تخمین میزان اثر عوامل ایجاد اتلاف زمانی در پروژه‌های ساختمانی با استفاده از روش شبکه عصبی تعمیم‌یافته

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دکتری، دانشکده معماری، پردیس هنرهای زیبا، دانشگاه تهران، تهران، ایران

2 استاد، دانشکده معماری پردیس هنرهای زیبا، دانشگاه تهران، تهران، ایران

چکیده

یکی از عوامل مهم موفقیت در مدیریت، کاهش زمان تولید است که موجب افزایش میزان تولید و کاهش انتظار مشتریان است. در پروژه‌های ساخت آپارتمان مسکونی، زمان تولید تفاوت زیادی با کشورهای پیشرفته‌ای همچون ژاپن و آمریکا دارد؛ لذا کاهش زمان تولید آپارتمان یکی از دغدغه‌های مدیران پروژه است. یکی از راه‌های کاهش زمان، کاهش یا حذف اتلاف‌های زمانی است که در فرآیند تولید اتفاق می‌افتد و هدف این مقاله، شناسایی و تعیین میزان اثر این اتلاف‌هاست. برای شناسایی علت‌ها بعد از مطالعه کتابخانه‌ای، از مصاحبه نیمه ساختاریافته و تحلیل مضمون استفاده شد و 8 عامل به دست آمد که همگی دارای سه ویژگیِ حضور در مرحله اجرا، قابل‌کنترل بودن و تأثیر مستقیم بر زمان بودند. این 8 عامل شامل جلوگیری ناظر، کمبود مصالح، کمبود تجهیزات، دوباره‌کاری، تأخیر پیمانکار، جابجایی و انبارش، تداخل کاری و درنهایت، انجام همه فعالیت‌ها درون کارگاه هستند. برای تعیین میزان اثر، پس از دریافت 214 پاسخنامه قابل‌استفاده، از روش شبکه عصبی رگرسیونی تعمیم‌یافته استفاده شد. نتیجه به‌دست‌آمده به این صورت بود که در صورت وجود هر 8 عامل، 4/41 درصد زمان پروژه، به اتلاف زمانی اختصاص دارد و تنها سه عامل تأخیر پیمانکار، انجام فعالیت‌ها درون کارگاه و دوباره‌کاری باعث 8/31 درصد اتلاف می‌شود. این نتایج، معیار مناسبی را برای تصمیم‌گیری مدیران پروژه نسبت به نوع رفتار با این عوامل، فراهم می‌سازد تا درنهایت بتوانند طول مدت پروژه را کاهش دهند.

کلیدواژه‌ها


عنوان مقاله [English]

Estimating the amount of time waste causes effect on apartment projects by GRNN Method

نویسندگان [English]

  • Mahdi Mohammadi Ghazimahalleh 1
  • Mahmood Golabchi 2
1 PhD, Faculty of Architecture, School of Art, Tehran University, Tehran, Iran
2 Professor, Faculty of Architecture, School of Art, Tehran University, Tehran, Iran.
چکیده [English]

One of important factors in management success is reducing production time. In Iran Residential apartment's production process takes considerably more time compared to developed countries like U.S.A and Japan so decrease of process time is one of important problems for construction managers. One of decreasing ways is reducing or elimination of time wastes. The aim of this paper is to identify time waste causes and estimating the amount of these effects. For identifying, after literature review, semi-structured interview and thematic analysis were used and 8 causes were identified that have three factors including presence in executing, direct impact and controllability. These 8 causes are supervisor blockage, lack of materials, lack of equipment, rework, contractors delay, transfer and depot, subcontractors' conflict and doing more activities in site. For estimating the amount of effect, questionnaires were distributed between project managers that had 5 to 7 floor residential apartments and after receiving 214 usable responses, Generalized Regression Neural Network Method were used. The result obtained in this paper is in the presence of all 8 causes in projects, 41.4 percent of executing time is waste and 77percent of this significant amount is because of three causes contractor delay, do more activities in site and rework. These results are appropriate criteria for project manager to make decision about how to dealing with contractors so they can reduce the project duration.

کلیدواژه‌ها [English]

  • Building construction
  • Generalized Regression Neural Network
  • Lean approach
  • Residential apartment
  • Time waste
[1]   Zakeri M., Olomolaiye P. O., Holt G. D, and Harris F. C., “A survey of constraints on Iranian construction operatives’ productivity,” Construction Management & Economics, vol. 14, no. 5, pp. 417–426, 1996.
[2]   Womack, J. P., Jones D. T., and Roos, D., Machine that changed the world. Simon and Schuster, 1990.
[3]   Liker  J. K., The Toyota way. Esensi, 2005.
[4]   Koskela L. J, “Moving on-beyond lean thinking,” Lean Construction Journal, vol. 1, no. 1, pp. 24–37, 2004.
[5]   Koskela L., An exploration towards a production theory andits application to construction. VTT Technical Research Centre of Finland, 2000.
[6]   Mottaghi H., and Ghadrdan A., “Reduction of lead-time Production by Using Value Stream Mapping and Simulation,” Journal of Management Researches in Iran, vol. 18, no. 4, pp. 161–181, 2014.[In Persian]
[7]   Perera N. A., Sutrisna M., and Yiu T. W., “Decision-Making Model for Selecting the Optimum Method of Delay Analysis in Construction Projects,” Journal of Management in Engineering, vol. 32, no. 5, p. 4016009, 2016.
[8]   Koushki P. A., Al Rashid K., and Kartam, N., “Delays and cost increases in the construction of private residential projects in Kuwait,” Construction Management and Economics, vol. 23, no. 3, pp. 285–294, 2005.
[9]   Womack J. P., and Jones D. T., Lean thinking: banish waste and create wealth in your corporation. Simon and Schuster, 2010.
[10]  Larsen J. K., Brunoe T. D., and Lindhard S. M., “Analyzing Factors Affecting Time, Cost, and Quality between Diverse Public Construction Agencies,” in ICCREM 2015@ Environment and the Sustainable Building, 2015, no. 2011, pp. 67–77.
[11]  Mukuka M. J., Aigbavboa C. O., and Thwala W. D., “A Theoretical Review of the Causes and Effects of Construction Projects Cost and Schedule Overruns,” pp. 16–19, 2014.
[12]  Phaniraj K., and Sreekumar K. S., “Practical Factors Affecting Delay in High Rise Construction – A Case Study in a Construction Organization,” International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 5, pp. 875–881, 2014.
[13]  Marzouk M. M., and El-Rasas T. I., “Analyzing delay causes in Egyptian construction projects,” Journal of Advanced Research, vol. 5, no. 1, pp. 49–55, 2014.
[14]  González P., González V., Molenaar K., and Orozco F., “Analysis of causes of delay and time performance in construction projects,” Journal of Construction Engineering and Management, vol. 140, no. 1, p. 4013027, 2013.
[15]  Ghoddousi P., and Hosseini M. R., “A survey of the factors affecting the productivity of construction projects in Iran,” Technological and Economic Development of Economy, vol. 18, no. 1, pp. 99–116, 2012.
[16]  Khattri T., Agarwal S., and Gupta V., “Causes and Effects of Delay in Construction Project,” International Journal of Engineering and Technology, vol. 3, no. 10, pp. 564–568, 2016.
[17]  Gündüz M., Nielsen Y., and Özdemir M., “Quantification of Delay Factors Using the Relative Importance Index Method for Construction Projects in Turkey,” Journal of Management in Engineering, vol. 29, no. April, pp. 133–139, 2012.
[18]  Khoshgoftar M., Bakar A. H. A., and Osman O., “Causesof delays in Iranian construction projects,” International Journal of Construction Management, vol. 10, no. 2, pp. 53–69, 2010.
[19]  Abd El-Razek M. E., Bassioni H. A., and Mobarak A. M., “Causes of delay in building construction projects in Egypt,” Journal of Construction Engineering and Management, vol. 134, no. 11, pp. 831–841, 2008.
[20]  Sambasivan M., and Soon Y. W., “Causes and effects of delays in Malaysian construction industry,” International Journal of Project Management, vol. 25, no. 5, pp. 517–526, 2007.
[21]  Lo T. Y., Fung I. W., and Tung K. C., “Construction Delays inHong Kong Civil Engineering Projects,” Journal of Construction Engineering and Management, vol. 132, no. 6, pp. 636–649, 2006.
[22]  Aibinu A. A., and Odeyinka H. A., “Construction Delays and Their Causative Factors in Nigeria,” Journal of Construction Engineering and Management, vol. 132, no. 7, pp. 667–677, 2006.
[23]  Braun V., and Clarke V., “Using thematic analysis in psychology,” Qualitative Research in Psychology, vol. 3, no. 2, pp. 77–101, Jan. 2006.
[24]  Specht D. F., “A general regression neural network,” IEEE Transactions on Neural Networks, vol. 2, no. 6, pp. 568–576, 1991.
[25]  Haykin S. S., Neural networks: a comprehensive foundation. Tsinghua University Press, 2001.
[26]  Mirfakhraddiny S. H., Babaei Meybodi H., and Morovati Sharifabadi A., “Predicting Energy Consumption of Iran via a Hybrid Model of Artificial Neural Networks and Genetic Algorithms and Comparing It with Traditional Models,” Journal of Management Researches in Iran, vol. 17, no. 2, pp. 196–222, 2013. [In Persian]
[27]  Bhatt A., and Helle H. B., “Committee neural networks for porosity and permeability prediction from well logs,” Geophysical Prospecting, vol. 50, no. 6, pp. 645–660, 2002.
[28]  Smith M., Neural networks for statistical modeling. Thomson Learning, 1993.
[29]  Fausett L., Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Inc., 1994.
[30]  Beale M. H., Hagan M. T., and Demuth H. B., “Neural Network Toolbox TM User’s Guide,” Math Works Inc., 1992.