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مدل رتبه‌بندی پیمانکاران با رویکرد کاهش تاخیرات پروژه‌های عمرانی و بهینه‌سازی ضرایب توسط الگوریتم ژنتیک | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 18، دوره 54، شماره 10، دی 1401، صفحه 3989-4006 اصل مقاله (2.4 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2022.20514.7449 | ||
| نویسندگان | ||
| آزیتا خیام باشی؛ آرمین منیر عباسی* | ||
| دانشکده مهندسی عمران، دانشگاه پیام نور، تهران، ایران | ||
| چکیده | ||
| در ایران سازمان مدیریت و برنامه‌ریزی کشور رسالت طبقه‌بندی و تشخیص صلاحیت پیمانکاران را بر عهده داشته و از معیارهایی برای ارزیابی توانایی و شایستگی پیمانکاران استفاده می‌نماید. از جمله عوامل ارزیابی بهره‌وری یک پروژه، زمان بوده و تاثیر عملکرد پیمانکار بر این شاخص غیرقابل انکار است. بنابراین در این پژوهش مدلی برای رتبه‌بندی پیمانکاران با رویکرد کاهش تاخیرات پروژه‌های عمرانی ارائه شده است. در این مدل علاوه بر معیارهای آیین‌نامه تشخیص صلاحیت پیمانکاران سازمان برنامه و بودجه، با توجه به تحقیقات گسترده در مطالعات پیشین و نظر کارشناسان ذی‌ربط، با استفاده از روش FMEA چهار معیار دیگر به عنوان معیارهای موثر در رخداد تاخیر و شدت آن جهت انتخاب پیمانکار شناسایی شد. هشت معیار از طریق روش الگوریتم ژنتیک مدل‌سازی شد به طوری که مستندات موجود شرکت‌های پیمانکاری در سازمان معاونت عمران شهری شهرداری اصفهان جمع‌آوری و به عنوان داده‌های ورودی مدل در نظر گرفته شد و پس از تحلیل آن‌ها با مدل و مقایسه امتیاز صلاحیت به دست آمده با امتیاز موجود هر شرکت، مدل نهایی استخراج گردید. متغیر متوسط درصد تاخیرات غیرمجاز به مدت قرارداد شدت همبستگی 0/712 با رابطه امتیاز صلاحیت پیشنهادی نشان داد. در نهایت با محاسبه امتیاز صلاحیت و رتبه‌بندی در مدل پیشنهادی، جابه‌جایی تعدادی از شرکت‌ها و افت از موقعیت قبلی به دلیل داشتن تاخیرات غیرمجاز دیده شد به طوری که گاهی حتی به تغییر رتبه شرکت نیز منجر می‌گردید. در مقایسه امتیاز صلاحیت دو مدل، تغییر شیب از1/3 درصد به 8/9 درصد هم دیده شد که حاکی از تاثیر معیارهای پیشنهادی بر امتیاز صلاحیت بود. | ||
| کلیدواژه‌ها | ||
| رتبه‌بندی پیمانکاران؛ کاهش تاخیرات؛ مدل‌سازی؛ سازمان برنامه و بودجه؛ الگوریتم ژنتیک | ||
| موضوعات | ||
| مدیریت قراردادها ی ملی | ||
| عنوان مقاله [English] | ||
| Contractor Ranking Model with Approach to Reduce Construction Project Delays and Optimization of Coefficients by Genetic Algorithm | ||
| نویسندگان [English] | ||
| azita khayambashi؛ A. Monirabbasi | ||
| Department of Civil Engineering, Payame Noor University, Iran | ||
| چکیده [English] | ||
| In Iran, the Management and Planning Organization is responsible for classifying and determining the qualifications of contractors and uses criteria to assess the ability and competence of contractors. One of the factors evaluating the productivity of a project is time and the impact of the contractor's performance on this index is undeniable. Therefore, in this study, a model for ranking contractors with the approach of reduction in delays in construction projects is presented. In this model, in addition to the criteria of the Regulations for Competence of Contractors of the Plan and Budget Organization, according to extensive research in previous studies and the opinion of relevant experts, using the FMEA method, four other criteria are identified as effective criteria for delay and severity to select a contractor. Eight criteria were modeled through genetic algorithm method so that the existing documents of contracting companies in the Isfahan Municipality Deputy for Urban Development were collected and considered as model input data. After analyzing them with the model and comparing the qualification score with the existing score of the Company, the final model was extracted. The average variable of the percentage of unauthorized delays for the contract period showed a correlation intensity of 0.712 with the relationship of the proposed qualification score. Finally, by calculating the qualification score and ranking in the proposed model, the relocation of a number of companies and the decline from the previous position was seen due to unauthorized delays, so that sometimes it even led to a change in the company's rating. Comparing the qualification score of the two models, a change in slope from 1.3% to 8.9% was also seen, which indicated the effect of the proposed criteria on the qualification score. | ||
| کلیدواژه‌ها [English] | ||
| Contractor ranking, Reduction in delays, Modeling, Planning and Budget Organization, Genetic Algorithm | ||
| مراجع | ||
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[1] M. Reenu, R. Kumar, S. Babu, Construction project performance model using artificial neural network, International Journal of Recent Trends in Engineering and Research (IJRTER), 3(5) (2017) 77-86. [2] M. Khodadad Saryazdi, F. Ghasemi, V. Joiban, Methods of reviewing the causes of delay in construction projects, in: 2th National Congress of Construction Engineering and Evaluation of Civil Projects, Semnan, 2015.)in Persian) [3] H. Karimipour, H. Zoghi, Assessing why construction projects are delayed using a mathematical model: A case study of the comprehensive water supply plan for villages in guilan province, in: The Second National Conference on Modern Urban Architecture and Development, Urmia, 2015.(in Persion) [4] N. Ibadov, Contractor selection for construction project, with the use of fuzzy preference relation, Procedia Engineering, 111 (2015) 317-323. [5] J. Bochenek, The contractor selection criteria in open and restricted procedures in public sector in selected EU countries, Procedia Engineering, 85 (2014) 69-74. [6] M. Sambasivan, Y.W. Soon, Causes and effects of delays in Malaysian construction industry, International Journal of project management, 25(5) (2007) 517-526. [7] R.F. Aziz, Ranking of delay factors in construction projects after Egyptian revolution, Alexandria Engineering Journal, 52(3) (2013) 387-406. [8] A. Al‐Kharashi, M. Skitmore, Causes of delays in Saudi Arabian public sector construction projects, Construction Management and Economics, 27(1) (2009) 3-23. [9] B. Greenhalgh, Introduction to construction contract management, Routledge, 2016. [10] T. Nyoni, W.G. Bonga, Towards factors affecting delays in construction projects: A case of Zimbabwe, Dynamic Research Journals' Journal of Economics and Finance (DRJ-JEF), 2(1) (2017) 12-28. [11] M.M. Marzouk, T.I. El-Rasas, Analyzing delay causes in Egyptian construction projects, Journal of advanced research, 5(1) (2014) 49-55. [12] Z.K. Bangash, Analyzing the Causes of Delays in Construction Projects for Peshawar: Contractor Perception, Journal of Emerging Trends in Applied Engineering, 1(1) (2016) 13-18. [13] M. Nasrollahi, Use F-PROMETHEE to evaluate and rank contractors civil projects, Journal of Industrial Management, 7(1) (2015) 175-188.)in Persian) [14] H. Amin Tahmasebi, R. Tavakoli Moghadam, E. Najafi, Provide an integrated IAHP / DEA model to evaluate construction industry contractors despite inaccurate data, Production And Operations Management, 7(2) (2016) 199-216.)in Persian) [15] V. Ayan, M. Forouzan, H. Dargahi, A. Mahdizade, M. Keyhaninezhad, Management Evaluation and prioritization of contractor selection based on TOPSIS, TOPSIS fuzzy, AHP and AHP fuzzy models, in: 2nd International Conference on Civil Engineering, Architecture and Crisis Management, Tehran, 2017.)in Persian) [16] M. Jamali, A. Valipour, Identifying and ranking the effective criteria in selecting construction contractors in South Zagros Oil and Gas Company, in: 3rd International Conference on Civil Engineering, Architecture and Urban Design, Tabriz, 2018.)in Persian) [17] K. Lam, S.T. Ng, H. Tiesong, M. Skitmore, S. Cheung, Decision support system for contractor pre‐qualification—artificial neural network model, Engineering Construction and Architectural Management, 7(3) (2000) 251-266. [18] M.S. El-Abbasy, T. Zayed, M. Ahmed, H. Alzraiee, M. Abouhamad, Contractor selection model for highway projects using integrated simulation and analytic network process, Journal of Construction Engineering and Management, 139(7) (2013) 755-767. [19] M. Hasnain, M.J. Thaheem, F. Ullah, Best value contractor selection in road construction projects: ANP-based decision support system, International Journal of Civil Engineering, 16(6) (2018) 695-714. [20] H.A. El-khalek, R.F. Aziz, E.S. Morgan, Identification of construction subcontractor prequalification evaluation criteria and their impact on project success, Alexandria Engineering Journal, 58(1) (2019) 217-223. [21] K. Najafi, J. Kazemi Rad, M. Ghanbari, R. Hejazi, M. Kashefiasl, Identification and Assessment of Occupational Risks in Mechanized Excavation of Metro Tunnel using the Failure Mode and Effects Analysis Technique (FMEA) Occupational Hygiene and Health Promotion Journal, 1(2) (2017) 129-142.)in Persian) [22] M. Salmani, V. Mirchi, The role of risk management in preventing potential costs of a factory construction project, in: 6th International Project Management Conference, Tehran, 2010.)in Persian) [23] T.C.o. Ministers, Qualification Regulations, Management and Planning Organization, 2002. )in Persian) [24] M. Karamooz, A. Ahmadi, M. Fallahi, System Engineering, Fifth ed., Amirkabir University of Technology Publications, Tehran, 2015.)in Persian) | ||
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