توسعه مدلی برای ارزیابی ریسک پروژه‏های ساخت مبتنی بر تئوری مجموعه اعداد راف

نوع مقاله : مدیریت و سازمان(اخلاق و مسئولیت اجتماعی، رهبری و تحول سازمانی، عملکرد سازمانی، ریسک، مدیریت منابع سازمانی، سطوح تحلیل سازمانی، بلوغ سازمانی، آسیب­‌شناسی سازمانی، نظام اداری، . . . )

نویسندگان

1 عضو هیئت علمی دانشگاه سیستان و بلوچستان

2 دانشجوی کارشناسی ارشد مهندسی صنایع، دانشگاه سیستان و بلوچستان

چکیده

تصمیم‏گیری آگاهانه در صنعت ساخت، نقش به سزایی ایفا می‏کند. این صنعت با ریسک‏های مختلفی روبرو است که نادیده گرفتن آن‏ها می‏تواند باعث کاهش بازدهی و حتی شکست پروژه‏ها گردد. از سوی دیگر، عدم قطعیت موجود در ریسک‏ها و اطلاعات ناکافی در این زمینه، موفقیت پروژه‏ها را با ابهام مواجه می‏سازد. تمرکز این تحقیق بر روی ارزیابی ریسک پروژه‏های ساخت و انتخاب کم ریسک‏ترین پروژه می‏باشد. از طرف دیگر، ارزیابی ریسک خود شامل اظهارنظرهای شخصی و ابهاماتی می‏باشد. برای مقابله با این عدم قطعیت، رویکرد ارزیابی جدیدی بر پایه اعداد راف پیشنهاد شده است. روش پیشنهادی، شایستگی تئوری اعداد راف در رویارویی با ابهام و توانایی روش ویکور گروهی در مدل‏سازی و ارزیابی ریسک را با هم ترکیب می‏کند. همچنین روش پیشنهادی، در یک شرکت ساختمانی منتخب برای انتخاب کم ریسک‏ترین پروژه، جهت نشان دادن پتانسیل روش و کاربردی بودن آن، بکار گرفته شده است.کلمات کلیدی: صنعت ساخت، شناسایی ریسک، ارزیابی ریسک، روش ویکور گروهی، تئوری اعداد راف

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Developing a Construction Projects Risk Assessment Model Based on Rough Set Theory

چکیده [English]

Rational decision making plays an important role in construction industry. This industry faces a lot of risks that ignoring them, makes the projects less effective or even unsuccessful. Because of uncertainty in risks and inadequate information in this area, success of the projects dealing with ambiguity. This study mainly focuses on the risk evaluation of construction project risk and selecting the projects with minimum level of risk. Beside, evaluating project risk involves much subjectivity and vagueness. This study mainly focuses on the risk evaluation of construction project risk and selecting the projects with minimum level of risk. Beside, evaluating project risk involves much subjectivity and vagueness. To manipulate this uncertainty, a new approach on the basis of rough numbers proposed. To manipulate this uncertainty, a new approach on the basis of rough numbers proposed. This method integrated the merit of rough set theory in handling vagueness and the strength of group VIKOR process in modeling and assessing risk. Finally, an application in a construction company is provided to demonstrate the application and potential of the methodology. Finally, an application in a construction company is provided to demonstrate the application and potential of the methodology.
Keywords: Construction industry, Risk management, Group VIKOR method, Rough Set Theory.

کلیدواژه‌ها [English]

  • Construction industry
  • Risk management
  • Group VIKOR
  • method
  • Rough Set Theory

مراجع

منابع:
-          Chapman, C., & Ward, S. (1997). Project risk management: Processes, techniques and insights. New York: John Wiley and Sons.
-          Chapman, R. (1999). The controlling influences on effective risk identification and assessment for construction design management. International Journal of Project Management, 19(147-160).
-          Chu, M. T., Shyu, J., Tzeng, G. H., & Khosla, R. (2007). Comparison among three analytical methods for knowledge communities group-decision analysis. expert Systems with Applications, 57, 445-454.
-          Cooper, D., & Chapman, C. (1987). Risk analysis for large projects. New York: John Wiley and Sons.
-          Dey, P. K. (2001). Decision support system for risk management: a case study. Management Decision, 39, 634-649.
-          Dikmen, I., Birgonul, M. T., & Han, S. (2007). Using fuzzy risk assessment to rate cost overrun risk in international construction projects. International Journal of Project Management, 25, 494-505.
-          Ebrahimnejad, S., Mousavi, S. M., & Ghorbanikia, A. (2007). Risk identification and assessment in Iran construction supply chain. In First International Risk Congress, Tehran. Iran, 169-186.
-          Ebrahimnejad, S., Mousavi, S. M., Tavakkoli-Moghaddam, R., Hashemi, H., & Vahdani, B. (2012). A novel two-phase group decision making approach for construction project selection in fuzzy environment. Applied Mathematical Modelling, 36, 4197-4217.
-          Elkington, P., & Smallman, C. (2002). Managing project risk: a case study from the utilities sector. International Journal of Project Management, 20, 49-57.
-          Fang, C., & Marle, F. (2012). A simulation-based risk network model for decision support in project risk management. Decision Support Systems, 52, 635-644.
-          Forman, E., & Peniwati, K. (1998). Aggregating individual judgments and priorities with the analytic hierarchy process. European journal of operation research, 108, 165-169.
-          Greco, S., Matarazzo, B., & Slowinski, R. (2001). Rough set theory for multi-criteria decision analysis. European Journal of Operation Research, 129, 1-47.
-          Hillson, D. (2002). Extending thr risk process to manage opportunities. International Journal of Project Management, 20, 235-240.
-          Hillson, D. (2003). Using a risk breakdown structure in project management. Journal of Facilities Management, 2, 85-97.
-          Huang, J. J., Tzeng, G. H., & Liu, H. H. (2009). A revised VIKOR model for multiple criteria decision making - the perspective of regret theory. In: Shi, Y. et al (Eds.) Cutting-Edge Research Topics on Multiple Criteria Decision Making. Springer, Berlin Heidelberg, 761-768.
-          Jato-Espino, D., Castillo-Lopez, E., Rodriguez-Hernandez, J., & Canteras-Jordana, J. C. (2014). A review of application of multi-criteria decision making methods in construction. Automation in Construction, 45, 151-162.
-          Kackar, R. N. (1985). Off-line quality control, parameter design and the Taguchi method. Journal of Quality Technology, 17, 176-188.
-          KarimiAzari, A., Mousavi, N., Mousavi, S. F., & Hosseini, S. (2011). Risk assessment model selection in construction industry. expert Systems with Applications, 38, 9105-9111.
-          Laryea, S. (2008). Risk pricing practices in finance, insurance, and construction. The construction and building research conference of the Royal Institution of Chartered Surveyors, Dublin.
-          Liu, H., & Yan, T. (2007). Bidding-evaluation of construction projects based on VIKOR method. International Conference on Automation and Logistics, 1778-1782.
-          Miller, R., & Lessard, D. (2001). Understanding and managing risks in large engineering projects. International Journal of Project Management, 19, 437-443.
-          Mills, A. (2001). A systematic approach to risk management for construction. Structural Survey, 19, 245-252.
-          Mojtahedi, S. M. H., Mohammadi, S., Mousavi, S., & Makui, A. (2010). Project risk identification and assessment simultaneously using multi-attribute group decision making technique. Safety Science, 48, 499-507.
-          Nieto-Morote, A., & Ruz-Vila, F. (2011). A fuzzy approach to construction project risk assessment. International Journal of Project Management, 29, 220-231.
-          Opricovic, S. (1998). Muliti-criteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade.
-          Opricovic, S., & Tzeng, G. H. (2002). Multicriteria planning of post earthquake sustainable reconstruction. Computer-Aided Civil and Infrastructure Engineering, 17, 211-220.
-          Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operation Research, 156, 445-455.
-          Pawlak, Z. (1982). Rough Sets. International Journal of Computing and Information Science, 11, 341-356.
-          Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishing.  
-          Shang, H., Anumba, C. J., Bouchlaghem, D. M., Miles, J. C., Cen, M., & Taylor, M. (2005). An intelligent risk assessment system for distributed construction teams. Engineering Construction and Architectural Management, 12, 391-409.
-          Song, W., Ming, X., & Xu, Z. (2013). Risk evaluation of customer integration in new product development under uncertainty. Computers & Industrial Engineering, 65, 402-412
-          Tah, J., & Carr, V. (2001). Knowledge-based approach to construction project risk management. Journal of Computing in Civil Engineering, 15, 170.
-          Taroun, A. (2014). Toward a better modelling and assessment of construction risk:Insight from a literature review. International Journal of Project Management, 32, 101-115.
-          Taylan, O., Bafail, A. O., Abdulaal, R. M. S., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP an fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116.
-          Terano, T., Asai, K., & Sugeno, M. (1992). Fuzzy systems theory and its application. San Diego, CA: Academic Press.
-          Tuysuz, F., & Kahraman, c. (2006). Project risk evaluation using fuzzy analytic hierarchy Process: An application to information technology projects. International Journal of Intelligent Systems, 21, 559-584.
-          Zeng, J., An, M., & Smith, N. J. (2007). Application of fuzzy based decision making methodology to construction project risk assessment. International Journal of Project Management, 25, 589-600. 
پژوهشنامه مدیریت اجرایی
دوره 8، شماره 16
اسفند 1395
صفحه 39-62

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سابقه مقاله

  • تاریخ دریافت: 17 آذر 1394
  • تاریخ بازنگری: 08 شهریور 1395
  • تاریخ پذیرش: 08 دی 1395

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