Skip to main content

Fuzzy logic-based decision-making system design for safe forklift truck speed: cast cobblestone production application

Soft Computing volume 24, pages 14907–14920 (2020)Cite this article

Abstract

This paper presents a model in which “fuzzy logic multi-criteria decision-making method” is suggested to determine real-time forklift speed to reduce occupational accidents caused by operators. The model developed in the study uses the variables: the weight and height of the load carried by the forklift, the number of products on its pallet, the places with high risk of accident, and the wet-dry condition of the ground. In order to evaluate the performance of the suggested model, a data set comprises 128 different conditions in cast cobblestone production. Determined forklift speeds were compared with the forklift speeds determined by fuzzy logic using statistically analyses. Results showed that fuzzy logic model has a high accuracy and low error. Fuzzy logic modeling has proved to be a good way to decide the real-time speed of the forklifts being used in production without compromising occupational safety. Friedman test and Wilcoxon test have been used to estimate the significance of fuzzy logic method. The fuzzy logic results showed that our method achieved better results compared to beginner operator.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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

References

  • Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Google Scholar 

  • Dinler G (2000) Forklift maintenance and operation manual. IOP Publishing İstanbul Medical Chamber. https://www.iyh.istabip.org.tr/sirer/im/1.pdf. Accessed 13 Feb 2017

  • Ersoy M (2015) A proposal on occupational accident risk analysis: a case study of a marble factory. J Hum Ecol Risk Assess Int J 21(8):2099–2125

    Google Scholar 

  • Fan GF, Peng LL, Hong WC (2018) Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model. Appl Energy 224:13–33

    Google Scholar 

  • Galal AA, Tayfour A (2012) Characteristics and prediction of traffic accident casualties in sudan using statistical modeling. Int J Transp Sci Technol 1(4):305–317

    Google Scholar 

  • García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inform Sci 180(10):2044–2064

    Google Scholar 

  • Gnoni MG, Lettera G, Bragatto PA (2012) A multi-criteria decision model for controlling knock-on risk inside chemical plants. Chem Eng Commun 199(6):798–811

    Google Scholar 

  • Hall L (1996) Forklift fatalities. Safety Meeting Outline, SMO 96-0601. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A433488&dswid=-2103. Accessed 11 Aug 2011

  • Holčapek M, Turčan M (2003) A structure of fuzzy systems for support of decision making. Soft Comput 7:234–243

    MATH  Google Scholar 

  • Hong WC, Li MW, Geng J, Zhang Y (2019) Novel chaotic bat algorithm for forecasting complex motion of floating platforms. Appl Math Model 72:425–443

    MathSciNet  MATH  Google Scholar 

  • Horberry T, Larsson TJ, Johnston I, Lambert J (2004) Forklift safety, traffic engineering and intelligent transport systems: a case study. Appl Ergon 35:575–581

    Google Scholar 

  • Horberry T, Gunatilaka A, Regan M (2006) Intelligent transport systems for industrial mobile safety. J Occup Health Saf 22(4):323–334

    Google Scholar 

  • Imriyas K, Pheng LS, Lin TA (2011) A decision support system for predicting accident risks in building projects. Archit Sci Rev 50(2):149–162

    Google Scholar 

  • Jana DK, Pramanik S, Sahoo P, Mukherjee A (2019) Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries. Soft Comput 23:557–567

    Google Scholar 

  • Kaya G, Erkaymaz O, Sarac Z (2018) A new adaptive neuro-fuzzy solution for optimization of the parameters in the digital holography setup. Methodol Appl 2(1):1–11

    Google Scholar 

  • Koçar O, Dizdar EN, Çetiner E (2016) Trend analysis of forklift truck accidents. 8. In: International conference on safety and health, 8–11 May, İstanbul, Turkey

  • Kumar P, Bauer P (2009) Progressive design methodology for complex engineering systems based on multiobjective genetic algorithms and linguistic decision making. Soft Comput 13:649–679

    Google Scholar 

  • Larsso TJ, Rechnıtzerb G (1994) Forklift trucks—analysis of severe and fatal occupational injuries, critical incidents and priorities for preventions. Saf Sci 17:275–289

    Google Scholar 

  • Lekka C, Okunribido O (2010) A review of workplace transport safety and HSE commissioned work on manual handling and delivery of goods. Health and Safety Laboratory, Health and Safety Executive (HSE), Workplace Transport Programme

  • Li DC, Liu CW, Hu SC (2011) A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artif Intell Med 52:45–52

    Google Scholar 

  • Lifschultz BD, Donoghue ER (1994) Deaths due to forklift truck accidents. Forensic Sci Int 65:121–134

    Google Scholar 

  • Lovested G (1997) Top ten forklift accidents. Nat Saf News 116:123–127

    Google Scholar 

  • Lu J, Zhang G, Ruan D (2008) Intelligent multi-criteria fuzzy group decision-making for situation assessments. Soft Comput 12:289–299

    MATH  Google Scholar 

  • Ma H, Li X, Liu Y (2019) Multi-period multi-scenario optimal design for closed-loop supply chain network of hazardous products with consideration of facility expansion. Soft Comput 24:2769–2780. https://doi.org/10.1007/s00500-019-04435-z

    Article  Google Scholar 

  • Milanowicz M, Budziszewski P, Kedzior K (2018) Numerical analysis of passive safety systems in forklift trucks. Saf Sci 10:98–107

    Google Scholar 

  • Miller BC (1998) Forklift safety by desing. Prof Saf 33:18–21

    Google Scholar 

  • Mueller SJ, Haupt RO, Haupt DG, Kempfert LA (1997) Travel speed limiting system for forklift trucks. Patent-US005652486A, USA

  • MUARC OHS Group (2011) Forklift literature review. IOP Publishing Dijitala Vetensapliga Arkivet. https://www.diva-portal.org/smash/get/diva2:433488/FULLTEXT01.pdf. Accessed 08 Aug 2011

  • Naieni JSGhR, Makui A, Ghousi R (2012) An approach for accident forecasting using fuzzy logic rules: a case mining of lift truck accident forecasting in one of the Iranian car manufacturers. Int J Ind Eng Prod Res 23(1):53–64

    Google Scholar 

  • Naim S, Hagras H (2013) A type 2-hesitation fuzzy logic based multi-criteria group decision making system for intelligent shared environments. Soft Comput 18:1305–1319

    Google Scholar 

  • Perttula P, Salminen S (2015) Workplace accidents in materials transfer in Finland. Int J Occup Saf Ergon 18(4):541–548

    Google Scholar 

  • Raj A, Gautam G, Abdullah SNHS, Zaini AS, Mukhopadhyay S (2019) Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy. Image Vis Comput 91:1–14

    Google Scholar 

  • Rebelle J, Mistrot P, Poirot P (2009) Development and validation of a numerical model for predicting forklift truck tip-over. J Veh Syst Dyn Int J Veh Mech Mobil 47(7):771–804

    Google Scholar 

  • Rhodes C, Myrtle RA (1999) Speed controller. Patent-WO2001042868A1, USA

  • Saric S, Hadiashar AB (2013) Analysis of forklift accident trends within Victorian industry (Australia). Saf Sci 60:176–184

    Google Scholar 

  • Takagi T, Sujgeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst 15:116–132

    Google Scholar 

  • Wirgand NS (2002) Characteristics of work-related injuries involving forklift trucks. J Saf Res 18:179–190

    Google Scholar 

  • Woldt W, Dvorak B, Dahab M (2003) Application of fuzzy set theory to industrial pollution prevention: production system modeling and life cycle assessment. Soft Comput 7:419–433

    Google Scholar 

  • Yuen KK, Choi SH, Yang B (2010) A Full-immersive CAVE-based VR Simulation System of Forklift Truck Operations for Safety Training. J Comput Aided Des Appl 7(2):235–245

    Google Scholar 

  • Zadeh LA (1975) Calculus of fuzzy restrictions. In: Zadeh LA, Fu KS, Tanaka K, Shimura M (eds) Fuzzy sets and their applications to cognitive and decision processes. Academic Press, New York, pp 1–39

    Google Scholar 

  • Zhang ZC, Hong WC (2019) Electric load forecasting by complete ensemble empirical model decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dyn 98:1107–1136. https://doi.org/10.1007/s11071-019-05252-7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Occupational Safety and Health Department, Yapraklı Vocational High School, Çankırı Karatekin University, Çankırı, Turkey

    Ercüment Neşet Dizdar

  2. Mechatronics Engineering Department, Faculty of Engineering, Bülent Ecevit University, Zonguldak, Turkey

    Oğuz Koçar

Authors
  1. Ercüment Neşet Dizdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oğuz Koçar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oğuz Koçar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dizdar, E.N., Koçar, O. Fuzzy logic-based decision-making system design for safe forklift truck speed: cast cobblestone production application. Soft Comput 24, 14907–14920 (2020). https://doi.org/10.1007/s00500-020-04843-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04843-6

Keywords

  • Fuzzy logic application
  • Safely forklift speed
  • Forklift accident
  • Fuzzy decision-making
  • Safely logistics