Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 17

Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems

Optimizing Maintenance in Project Management by Considering HSE and Resilience Engineering

, ,

DOI: http://dx.doi.org/10.15439/2018F70

Citation: Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 17, pages 6168 ()

Full text

Abstract. One of the most important objectives of project management is to complete the project within the specified completion date of the project. Another important objective of project is to terminate the project by minimum rate of injuries and damage to the environment. One of the important factors which affect the time objective of the project is the failures or breakdowns of the project Machines and Equipment. Also, HSE factors are crucial in the efficient execution of the project. Resilience engineering is a new concept that will improve the safety and reliability of a high-risk system such as power plant construction project. Previous studies didn't consider the resilience engineering (RE) factors which could help the project to achieve its goals. Related data was collected from a power plant construction project and fuzzy DEA and Z-number DEA were utilized to analyze the Data and Best DEA model is selected according to maximum average efficiency and also for identifying most effective factors sensitivity analysis was done, and we found that flexibility, and project percent progress, system downtime, reporting culture and HSE costs are the most important factors on maintenance of the project. To the best of our knowledge, this is the first study considering RE and HSE factors to optimize maintenance of the project.

References

  1. L. T. T. T. Dinh, H. Pasman, X. Gao, and M. S. Mannan, “Resilience engineering of industrial processes: Principles and contributing factors,” J. Loss Prev. Process Ind., vol. 25, no. 2, pp. 233–241, Mar. 2012.
  2. J. Wreathall, “Properties of Resilient Organizations: An Initial View,” in Resilience engineering: Concepts and precepts, Ashgate, Aldershot, UK, 2006, pp. 275–288.
  3. N. G. L. Erik Hollnagel, David D. Woods, Resilience Engineering: Concepts and Precepts. Ashgate Publishing, Ltd., 2006.
  4. C. P. Nemeth, E. Hollnagel, and S. W. A. Dekker, “Resilience Engineering Perspectives: v 2 Preparation and Restoration,” p. XIX-288 , 2009.
  5. A. Azadeh and V. Salehi, “Modeling and optimizing efficiency gap between managers and operators in integrated resilient systems: The case of a petrochemical plant,” Process Saf. Environ. Prot., vol. 92, no. 6, pp. 766–778, Nov. 2014.
  6. E. Hollnagel and D. D. Woods, “Epilogue: Resilience engineering precepts,” Resil. Eng. Precepts, ..., no. January, pp. 347–358, 2006.
  7. A. Azadeh, V. . Salehi, B. . Ashjari, and M. . Saberi, “Performance evaluation of integrated resilience engineering factors by data envelopment analysis: The case of a petrochemical plant,” Process Saf. Environ. Prot., vol. 92, no. 3, pp. 231–241, May 2014.
  8. A. Azadeh, S. Motevali Haghighi, and V. Salehi, “Identification of managerial shaping factors in a petrochemical plant by resilience engineering and data envelopment analysis,” J. Loss Prev. Process Ind., vol. 36, pp. 158–166, Jul. 2015.
  9. A. Azadeh and M. Sheikhalishahi, “An Efficient Taguchi Approach for the Performance Optimization of Health, Safety, Environment and Ergonomics in Generation Companies,” Saf. Health Work, vol. 6, no. 2, pp. 77–84, Jun. 2015.
  10. G. di Marzo Serugendo, “Robustness and dependability of self- organizing systems: a safety engineering perspective,” in Stabilization, Safety, and Security of Distributed Systems, Proceedings, no. 5873, 2009, pp. 254–268.
  11. D. A. Plowman, S. Solansky, T. E. Beck, L. Baker, M. Kulkarni, and D. V. Travis, “The role of leadership in emergent, self-organization,” Leadersh. Q., vol. 18, no. 4, pp. 341–356, Aug. 2007.
  12. A. Xyrichis and E. Ream, “Teamwork: A concept analysis,” J. Adv. Nurs., vol. 61, no. 2, pp. 232–241, Jan. 2008.
  13. J. Rasmussen, A. M. Pejtersen, and L. P. Goodstein, “Cognitive Systems Engineering John Wiley & Sons,” Inc., New York, NY, USA, 1994.
  14. J. Battles and H. King, “TeamSTEPPS® Teamwork Perceptions Questionnaire (T-TPQ) Manual,” Am. Inst. Res., pp. 23–25, 2010.
  15. P. Kalungi and T. T. Tanyimboh, “Redundancy model for water distribution systems,” Reliab. Eng. Syst. Saf., vol. 82, no. 3, pp. 275–286, 2003.
  16. F. Størseth, R. K. Tinmannsvik, and K. Øien, “Building Safety by resilient organization – a case specific approach,” in Reliability, risk and safety: theory and applications , no. 1, 1997, pp. 1209–1214.
  17. P. S. Gholami, P. Nassiri, R. Yarahmadi, A. Hamidi, and R. Mirkazemi, “Assessment of health safety and environment management System function in contracting companies of one of the petro-chemistry industries in Iran, a case study,” Saf. Sci., vol. 77, pp. 42–47, Aug. 2015.
  18. P. Okoh and S. Haugen, “Improving the robustness and resilience properties of maintenance,” Process Saf. Environ. Prot., vol. 94, no. C, pp. 212–226, 2015.
  19. B. Al‐Najjar and I. Alsyouf, “Improving effectiveness of manufacturing systems using total quality maintenance,” Integr. Manuf. Syst., vol. 11, no. 4, pp. 267–276, Jul. 2000.
  20. A. Azadeh, M. S. Gharibdousti, M. Firoozi, M. Baseri, M. Alishahi, and V. Salehi, “Selection of optimum maintenance policy using an integrated multi-criteria Taguchi modeling approach by considering resilience engineering,” Int. J. Adv. Manuf. Technol., vol. 84, no. 5–8, pp. 1067–1079, Sep. 2016.
  21. A. Azadeh, V. Salehi, M. Mirzayi, and E. Roudi, “Combinatorial optimization of resilience engineering and organizational factors in a gas refinery by a unique mathematical programming approach,” Hum. Factors Ergon. Manuf., vol. 27, no. 1, pp. 53–65, Jan. 2017.
  22. G. A. Shirali, M. Shekari, and K. A. Angali, “Quantitative assessment of resilience safety culture using principal components analysis and numerical taxonomy: A case study in a petrochemical plant,” J. Loss Prev. Process Ind., vol. 40, pp. 277–284, Mar. 2016.
  23. A. Azadeh, M. Sheikhalishahi, and M. Koushan, “An integrated fuzzy DEA-Fuzzy simulation approach for optimization of operator allocation with learning effects in multi products CMS,” Appl. Math. Model., vol. 37, no. 24, pp. 9922–9933, Dec. 2013.
  24. S. Saati M, A. Memariani, and G. R. Jahanshahloo, “Efficiency analysis and ranking of DMUs with fuzzy data,” Fuzzy Optim. Decis. Mak., vol. 1, no. 3, pp. 255–267, 2002.
  25. L. A. Zadeh, “A Note on Z-numbers,” Inf. Sci. (Ny)., vol. 181, no. 14, pp. 2923–2932, Jul. 2011.
  26. B. Kang, D. Wei, Y. Li, and Y. Deng, “A Method of Converting Z- number to Classical Fuzzy Number,” J. Inf. Comput. Sci., vol. 9, no. 3, pp. 703–709, 2012.
  27. A. Azadeh and R. Kokabi, “Z-number DEA: A new possibilistic DEA in the context of Z-numbers,” Adv. Eng. Informatics, vol. 30, no. 3, pp. 604–617, 2016.