نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشگاه شهید بهشتی.

2 استاد، دانشگاه شهید بهشتی.

3 استادیار، دانشگاه شهید بهشتی.

10.52547/jimp.12.2.135

چکیده

اجرای صحیح پروژه‌های تعمیرات اساسی کارخانه‌های فرایندی در صنایع بالادستی نفت که شامل تجهیزات و تأسیسات سرمایه‌بر بسیاری هستند، از اهمیت بالایی برخوردار است. شناسایی و ارزیابی صحیح ریسک‌های این نوع پروژه‌ها، گامی مهم در جهت کاهش چشمگیر خسارات مالی، انسانی و زیست‌محیطی آن‌ها محسوب می‌­شود. در این پژوهش، چارچوبی جدید برای ارزیابی انواع ریسک‌های این نوع از پروژه‌ها ارائه شده است. بر این اساس ابتدا با استفاده از نظر خبرگان، ریسک‌های این نوع پروژه‌ها با استفاده از روش مصاحبه و طوفان فکری شناسایی شد. 10 مورد از این ریسک‌ها با استفاده از روش دلفی فازی به‌عنوان مهم‌ترین موارد انتخاب شده و با روش ترکیبی SWARA و EDAS فازی مبتنی بر اعداد فازی ذوزنقه‌ای مورد­ارزیابی قرار گرفت. بر اساس یافته‌های پژوهش، از منظر خبرگان ریسک تأمین مالی به‌موقع از سوی کارفرما، با امتیاز ارزیابی 0/83 دارای بالاترین رتبه در میان ریسک‌ها است و ریسک شکست و نقص تجهیزات در حین عملیات، با امتیاز ارزیابی 0/04، پایین‌ترین رتبه را در میان ریسک‌های پروژه‌های تعمیرات اساسی صنایع فرایندی بالادستی نفت دارا است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Upstream Oil Process Plants Turnaround Projects Risk Evaluation Using a Hybrid Fuzzy MADM Method

نویسندگان [English]

  • Mohammadreza Moniri 1
  • Akbar Alem Tabriz 2
  • Ashkan Ayough 3

1 Ph.D Candidate, Shahid Beheshti University.

2 Professor, Shahid Beheshti University.

3 Assistant Professor, Shahid Beheshti University.

چکیده [English]

Process plants turnaround maintenance projects in upstream oil industry, which includes many capital-intensive installations and equipments, are very important. Accurate risk identification and evaluation of such project’s risks is an important step to significant decrease in financial, human and environmental damages of them. A new framework for such projects risk asessment is presented in this article. According to this framework, risks were identified according to expert’s judgment, using interviews and brain storming at first. Then, using fuzzy Delphi method, ten risks have been chosen as most important risks and then, analyzed using a hybrid fuzzy SWARA and EDAS method based on traoezodial fuzzy numbers. According to research   findings, on time financial providence from project owner with an appraisal score of 0.83 ranked the highest and equipment failure during operations with an appraisal score of 0.04 ranked the lowest among the oil upstream process plants turnaround project’s risks.

کلیدواژه‌ها [English]

  • Project Risk Management
  • Oil Upstream Industry
  • Turnaround
  • Fuzzy Multiple Attribute Decision Making
  • Process Industries
  1. Adamtey, S., & Onsarigo, L. (2018). Analysis of pipe-bursting construction risks using probability-impact model. Journal of Engineering, Design and Technology, 16(3), 461-477.
  2. Ahmadi Moghadam, J., Motahari Farimani, N., & Kazemi, M. (2021). Developing a Project Planning Model Considering the Executive Methods and the Rework Activity. Journal of Industrial Management Perspective, 11(1), 147-173. (In Persian).
  3. Al-Turki, U., Duffuaa, S., & Bendaya, M. (2019). Trends in turnaround maintenance planning: literature review. Journal of quality in maintenance engineering. 25(2), 253-271.
  4. Alinezhad, A., & Khalili, J. (2019). SWARA Method. In New Methods and Applications in Multiple Attribute Decision Making (MADM) (pp. 99-102): Springer.
  5. Arunraj, N., & Maiti, J. (2007). Risk-based maintenance—Techniques and applications. Journal of hazardous materials, 142(3), 653-661.
  6. Badiru, A. B., & Osisanya, S. O. (2016). Project management for the oil and gas industry: a world system approach, CRC Press.
  7. Barghi, B., & Shadrokh, S. S. (2020). Qualitative and quantitative project risk assessment using a hybrid PMBOK model developed under uncertainty conditions. Heliyon6(1),
  8. Bashardoost, P., Nasirzadeh, F., & Mohtashemi, N. N. (2018, March). An integrated fuzzy-DEMATEL approach to project risk analysis. In 2018 7th International Conference on Industrial Technology and Management (ICITM)(pp. 411-416). IEEE.
  9. Bevilacqua, M., Ciarapica, F. E., & Giacchetta, G. (2009). Critical chain and risk analysis applied to high-risk industry maintenance: A case study. International Journal of Project Management, 27(4), 419-432.
  10. Bevilacqua, M., Ciarapica, F. E., Giacchetta, G., & Marchetti, B. (2012). Development of an innovative criticality index for turnaround management in an oil refinery. International Journal of Productivity and Quality Management, 9(4), 519-544.
  11. Bissonette, M. M. (2016). Project risk management: a practical implementation approach. Project Management Institute.
  12. Chang, P. T., Huang, L. C., & Lin, H. J. (2000). The fuzzy Delphi method via fuzzy statistics and membership function fitting and an application to the human resources. Fuzzy sets and systems, 112(3), 511-520.
  13. Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International journal of production economics, 102(2), 289-301.
  14. Cheng, C. H., & Lin, Y. (2002). Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European journal of operational research, 142(1), 174-186.
  15. Cheraghi, M., Karbassi, A., Monavari, S. M., & Baghvand, A. (2018). Environmental risk management associated with the development one of oil fields in southwestern Iran using AHP and FMEA methods. Anthropogenic Pollution Journal, 2(2), 33-40.
  16. Cooper, D. F., Grey, S., Raymond, G., & Walker, P. (2013). Project risk management guidelines: managing risk in large projects and complex procurements: Wiley.
  17. Ebrahimnejad, S., Mousavi, S. M., & Seyrafianpour, H. (2010). Risk identification and assessment for build–operate–transfer projects: A fuzzy multi attribute decision making model. Expert systems with Applications, 37(1), 575-586.
  18. Ebrahimnejad, S., Mousavi, S. M., Tavakkoli-Moghaddam, R., & Heydar, M. (2014). Risk ranking in mega projects by fuzzy compromise approach: A comparative analysis. Journal of Intelligent & Fuzzy Systems26(2), 949-959.
  19. Fouladgar, M. M., Yazdani-Chamzini, A., & Zavadskas, E. K. (2012). Risk evaluation of tunneling projects. Archives of civil and mechanical engineering12, 1-12.
  20. Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2018). A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Archives of Civil and Mechanical Engineering, 18(1), 32-49.
  21. Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2017). A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations. Computers & Industrial Engineering, 112, 156-174.
  22. Ghorabaee, M. K., Zavadskas, E. K., Amiri, M., & Turskis, Z. (2016). Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection. International journal of computers communications & control, 11(3), 358-371.
  23. Grzegorzewski, P., & Mrówka, E. (2005). Trapezoidal approximations of fuzzy numbers. Fuzzy Sets and Systems, 153(1), 115-135.
  24. Habibi, A., Jahantigh, F. F., & Sarafrazi, A. (2015). Fuzzy Delphi technique for forecasting and screening items. Asian Journal of Research in Business Economics and Management, 5(2), 130-143 .
  25. Hatefi, S. M., & Tamošaitienė, J. (2019). An integrated fuzzy DEMATEL-fuzzy ANP model for evaluating construction projects by considering interrelationships among risk factors. Journal of Civil Engineering and Management25(2), 114-131.
  26. Heins, W., & Röling, M. (1995). Application of multiattribute theory in a safety monitor for the planning of maintenance jobs. European journal of operational research, 86(2), 270-280.
  27. Hey, R. B. (2019). Turnaround Management for the Oil, Gas, and Process Industries: A Project Management Approach. Gulf Professional Publishing.
  28. Hillson, D. (2012). Practical project risk management: The ATOM methodology. Berrett-Koehler Publishers.
  29. Honari Choobar, F., & Nazari, A. (2012). Power plant project risk assessment using a fuzzy-ANP and fuzzy-TOPSIS method. International Journal of Engineering, 25(2), 107-120.
  30. Hsu, Y. L., Lee, C. H., & Kreng, V. B. (2010). The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Applications, 37(1), 419-425.
  31. Husin, S., Fachrurrazi, F., Rizalihadi, M., & Mubarak, M. (2019). Implementing fuzzy TOPSIS on project risk variable ranking. Advances in Civil Engineering2019.
  32. Ishikawa, A., Amagasa, M., Shiga, T., Tomizawa, G., Tatsuta, R., & Mieno, H. (1993). The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy sets and systems, 55(3), 241-253 .
  33. 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.
  34. Jahn, F., Cook, M., & Graham, M. (2008). Hydrocarbon Exploration and Production. Elsevier Science.
  35. Kahraman, C., Keshavarz Ghorabaee, M., Zavadskas, E. K., Cevik Onar, S., Yazdani, M., & Oztaysi, B. (2017). Intuitionistic fuzzy EDAS method: an application to solid waste disposal site selection. Journal of Environmental Engineering and Landscape Management, 25(1), 1-12
  36. KarimiAzari, A., Mousavi, N., Mousavi, S. F., & Hosseini, S. (2011). Risk assessment model selection in construction industry. Expert Systems with Applications38(8), 9105-9111.
  37. Kassem, M. A., Khoiry, M. A., & Hamzah, N. (2019). Risk factors in oil and gas construction projects in developing countries: A case study. International Journal of Energy Sector Management, 13(4), 846-861.
  38. Kaufmann, A., & Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. Elsevier Science Inc.
  39. Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management, 11(2), 243-258.
  40. Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451.
  41. Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic:Theory and application. New Jersey: Prentice-Hall Inc.
  42. Kobbacy, K. A. H., & Murthy, D. P. (2008). Complex system maintenance handbook. Springer Science & Business Media.
  43. Kraidi, L., Shah, R., Matipa, W., & Borthwick, F. (2019). Analyzing the critical risk factors associated with oil and gas pipeline projects in Iraq. International Journal of Critical Infrastructure Protection, 24, 14-22.
  44. Kuo, Y. F., & Chen, P. C. (2008). Constructing performance appraisal indicators for mobility of the service industries using Fuzzy Delphi Method. Expert systems with applications, 35(4), 1930-1939.
  45. Kuo, Y. C., & Lu, S. T. (2013). Using fuzzy multiple criteria decision making approach to enhance risk assessment for metropolitan construction projects. International Journal of Project Management31(4), 602-614.
  46. Lenahan, T. (2011). Turnaround, shutdown and outage management: Effective planning and step-by-step execution of planned maintenance operations. Elsevier.
  47. Li, J., & Zou, P. X. (2011). Fuzzy AHP-based risk assessment methodology for PPP projects. Journal of Construction Engineering and Management137(12), 1205-1209.
  48. Lin, Z., & Jianping, Y. (2011). Risk assessment based on fuzzy network (F-ANP) in new campus construction project. Systems engineering procedia1, 162-168.
  49. Liu, J., Li, Q., & Wang, Y. (2013). Risk analysis in ultra deep scientific drilling project—A fuzzy synthetic evaluation approach. International Journal of Project Management31(3), 449-458.
  50. Liu, J., & Wei, Q. (2018). Risk evaluation of electric vehicle charging infrastructure public-private partnership projects in China using fuzzy TOPSIS. Journal of Cleaner Production, 189, 211-222.
  51. Ma, M., Kandel, A., & Friedman, M. (2000). A new approach for defuzzification. Fuzzy sets and Systems, 111(3), 351-356.
  52. Mardani, A., Nilashi, M., Zakuan, N., Loganathan, N., Soheilirad, S., Saman, M. Z. M., & Ibrahim, O. (2017). A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments. Applied Soft Computing, 57, 265-292.
  53. Marhavilas, P.-K., Koulouriotis, D., & Gemeni, V. (2011). Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000–2009. Journal of Loss Prevention in the Process Industries, 24(5), 477-523.
  54. Mohaghar, A., Khorasani A. R. (2020). Designing the Model of Assessing the Risk management of Automaker Companies in Iran: Grounded Theory. Journal of Industrial Management Perspective, 10(3), 9-28. (In Persian).
  55. Moradpour, S., Ebrahimnejad, S., Mehdizadeh, E., & Mohamadi, A. (2011). Using hybrid fuzzy PROMETHEE II and fuzzy binary goal programming for risk ranking: A case study of highway construction projects. Journal of Optimization in Industrial Engineering, (9), 47-55.
  56. Murray, T. J., Pipino, L. L., & van Gigch, J. P. (1985). A pilot study of fuzzy set modification of Delphi. Human Systems Management, 5(1), 76-80 .
  57. Nadizadeh, A., Ranjbar, H., & Moubed, M. (2020). Periodic Inspection Optimization for a Two-Component System with Dependent Failures. Journal of Industrial Management Perspective10(2), 83-110. (In Persian).
  58. Nguyen, A. T., Nguyen, L. D., Le-Hoai, L., & Dang, C. N. (2015). Quantifying the complexity of transportation projects using the fuzzy analytic hierarchy process. International journal of project management33(6), 1364-1376.
  59. Nieto-Morote, A., & Ruz-Vila, F. (2011). A fuzzy AHP multi-criteria decision-making approach applied to combined cooling, heating, and power production systems. International Journal of Information Technology & Decision Making10(03), 497-517.
  60. Othman, A. A. E., & Abdelwahab, N. M. A. (2018). Achieving sustainability through integrating risk management into the architectural design process. Journal of Engineering, Design and Technology, 16(1), 25-43.
  61. (2017). A Guide to the Project Management Body of Knowledge (PMBOK Guide): Agile Practice Guide. Project Management Institute.
  62. (2019). Standard for Risk Management in Portfolios, Programs, and Projects. In: Project Management Institute.
  63. Pritchard, C. L. (2014). Risk management: concepts and guidance. CRC Press.
  64. Rajagopalan, S., Sahinidis, N. V., Amaran, S., Agarwal, A., Bury, S. J., Sharda, B., & Wassick, J. M. (2017). Risk analysis of turnaround reschedule planning in integrated chemical sites. Computers & Chemical Engineering, 107, 381-394.
  65. Raz, T., & Hillson, D. (2005). A comparative review of risk management standards. Risk Management, 7(4), 53-66.
  66. Seiti, H., & Hafezalkotob, A. (2019). Developing the R-TOPSIS methodology for risk-based preventive maintenance planning: A case study in rolling mill company. Computers & Industrial Engineering, 128, 622-636.
  67. Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
  68. Serrano-Gomez, L., & Munoz-Hernandez, J. I. (2019). Monte Carlo approach to fuzzy AHP risk analysis in renewable energy construction projects. PloS one14(6),
  69. Subramanyan, H., Sawant, P. H., & Bhatt, V. (2012). Construction project risk assessment: development of model based on investigation of opinion of construction project experts from India. Journal of construction engineering and management, 138(3), 409-421.
  70. Tavakkoli-Moghaddam, R., Mousavi, S., & Hashemi, H. (2011). A fuzzy comprehensive approach for risk identification and prioritization simultaneously in EPC projects. Risk management in environment, production and economy, 123-146.
  71. Taylan, O., Bafail, A. O., Abdulaal, R. M., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing17, 105-116.
  72. Valipour, A., Yahaya, N., Md Noor, N., Kildienė, S., Sarvari, H., & Mardani, A. (2015). A fuzzy analytic network process method for risk prioritization in freeway PPP projects: an Iranian case study. Journal of Civil Engineering and Management21(7), 933-947.
  73. Valipour, A., Sarvari, H., & Tamošaitiene, J. (2018). Risk assessment in PPP projects by applying different MCDM methods and comparative results analysis. Administrative Sciences8(4),
  74. Wang, L., Zhang, H. Y., Wang, J. Q., & Li, L. (2018). Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project. Applied Soft Computing64, 216-226.
  75. Ward, S. C., & Chapman, C. (2003). Project risk management. processes, techniques and insights.
  76. Willumsen, P., Oehmen, J., Stingl, V., & Geraldi, J. (2019). Value creation through project risk management. International Journal of Project Management, 37(5), 731-749.
  77. Wu, Y., & Zhou, J. (2019). Risk assessment of urban rooftop distributed PV in energy performance contracting (EPC) projects: an extended HFLTS-DEMATEL fuzzy synthetic evaluation analysis. Sustainable Cities and Society47, 101-524.
  78. Xia, N., Wang, X., Wang, Y., Yang, Q., & Liu, X. (2017). Lifecycle cost risk analysis for infrastructure projects with modified Bayesian networks. Journal of Engineering, Design and Technology, 15(1) , 79-103.
  79. Yager, R. R. (1993). Fuzzy screening systems. In Fuzzy Logic (pp. 251-261). Springer, Dordrecht.
  80. Yan, W., Baoguo, L., & Yi, Q. (2015). Fuzzy analytic network process and its application in tunnel engineering risk analysis. Electronic Journal of Geotechnical Engineering20, 6685-6701.
  81. Yazdani, M., Alidoosti, A., & Zavadskas, E. K. (2011). Risk analysis of critical infrastructures using fuzzy COPRAS. Economic research-Ekonomska istraživanja, 24(4), 27-40.
  82. Yazdani, M., Abdi, M. R., Kumar, N., Keshavarz-Ghorabaee, M., & Chan, F. T. (2019). Improved decision model for evaluating risks in construction projects. Journal of Construction Engineering and Management145(5),
  83. Yazdani-Chamzini, A., Yakhchali, S. H., & Mahmoodian, M. (2013). Risk ranking of tunnel construction projects by using the ELECTRE technique under a fuzzy environment. International Journal of Management Science and Engineering Management, 8(1), 1-14.
  84. Zeng, J., An, M., & Smith, N. J. (2007). Application of a fuzzy based decision making methodology to construction project risk assessment. International Journal of Project Management, 25(6), 589-600.