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A Fuzzy Model for Assessing Risk of Occupational Safety in the Processing Industry

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Managing occupational safety in any kind of industry, especially in processing, is very important and complex. This paper develops a new method for occupational risk assessment in the presence of uncertainties. Uncertain values of hazardous factors and consequence frequencies are described with linguistic expressions defined by a safety management team. They are modeled with fuzzy sets. Consequence severities depend on current hazardous factors, and their values are calculated with the proposed procedure. The proposed model is tested with real-life data from fruit processing firms in Central Serbia.
Rocznik
Strony
115--126
Opis fizyczny
Bibliogr. 25 poz., tab., wykr.
Twórcy
autor
  • Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
autor
  • Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
autor
  • Faculty of Mechanical Engineering, University of Belgrade, Belgrade, Serbia
  • Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
  • Faculty of Mechanical Engineering, University of Belgrade, Belgrade, Serbia
Bibliografia
  • 1.Chia ES. Risk assessment framework for project management. In: Proceedings of the Engineering Management Conference, 2006 IEEE International. 2006. p. 376–9.
  • 2.A recipe for safety. Occupational and safety in food and drink manufacture. Sudbury, Suffolk, UK: Health and Safety Executive; 2005. Retrieved May 8, 2012, from: http://www.hse.gov.uk/pubns/priced/hsg252.pdf.
  • 3.Turksen IB, Fazel Zarandi MH. Production planning and scheduling - fuzzy and crisp approaches. In: Zimmermann HJ, editor. Practical applications of fuzzy technologies. Norwell, MA, USA: Kluwer Academic; 1999. p. 479–529.
  • 4.Zadeh LA. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems.1978;100 Suppl 1:9–34.
  • 5.Kaur P, Chakrabortyb S. A new approach to vendor selection problem with impact factor as an indirect measure of quality. Journal of Modern Mathematics and Statistics. 2007;1(1):8–14.
  • 6.Sii HS, Ruxton T, Wang J. A fuzzylogic - based approach to qualitative safety modelling for marine systems. Reliability Engineering & System Safety. 2001;73:19–34.
  • 7.Zeng J, An M, Smith NJ. Application of a fuzzy based decision making methodology to construction project risk assessment. International Journal of Project Management. 2007;25(6):589–600.
  • 8.Markowski AS, Mannan MS, Bigoszewska A. Fuzzy logic for process safety analysis. Journal of Loss Prevention in the Process Industries. 2009;22(6): 695–702.
  • 9.Nieto-Morote A, Ruz-Vila F. A fuzzy approach to construction project risk assessment. International Journal of Project Management, 2011;29(2):220–31.
  • 10.Saaty TL. How to make a decision: the analytic hierarchy process. European Journal Operation Research. 1999;48:9–26.
  • 11.Wang LX. A course in fuzzy systems and control. New York, NY, USA: Prentice Hall PTR; 1997.
  • 12.Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud. 1975;7(1):1–13.
  • 13.Bass SM, Kwakernaak H. Rating and ranking of multiple-aspect alternatives using fuzzy sets. Automatica. 1977;13(1):47–58.
  • 14.Klir GJ, Folger TA. Fuzzy sets, uncertainty, and information. Englewood Cliffs, NJ, USA: Prentice Hall; 1988.
  • 15.Zimmermann HJ. Fuzzy set theory and its applications. Boston, MA, USA: Kluwer Nijhoff; 1996.
  • 16.Dubois D, Prade H. Fuzzy sets and systems: theory and applications. New York, NY, USA: Academic Press; 1980.
  • 17.Kaufmann A, Gupta MM. Introduction to fuzzy arithmetic: theory and applications. New York, NY, USA: Van Nostrand Reinhold; 1991.
  • 18.Robbins SP. Management. 4th ed. Englewood Cliffs, NJ, USA: Prentice Hall; 1994.
  • 19.Pedrycz W, Gomide F. An introduction to fuzzy sets. Analysis and design. Cambridge, MA, USA: MIT Press; 1998.
  • 20.Sadeghpour-Gildeh B, Gien D. La distance - Dp,q et le cofficient de correlation entre deux variables aleatoires floues [Distance-Dp,q and coefficient correlation between two random fuzzy variables]. In: Actes de LFA’01. Mons, Belgium. 2001. p. 97–102.
  • 21.Bas M, Yuksel M, Cavusoglu T. Difficulties and barriers for the implementing of HACCP and food. Food Control. 2007;18(2):124–30.
  • 22.International Organization for Standardization (ISO). Food safety management systems-requirements for any organization in the food chain (Standard No. ISO 22000:2005). Geneva, Switzerland: ISO; 2005.
  • 23.Jacinto C, Canoa M, Guedes Soares C. Workplace and organisational factors in accident analysis within the food industry. Saf Sci. 2009;47(5):626–35.
  • 24.Health and Safety Executive (HSE). Priorities for the fruit and vegetable industry. Food Sheet No. 5. Sudbury, Suffolk, UK: HSE; 1995. Retrieved May 8, 2012, from: http://www.safe2workinternational.com/library/FIS05_Priorities_for_the_fruit_and_vegetable_industry.pdf.
  • 25.Petrovic R. Petrovic D. Multicriteria ranking of inventory replenishment policies in the presence of uncertainty in customer demand. International Journal of Production Economics. 2001;71(1–3):439–46.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a12dfd20-f356-4359-945f-afec016d8840
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