Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science » Journal of Project Management » Fuzzy inference system-Latin hypercube simulation: An integrated hybrid model for OHS risks management

Journals

  • IJIEC (726)
  • MSL (2637)
  • DSL (649)
  • CCL (495)
  • USCM (1092)
  • ESM (404)
  • AC (557)
  • JPM (247)
  • IJDS (912)
  • JFS (91)
  • HE (21)

JPM Volumes

    • Volume 1 (8)
      • Issue 1 (5)
      • Issue 2 (3)
    • Volume 2 (13)
      • Issue 1 (4)
      • Issue 2 (3)
      • Issue 3 (3)
      • Issue 4 (3)
    • Volume 3 (17)
      • Issue 1 (4)
      • Issue 2 (5)
      • Issue 3 (4)
      • Issue 4 (4)
    • Volume 4 (24)
      • Issue 1 (4)
      • Issue 2 (8)
      • Issue 3 (8)
      • Issue 4 (4)
    • Volume 5 (20)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • Volume 6 (20)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • Volume 7 (21)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (6)
    • Volume 8 (21)
      • Issue 1 (6)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)
    • Volume 9 (35)
      • Issue 1 (6)
      • Issue 2 (5)
      • Issue 3 (9)
      • Issue 4 (15)
    • Volume 10 (68)
      • Issue 1 (15)
      • Issue 2 (21)
      • Issue 3 (13)
      • Issue 4 (19)

Keywords

Jordan(161)
Supply chain management(160)
Vietnam(148)
Customer satisfaction(120)
Performance(113)
Supply chain(108)
Service quality(98)
Tehran Stock Exchange(94)
Competitive advantage(93)
SMEs(86)
optimization(84)
Financial performance(83)
Trust(81)
Job satisfaction(79)
Social media(78)
Factor analysis(78)
TOPSIS(78)
Knowledge Management(77)
Genetic Algorithm(76)
Sustainability(76)


» Show all keywords

Authors

Naser Azad(82)
Mohammad Reza Iravani(64)
Zeplin Jiwa Husada Tarigan(59)
Endri Endri(45)
Muhammad Alshurideh(42)
Hotlan Siagian(39)
Jumadil Saputra(36)
Muhammad Turki Alshurideh(35)
Dmaithan Almajali(35)
Barween Al Kurdi(32)
Basrowi Basrowi(31)
Hassan Ghodrati(31)
Ahmad Makui(31)
Mohammad Khodaei Valahzaghard(30)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Prasadja Ricardianto(28)
Sautma Ronni Basana(27)
Haitham M. Alzoubi(27)


» Show all authors

Countries

Iran(2162)
Indonesia(1276)
Jordan(783)
India(779)
Vietnam(500)
Saudi Arabia(438)
Malaysia(438)
United Arab Emirates(220)
China(181)
Thailand(151)
United States(109)
Turkey(102)
Ukraine(99)
Egypt(95)
Canada(89)
Pakistan(84)
Peru(83)
United Kingdom(77)
Nigeria(77)
Morocco(73)


» Show all countries

Journal of Project Management

ISSN 2371-8374 (Online) - ISSN 2371-8366 (Print)
Quarterly Publication
Volume 4 Issue 2 pp. 127-140 , 2019

Fuzzy inference system-Latin hypercube simulation: An integrated hybrid model for OHS risks management Pages 127-140 Right click to download the paper Download PDF

Authors: Ehsan Haqiqat, Yahia Zare Mehrjerdi, Ali Zare Bidaki

DOI: 10.5267/j.jpm.2018.11.001

Keywords: Occupational Health and Safety, Healthcare System, Construction Projects, Risk Evaluation, Risk management, sensitivity analysis, Integrated hybrid model

Abstract: Risk management in construction industry in several cases is not only incomplete regarding the unification of Occupational Health and Safety (OHS) hazards, but it is also incomplete in not having a systematic and innovative method to assess the impacts of these risks on the objectives of a project. An integrated hybrid Fuzzy Inference System-Latin Hypercube Simulation for the evaluation of OHS risks in construction projects is presented in this paper. Prioritization of safety risks systematically without human interference with fuzzy inference system gives the appropriate response to the identified risks. An advanced Monte Carlo simulation is also used for the evaluation of quantitative objectives of a project. This approach allows us to get away from discrimination and simulate the risks with high impacts but with low probabilities. In order to measure the relationship between the occurrences of each of the risks impacts on project objectives, the sensitivity analysis based on Pearson correlation coefficient is used to determine the usefulness of the proposed integrated hybrid method.

How to cite this paper
Haqiqat, E., Mehrjerdi, Y & Bidaki, A. (2019). Fuzzy inference system-Latin hypercube simulation: An integrated hybrid model for OHS risks management.Journal of Project Management, 4(2), 127-140.

Refrences
Aminbakhsh, S., Gunduz, M., & Sonmez, R. (2013). Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects. Journal of safety research, 46, 99-105.
Aubert, B. A., & Bernard, J.-G. (2004). Mesure intégrée du risque dans les organisations: PUM.
Badri, A., Gbodossou, A., & Nadeau, S. (2012). Occupational health and safety risks: Towards the integration into project management. Safety Science, 50(2), 190-198.
Badri, A., Nadeau, S., & Gbodossou, A. (2012). Proposal of a risk-factor-based analytical approach for integrating occupational health and safety into project risk evaluation. Accident Analysis & Prevention, 48, 223-234.
Baradan, S., & Usmen, M. A. (2006). Comparative injury and fatality risk analysis of building trades. Journal of Construction Engineering and Management, 132(5), 533-539.
Benjaoran, V., & Bhokha, S. (2010). An integrated safety management with construction management using 4D CAD model. Safety Science, 48(3), 395-403.
Dement, J. M. (1999). Workers' compensation experience of North Carolina residential construction workers, 1986-1994. Applied Occupational and Environmental Hygiene, 14(2), 97-106.
Deshmukh, A., & Romine, J. (1998). Assessing the risk of management fraud using red flags: a fuzzy number based spreadsheet approach. Journal of Accounting and Computers, 4(3), 5-15.
Gürcanli, G. E., & Müngen, U. (2009). An occupational safety risk analysis method at construction sites using fuzzy sets. International Journal of Industrial Ergonomics, 39(2), 371-387.
Hagigi, M., & Sivakumar, K. (2009). Managing diverse risks: An integrative framework. Journal of International Management, 15(3), 286-295.
Hallowell, M. R. (2008). A formal model for construction safety and health risk management: Oregon State University.
Hare, B., Cameron, I., & Roy Duff, A. (2006). Exploring the integration of health and safety with pre-construction planning. Engineering, Construction and Architectural Management, 13(5), 438-450.
Harms-Ringdahl, L. (2003). Safety analysis: principles and practice in occupational safety: CRC Press.
Hulett, D. (2016). Integrated cost-schedule risk analysis: Routledge.
Iman, R. L., Helton, J. C., & Campbell, J. E. (1981). An approach to sensitivity analysis of computer models: Part I—Introduction, input variable selection and preliminary variable assessment. Journal of Quality Technology, 13(3), 174-183.
INE, I. d. E. (2008). Statistical yearbook of Portugal 2007. 1 vols. Lisboa: Instituto Nacional de Estatística, IP.
Islam, M. S., Nepal, M. P., Skitmore, M., & Attarzadeh, M. (2017). Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projects. Advanced Engineering Informatics, 33, 112-131.
Jannadi, O. A., & Almishari, S. (2003). Risk assessment in construction. Journal of Construction Engineering and management, 129(5), 492-500.
Kautt, G., & Wieland, F. (2001). Modeling the future: the full Monte, the Latin hypercube and other curiosities. Journal of Financial Planning, 14(12), 78-78.
Kendrick, T. (2015). Identifying and managing project risk: essential tools for failure-proofing your project: AMACOM Div American Mgmt Assn.
Lafuente, E., & Abad, J. (2018). Analysis of the relationship between the adoption of the OHSAS 18001 and business performance in different organizational contexts. Safety Science, 103, 12-22.
Lee, S., Halpin, D. W., & Chang, H. (2006). Quantifying effects of accidents by fuzzy-logic-and simulation-based analysis. Canadian Journal of Civil Engineering, 33(3), 219-226.
Leigh, P. J., & Miller, T. R. (1997). Ranking occupations based upon the costs of job-related injuries and diseases. Journal of Occupational and Environmental medicine, 39(12), 1170-1182.
Lipscomb, H. J., Dement, J. M., & Behlman, R. (2003). Direct costs and patterns of injuries among residential carpenters, 1995–2000. Journal of Occupational and Environmental medicine, 45(8), 875-880.
Lipscomb, H. J., Glazner, J. E., Bondy, J., Guarini, K., & Lezotte, D. (2006). Injuries from slips and trips in construction. Applied ergonomics, 37(3), 267-274.
Liu, Z., & Guo, C. (2009). Study on the risks management of construction supply chain. Paper presented at the Service Operations, Logistics and Informatics, 2009. SOLI'09. IEEE/INFORMS International Conference on.
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239-245.
Monarca, D., Cecchini, M., Colantoni, A., Gubiani, R., & Vello, M. (2008). IS Project: a methodology of evaluation for integrated systems in agroindustrial sector. Innovation Technology to Empower Safety, Health and Welfare in Agriculture and Agro-food Systems. Ragusa, 15-17.
Pheng, L. S., & Kwang, G. K. (2005). ISO 9001, ISO 14001 and OHSAS 18001 management systems: integration, costs and benefits for construction companies. Architectural Science Review, 48(2), 145-151.
Pinto, A., Nunes, I. L., & Ribeiro, R. A. (2011). Occupational risk assessment in construction industry–Overview and reflection. Safety Science, 49(5), 616-624.
PMI. (2008). A guide to the project management body of knowledge.
Rozenfeld, O., Sacks, R., Rosenfeld, Y., & Baum, H. (2010). Construction job safety analysis. Safety Science, 48(4), 491-498.
Siegel, P. H., De Korvin, A., & Omer, K. (1995). Applications of fuzzy sets and the theory of evidence to accounting (Vol. 2): Jai Press.
Tong, R., Cheng, M., Zhang, L., Liu, M., Yang, X., Li, X., & Yin, W. (2018). The construction dust-induced occupational health risk using Monte-Carlo simulation. Journal of Cleaner Production.
Van Dorp, J., & Duffey, M. (1999). Statistical dependence in risk analysis for project networks using Monte Carlo methods. International Journal of Production Economics, 58(1), 17-29.
Waehrer, G. M., Dong, X. S., Miller, T., Men, Y., & Haile, E. (2007). Occupational injury costs and alternative employment in construction trades. Journal of Occupational and Environmental medicine, 49(11), 1218-1227.
Wu, W., Yang, H., Chew, D. A., Yang, S.-h., Gibb, A. G., & Li, Q. (2010). Towards an autonomous real-time tracking system of near-miss accidents on construction sites. Automation in Construction, 19(2), 134-141.
Zare Mehrjerdi, Y., & Haqiqat, E. (2015). Developing a conceptual model based upon the Latin Hypercube Sampling for integrating OHS into project risk evaluation. International Journal of Industrial Engineering & Production Research, 26(4), 229-241.
Zio, E. (2013). The Monte Carlo simulation method for system reliability and risk analysis (Vol. 39): Springer.
  • 68
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: Journal of Project Management | Year: 2019 | Volume: 4 | Issue: 2 | Views: 2660 | Reviews: 0

Related Articles:
  • Impacts of change management on risk and cost management of a construction ...
  • An adaptive algorithm for performance assessment of construction project ma ...
  • Risks identification and ranking using AHP and group decision making techni ...
  • Assessment of worker safety in a pharmaceutical industry using FMEA
  • An empirical study for ranking risk factors using linear assignment: A case ...

Add Reviews

Name:*
E-Mail:
Review:
Bold Italic Underline Strike | Align left Center Align right | Insert smilies Insert link URLInsert protected URL Select color | Add Hidden Text Insert Quote Convert selected text from selection to Cyrillic (Russian) alphabet Insert spoiler
winkwinkedsmileam
belayfeelfellowlaughing
lollovenorecourse
requestsadtonguewassat
cryingwhatbullyangry
Security Code: *
Include security image CAPCHA.
Refresh Code

® 2010-2025 GrowingScience.Com