problems and awkward posture in a Pakistani garments manufacturing industry. Malaysian Journal of Public Health Medicine, 1, pp. 75-79.         [ Links ]

[21] Alabdulkarim, S., Nussbaum, M.A., Rashedi, E., Kim, S., Agnew, M. & Gardner, R. 2017. Impact of task design on task performance and injury risk: Case study of a simulated drilling task. Ergonomics, 60(6), pp. 851-866.         [ Links ]

[22] Binoosh, S.A., Madhan Mohan, G., Ashok, P.& Dhana Sekaran, K. 2017. Virtual postural assessment of an assembly work in a small scale submersible pump manufacturing industry. Work, 58(4), pp. 567-578.         [ Links ]

[23] Mohamed, M.S.S., Halim, I., Azani, A.H.& Saptari, A. 2019. Work posture improvement at plastic printing process in plastic manufacturing industry. Journal of Advanced Manufacturing Technology, 13(3), pp. 25-36.         [ Links ]

[24] Daneshmandi, H., Kee, D., Kamalinia, M., Oliaei, M. & Mohammadi, H. 2019. An ergonomic intervention to relieve musculoskeletal symptoms of assembly line workers at an electronic parts manufacturer in Iran. Work, 61(4), pp. 515-521.         [ Links ]

[25] Ijaz, M., Ahmad, S.R., Akram, M., Khan,W.U., Yasin, N.A. & Nadeem, F.A. 2020. Quantitative and qualitative assessment of musculoskeletal disorders and socioeconomic issues of workers of brick industry in Pakistan. International Journal of Industrial Ergonomics, 76,102933.         [ Links ]

[26] Hanson, R., Medbo, L., Berlin, C. & Hansson, J. 2018. Manual picking from flat and tilted pallet containers. International Journal of Industrial Ergonomics, 64, pp. 199-212.         [ Links ]

[27] Yoon, S.Y., Ko, J. & Jung, M.C. 2016. A model for developing job rotation schedules that eliminate sequential high workloads and minimize between-worker variability in cumulative daily workloads: Application to automotive assembly lines. Applied Ergonomics, 55, pp. 8-15.         [ Links ]

[28] Roseeranee, J., Paulsen, R.& Murgia, L. 2017. Risk assessment of cheese processing tasks using the Strain index and OCRA checklist. International Journal of Industrial Ergonomics, 61, pp. 142-148.         [ Links ]

[29] Sonne, M., Villalta, D. L. & Andrews, D.M. 2012. Development and evaluation of an office ergonomic risk checklist: ROSA - Rapid office strain assessment. Applied Ergonomics, 43(1), pp. 98-108. https://doi.org/10.1016/j.apergo.2011.03.008        [ Links ]

[30] Pooehada, W. & Chaiklieng, S. 2015. Ergonomic risk assessment among call center workers. Procedia Manufacturing, 3, pp. 4613-4620.         [ Links ]

[31] Borah, S. 2015. Ergonomic assessment of drudgery of women worker involved in cashew nut processing factory in Meghalaya, India. Procedia Manufacturing, 3, pp. 4665-4672.         [ Links ]

[32] Maldonado-Maeías, A. Gareía-Alearaz, J., Reyes, R.M. & Hernandez, J. 2015. Application of a fuzzy axiomatic design methodology for ergonomic compatibility evaluation on the selection of plastic molding machines: A case study. Procedia Manufacturing, 3, pp. 5769-5776.         [ Links ]

[33] Chander, D.S. & Cavatorta, M.P. 2017. An observational method for postural ergonomic risk assessment. International Journal of Industrial Ergonomics, 57, pp. 32-41.         [ Links ]

[34] Maman, S.Z, Yazdi, M.A. & Cavuoto, L. 2017. A data-driven approach to modelling physical fatigue in the workplace using wearable sensors. Applied Ergonomics, 65, pp. 515-529.         [ Links ]

[35] Plantard, P., Muller, A., Pontonnier, C., Dumont, G., Shum H.P.H. & Multon, F. 2017. Inverse dynamics based on occlusion-resistant Kinect data: Is it usable for ergonomics?. International Journal of Industrial Ergonomics, 61, pp. 71-80.         [ Links ]

[36] Kong, F. 2019.Development of metric method and framework model of integrated complexity evaluations of production process for ergonomics workstations. International Journal of Production Research, 57(8), pp. 2429-2445.         [ Links ]

[37] Kamala, V., Malliga, P. & Priyanka, G.M. 2016. Criteria based ergonomic assessment in a manufacturing industry. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). pp. 238-242.         [ Links ]

[38] Salvendy, G. 2012. Handbook of human factors and ergonomics. New Jersey: John Wiley & Sons.         [ Links ]

[39] Bridger, R.S. 2008. Introduction to ergonomics, 3^rded. London: CRC Press.         [ Links ]

[40] Botti, L., Mora, C. & Regattieri, A. 2017. Integrating ergonomics and lean manufacturing principles in a hybrid assembly line. Computers and Industrial Engineering, 111, pp. 481 -491.         [ Links ]

[41] Siemieniuch, C.E. & Sinclair, M. 2014. Extending systems ergonomics thinking to accommodate the socio-technical issues of systems of systems. Applied Ergonomics, 45(1), pp. 85-98.         [ Links ]

[42] Lin, C.T., Chiu, H. & Tseng, Y.H. 2006. Agility evaluation using fuzzy logic. International Journal of Production Economics, 101, pp. 353-368.         [ Links ]

[43] Guesgen, H.W.& Albrecht, J. 2000. Imprecise reasoning in geographic information systems. Fuzzy Sets and Systems, 113(1), pp. 121-131.         [ Links ]

[44] Ozfirat, M. P. 2020. A fuzzy event tree methodology modified to select and evaluate suppliers. South African Journal of Industrial Engineering, 31 (1 ), pp. 35-46.         [ Links ]

[45] Yetkin, M.E., Simsir, F.,Ozfirat,M.K., Ozfirat, P.M&Yenice, H. 2016. A fuzzy approach to selecting roof supports in longwall mining. South African Journal of Industrial Engineering, 27(1), pp. 162177.         [ Links ]

 

 

Submitted by authors 11 Sep 2021
Accepted for publication 5 Sep 2022
Available online 14 Dec 2022

 

 

* Corresponding author: kamala@annauniv.edu
ORCID® identifiers
V. Kamala: 0000-0002-2456-9002
T. Paul Robert: 0000-0002-5642-3470