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A new approach for ergonomic risk assessment integrating KEMIRA, best–worst and MCDM methods

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A Correction to this article was published on 05 August 2020

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Abstract

In this study, a new three-phase ergonomic risk assessment approach was proposed for manual lifting tasks to determine which worker has the highest ergonomic risk level considering two criteria sets as lifting-related criteria and human-related criteria. In the first phase, Modified Kemeny Median Indicator Ranks Accordance (KEMIRA-M) and a novel two-dimensional best–worst method (BWM) integration were proposed for weighting ergonomic risk criteria in two sets. In this way, weighting procedure of KEMIRA-M was advanced by the proposed two-dimensional BWM in a consistent manner and subjectivity in determining the best and the worst criteria in traditional BMW was prevented by using KEMIRA-M. Thus, the weaknesses of both methods have been developed. In the second phase, the rankings of workers were determined via utilizing multi-objective optimization on the basis of simple ratio analysis, multi-objective optimization by ratio analysis (MOORA) ratio, MOORA reference point and complex proportional assessment to see how worker rankings differ despite using the same advanced weighting approach based on KEMIRA-M and two-dimensional BWM integration. Finally, to aggregate these different ranking results, technique of precise order preference was applied. In this way, different viewpoints of each ranking approach can be reflected on a single worker’s priority. The applicability of the proposed ergonomic risk assessment approach was demonstrated with a real application in tube manufacturing.

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  • 05 August 2020

    Table 14. Weights of MOORA Ratio, MOORA reference point, MOOSRA and COPRAS methods.

References

  • Abadi F, Sahebi I, Arab A, Alavi A, Karachi H (2018) Application of best-worst method in evaluation of medical tourism development strategy. Decis Sci Lett 7(1):77–86

    Google Scholar 

  • ACGIH (2001) Hand activity level. In: 2001 TLVs and BEIs. ACGIH, Cincinnati, Ohio

  • Ahmadi HB, Kusi-Sarpong S, Rezaei J (2017) Assessing the social sustainability of supply chains using Best Worst Method. Resour Conserv Recycl 126:99–106

    Google Scholar 

  • Arslan N, Delice EK (2018) KEMIRA-M yöntemi ile kişisel kullanicilar için dron seçimi: bir uygulama. Endüstri Mühendisliği Dergisi, Ankara in press

    Google Scholar 

  • Bairagi B, Dey B, Sarkar B, Sanyal SK (2015) A De Novo multi-approaches multi-criteria decision making technique with an application in performance evaluation of material handling device. Comput Ind Eng 87:267–282

    Google Scholar 

  • Bera AK, Janab DK, Banerjeec D, Nandyd T (2020) A group evaluation method for supplier selection based on Interval Type-2 fuzzy TOPSIS method. Int J Bus Perform Supply Chain Model (in press)

  • Brauers WKM, Zavadskas EK (2006) The MOORA method and its application to privatization in a transition economy. Control Cybern 35(2):443–468

    MathSciNet  MATH  Google Scholar 

  • Corlett EN (1981) Pain, posture and performance. Stress Work Des Prod 3:27–42

    Google Scholar 

  • da Costa BR, Vieira ER (2010) Risk factors for work-related musculoskeletal disorders: a systematic review of recent longitudinal studies. Am J Ind Med 53(3):285–323

    Google Scholar 

  • Das MC, Sarkar B, Ray S (2012) Decision making under conflicting environment: a new MCDM method. Int J Appl Decis Sci 5:142–162

    Google Scholar 

  • David G, Woods V, Li G, Buckle P (2008) The development of the Quick Exposure Check (QEC) for assessing exposure to risk factors for work-related musculoskeletal disorders. Appl Ergon 39(1):57–69

    Google Scholar 

  • Gupta H (2018) Evaluating service quality of airline industry using hybrid best worst method and VIKOR. J Air Transp Manag 68:35–47

    Google Scholar 

  • Heart SG, Staveland LE (1986) NASA task load index (TLX). Human Performance Res. Grp. NASA Ames Res. Center, Moffett Field, CA, USA, Computerized Version v1. 0

  • Hignett S, McAtamney L (2000) Rapid entire body assessment (REBA). Appl Ergon 31(2):201–205

    Google Scholar 

  • Javad MOM, Darvishi M, Javad AOM (2020) Green supplier selection for the steel industry using BWM and fuzzy TOPSIS: a case study of Khouzestan steel company. Sustain Futures 2

  • Kaklauskas A, Zavadskas EK, Raslanas S, Ginevicius R, Komka A, Malinauskas P (2006) Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: a Lithuanian case. Energy Build 38:454–462

    Google Scholar 

  • Kemeny JG (1959) Mathematics without numbers. Daedalus 88(4):577–591

    Google Scholar 

  • Kheybari S, Kazemi M, Rezaei J (2019) Bioethanol facility location selection using best-worst method. Appl Energy 242:612–623

    Google Scholar 

  • Kosareva N, Zavadskas EK, Krylovas A, Dadelo S (2016) Personnel ranking and selection problem solution by application of KEMIRA method. Int J Comput Commun Control 11(1):51–66

    Google Scholar 

  • Krylovas A, Zavadskas EK, Kosareva N (2016) Multiple criteria decision-making KEMIRA-M method for solution of location alternatives. Econ Res Ekon istraživanja 29(1):50–65

    Google Scholar 

  • Krylovas A, Zavadskas EK, Kosareva N, Dadelo S (2014) New KEMIRA method for determining criteria priority and weights in solving MCDM problem. Int J Inf Technol Decis Mak 13(06):1119–1133

    Google Scholar 

  • Liao H, Mi X, Yu Q, Luo L (2019) Hospital performance evaluation by a hesitant fuzzy linguistic best worst method with inconsistency repairing. J Clean Prod 23:657–667

    Google Scholar 

  • Lu ML, Waters TR, Krieg E, Werren D (2014) Efficacy of the revised NIOSH lifting equation to predict risk of low-back pain associated with manual lifting: a one-year prospective study. Hum Factors 56(1):73–85

    Google Scholar 

  • Mattila M, Karwowski W, Wilkki M (1993) Analysis of working psotures in hammering tasks on building construction sites using the computerized OWAS method. Appl Ergon 24(6):405–412

    Google Scholar 

  • McAtamney L, Corlett EN (1993) RULA: a survey method for the investigation of work-related upper limb disorders. Appl Ergon 24(2):91–99

    Google Scholar 

  • Mi X, Tang M, Liao H, Shen W, Lev B (2019) The state-of-the-art survey on integrations and applications of the best worst method in decision making: why, what, what for and what’s next? Omega 87:205–225

    Google Scholar 

  • Moore S, Garg A (1995) The strain index: a proposed method to analyze jobs for risk of distal upper extremity disorders. Am Ind Hyg Assoc J 56:443–458

    Google Scholar 

  • Occhipintini E (1998) OCRA: a concise index for the assessment of exposure to repetitive movements of the Upper Limb. Ergonomics 41:1290–1311

    Google Scholar 

  • Pamučar D, Petrović I, Ćirović G (2018) Modification of the best-worst and MABAC methods: a novel approach based on interval-valued fuzzy-rough numbers. Exp Syst Appl 91:89–106

    Google Scholar 

  • Ren J, Liang H, Chan FT (2017) Urban sewage sludge sustainability and transition for eco-city: multi-criteria sustainability assessment of technologies based on best-worst method. Technol Forecast Soc Change 116:29–39

    Google Scholar 

  • Rezaei J (2015) Best- worst multi criteria decision making methods. OMEGA 53:49–57

    Google Scholar 

  • Rezaei J (2016) Best-worst multi-criteria decision-making method: some properties and a linear model. Omega 64:126–130

    Google Scholar 

  • Sarıçalı G (2018) Çok kriterli karar verme yöntemlerinden KEMIRA-M ve COPRAS yöntemlerinin mermer işletmesinde makine seçim sürecine uygulanması. Master’s thesis, Pamukkale Üniversitesi Sosyal Bilimleri Enstitüsü, Turkey

  • Sarıçalı G, Kundakcı N (2017) Forklift Alternatiflerinin KEMIRA-M Yöntemi ile Değerlendirilmesi. Optim J Econ Manag Sci 4(1):35–53

    Google Scholar 

  • Shojaei P, Haeri SAS, Mohammadi S (2018) Airports evaluation and ranking model using Taguchi loss function. Best-worst method and VIKOR technique. J Air Transp Manag 68:4–13

    Google Scholar 

  • Snook SH, Ciriello VM (1991) The design of manual handling tasks: revised tables of maximum acceptable weights and forces. Ergonomics 34(9):1197–1213

    Google Scholar 

  • Toktaş P, Can GF (2018) Şantiyelerin İş Sağlığı ve Güvenliği Açısından Risk Düzeylerine Göre KEMIRA-M Yöntemi ile Sıralanması. Ergonomics 1(3):123–136

    Google Scholar 

  • Toktaş P, Can GF (2019) Stochastic Kemira-M approach with consistent weightings. Int J Inf Technol Decis Mak 18(03):793–831

    Google Scholar 

  • Van Nieuwenhuyse A, Fatkhutdinova L, Verbeke G, Pirenne D, Johannik K, Somville Mairiaux P, Moens GF, Masschelein R (2004) Risk factors for first-ever low back pain among workers in their first employment. Occup Med 54:513–519

    Google Scholar 

  • Washington State Department of Labour Industries. Hazard zone jobs checklist. Retrieved January 24, 2008. http://www.lni.wa.gov/wisha/ergo/evaltools/HazardZoneChecklist.PDF

  • Waters TR, Putz-Anderson V, Garg A, Fine LJ (1993) Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics 36(7):749–776

    Google Scholar 

  • Williams JC (1988). A data-based method for assessing and reducing human error to improve operational performance. In: Conference record for 1988 IEEE fourth conference on human factors and power plants. IEEE, pp 436–450

  • Yazdi AK, Komijan AR, Wanke PF, Sardar S (2020) Oil project selection in Iran: a hybrid MADM approach in an uncertain environment. Appl Soft Comput 88:106066

    Google Scholar 

  • You X, Chen T, Yang Q (2016) Approach to multi-criteria group decision-making problems based on the best-worst-method and ELECTRE method. Symmetry 8(9):95

    MathSciNet  Google Scholar 

  • Zavadskas EK, Turskis Z, Kildienė S (2014) State of art surveys of overviews on MCDM/MADM methods. Technol Econ Dev Econ 20(1):165–179

    Google Scholar 

  • Zhang H, Yin C, Qi X, Zhang R, Kang X (2017) Cognitive best worst method for multiattribute decision-making. Mathemat Probl Eng 2017:11. https://doi.org/10.1155/2017/1092925

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Elif Kılıç Delice.

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The original article has been updated: Due to Table 14 and Equation 11 update.

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Delice, E.K., Can, G.F. A new approach for ergonomic risk assessment integrating KEMIRA, best–worst and MCDM methods. Soft Comput 24, 15093–15110 (2020). https://doi.org/10.1007/s00500-020-05143-9

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