مدل پایش انحراف پروژه‌های ساخت (مطالعه‌ی موردی: یک شرکت پیمانکاری)

نوع مقاله : پژوهشی

نویسندگان

مجتمع دانشگاهی مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران.

چکیده

مدیریت پروژه به‌دنبال روش‌هایی برای پایش انحراف پروژه است. در پژوهش حاضر، انحراف پروژه با ارزیابی هم‌زمان انحراف هزینه، زمان، و کیفیت پایش می‌شود. اهمیت عوامل و اهم ریسک‌های پروژه در مصاحبه از خبرگان یک شرکت پیمانکاری شناسایی ‌شده است. با روش‌ تحلیل حالت‌های بالقوه‌ی خطا و آثار آن‌ها و منطق فازی، ارزیابی ریسک انجام ‌شده است. همچنین، با استفاده از روش سیستم استنتاج فازی و شبکه‌های بیزی، انحراف پروژه با وجود وابستگی بین ریسک‌ها، کنترل و از معیار میانگین مربعات خطا برای بررسی اعتبار مدل‌ها استفاده شده است. با مقایسه‌ی درصد انحراف واقعی 15 پروژه‌ی اجراشده در شرکت مذکور با درصد انحراف برآوردی مدل‌ها، میانگین مربعات خطا در روش استنتاج فازی نسبت به روش شبکه‌ی بیزی کمتر به‌دست‌ آمده و روش استنتاج فازی با میانگین مربعات خطا معادل با 0011/0 نسبت به روش شبکه‌ی بیزی کاراتر بوده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Deviation monitoring model of construction projects, case study: a contracting company

نویسندگان [English]

  • Jafar Gheidar Kheljani
  • Mohammad Hossein Karimi Gavareshki
  • Fateme Malekaee Ashtiyani
Management and Indutrial Engineering Department, Malek Ashtar University of Technology, Tehran, Iran.
چکیده [English]

Project management is always looking for ways to complete the project on time, quality, and cost according to the project contract. Due to the existence of various risks and increased uncertainty in business environments, a high percentage of the projects have deviations when compared with the base plan. The purpose of this research is to continuously monitor the deviation of the project by evaluating the deviation of cost, time, and quality simultaneously under the conditions of uncertainty. By conducting a pairwise comparison between cost, time, and quality factors and interviewing experts of a contractor company, the relative importance of these factors has been determined. The most important risks of the project have been identified by interviewing experts in the contractor company. The risk assessment has been carried out with the failure mode and effect analysis and fuzzy logic method. By using the approach of fuzzy inference system and Bayesian networks, project deviation is predicted. In the fuzzy inference system, project risks are considered as input variables in the form of triangle fuzzy number and project deviation is obtained as the output variable of the cohesive fuzzy inference system in the Matlab software. In the Bayesian network approach, the initial and conditional probabilities of the nodes have been obtained by using the experts' opinion and the project deviation has been investigated using the network between risks in AgenaRisk software. To estimate the validity of the results of the models, the mean square error criterion was used. By comparing the actual deviation percentage of projects implemented in the mentioned company with the estimated deviation percentage of the models, the mean squared error in the fuzzy inference method is less compared to the Bayesian network method, and the fuzzy inference method with the mean squared error equal to 0.0011 is more efficient than the Bayesian network method.

کلیدواژه‌ها [English]

  • Project Management
  • Deviation
  • Failure Mode and Effect Analysis
  • Fuzzy Inference System
  • Bayesian Network

مراجع

1.Dastgheib, S.R., Feylizadeh, M.R., Bagherpour, M.and Mahmoudi, A., 2022. Improving estimate at completion (EAC) cost of construction projects using adaptive neuro-fuzzy inference system(ANFIS). Canadian Journal of Civil
Engineering, 49(2), pp.222-232.https://doi.org/10.1139/cjce-2020-0399.
2. Tidlund, M., Spross, J. and Larsson, S., 2023. Observational method as risk management tool: the hvalfjörður tunnel project, iceland. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 17(2), pp.346-360.https://doi.org/10.1080/17499518.2022.2046784
3. Fazli, M., Fallah, A. and KHakbaz, A., 2020. Risk management in construction projects considering the cross-dependency project risks: utility maximization. Industrial Management Studies, 18(56), pp.337-374.
https://doi.org/10.22054/jims.2019.25341.1875. [InPersian].
4. Chang, B., Kuo, C., Wu, C.H. and Tzeng, G.H., 2015. Using fuzzy analytic network process to assess the risks in enterprise resource planning system implementation. Applied Soft Computing, 28,pp.196-207.
https://doi.org/10.1016/j.asoc.2014.11.025.
5. Xiang, P., Xia, X. and Pang, X., 2024. Integrated risk assessment method for cross-regional mega construction projects. Engineering, Construction and Architectural Management, 31(6), pp.2369-2391.10.1108/ECAM-06-2022-0534
6. Castañón-Puga, M., Rosales-Cisneros, R.F., AcostaPrado, J.C., Tirado-Ramos, A., Khatchikian, C. and Aburto- amacllanqui, E., 2023. Earned value management agent-based simulation model. Systems, 11(2),p.86. https://doi.org/10.3390/systems11020086.
7. Khaledian, F. and Momeni, M., 2021. Measuring the performance of time and quality of project execution under uncertainty. Research in Production and Operations Management, 12(2), pp.71-91.10.22108/jpom.2021.128030.1359. [In Persian].
8. Peyman, F. and Fathi, A., 2016. Forecasting cost of civil engineering projects using ANN and EVA. Journal of Dam and Hydroelectric Powerplant, 3(10), pp.11-23.20.1001.1.23225882.1395.3.10.5.7 . [In Persian].
9. Syed, Z. and Lawryshyn, Y., 2020. Multi-criteria decision-making considering risk and uncertainty in physical asset management. Journal of Loss Prevention in the Process Industries, 65,p.104064. https://doi.org/10.1016/j.jlp.2020.104064
10. Alam Tabriz, A. and Hamzehi, E., 2011. Project risk evaluation and analysis using risk management based
on PMBOK standard and RFMEA technique. Industrial Management Studies, 9(23),pp.1-19. 20.1001.1.22518029.1390.9.23.1.2. [InPersian].
11. Bahadori-Amjaz, F. and Soleimani-sardo, M., 2021. Evaluation of the environmental risks of jiroft dam during the utilization phase. Geography and Environmental Planning, 32(4), pp.45-64.10.22108/gep.2021.129846.1446. [In Persian].
12. Boateng, A., Ameyaw, C. and Mensah, S., 2022. Assessment of systematic risk management practices on building construction projects in ghana. International Journal of Construction Management, 22(16), pp.3128- 3136. https://doi.org/10.1080/15623599.2020.1842962.
13. Shibani, A., Hasan, D., Saaifan, J., Sabboubeh, H.,Eltaip, M., Saidani, M. and Gherbal, N., 2024.Financial risk management in the construction projects. Journal of King Saud UniversityEngineering Sciences, 36(8), pp.552-561.
https://doi.org/10.1016/j.jksues.2022.05.00.
14. Zhang, Y., 2016. Selecting risk response strategies considering project risk interdependence. International Journal of Project Management, 34(5), pp.819-830. https://doi.org/10.1016/j.ijproman.2016.03.001.
15. Qazi, A., Shamayleh, A., El-Sayegh, S. and Formaneck, S., 2021. Prioritizing risks in sustainable construction projects using a risk matrix-based monte carlo simulation approach. Sustainable Cities and Society, 65, p.102576.
https://doi.org/10.1016/j.scs.2020.102576.
16. Chen, L., Lu, Q., Li, S., He, W. and Yang, J., 2021. Bayesian monte carlo simulation–driven approach for construction schedule risk inference. Journal of Management in Engineering, 37(2), p.04020115. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000884.
17. Adedipe, T., Shafiee, M. and Zio, E., 2020. Bayesian network modelling for the wind energy industry: an overview. Reliability Engineering & System Safety, 202, p.107053. https://doi.org/10.1016/j.ress.2020.107053.
18. Guan, L., Abbasi, A. and Ryan, M.J., 2021. Simulation-based risk interdependency network model for project risk assessment. Decision Support Systems, 148, p.113602.https://doi.org/10.1016/j.dss.2021.113602.
19. Sharifi Ghazvini, M., Ghezavati, V., Makui, A. and Raissi, S., 2018. New multi-objective model for projects portfolio optimization considering integrated efficiency-risk approach using NSGAΙΙ. Research in Production and Operations
Management, 9(2), pp.139-157.10.22108/jpom.2018.109207.1107. [In Persian].
20. Nieto-Morote, A. and Ruz-Vila, F., 2011. Fuzzy approach to construction project risk assessment. International journal of project management, 29(2), pp.220-231. https://doi.org/10.1016/j.ijproman.2010.02.002.
21. Zadeh, L.A., 1965. Fuzzy sets. Information and Control, 8(3), pp.338-353. https://doi.org/10.1016/S0019-9958(65)90241-X.
22. Xu, Z., Khoshgoftaar, T.M. and Allen, E.B., 2003. Application of fuzzy expert systems in assessing operational risk of software. Information and Software Technology, 45(7), pp.373-388.https://doi.org/10.1016/S0950-5849(03)00010-7.
23. Jafarzadeh Ghoushchi, S., Ab Rahman, M.N., Raeisi, D., Osgooei, E. and Jafarzadeh Ghoushji, M., 2020. Integrated decision-making approach based on SWARA and GRA methods for the prioritization of failures in solar panel systems under zinformation. Symmetry, 12(2), p.310. https://doi.org/10.3390/sym12020310.
24. Kahraman, C., Cebeci, U. and Ruan, D., 2004. Multiattribute comparison of catering service companies using fuzzy AHP: the case of turkey. International Journal of Production Economics, 87(2), pp.171-184. https://doi.org/10.1016/S0925-5273(03)00099-9.
25. Batar, M., Birant, K.U. and Işık, A.H., 2021. Development of rule‐based software risk assessment and management method with fuzzy inference system. Scientific Programming, 2021(1),p.5532197. https://doi.org/10.1155/2021/5532197.
26. Nicholas, J.M. and Steyn, H., 2020. Project management for engineering, business and technology. Routledge.
https://doi.org/10.4324/9780429297588.
27. Izadi, B. and Shafie, M., 2018. Decision support system for evaluation and prioritization, the import risks to manage the effects of sanctions on iran (case study: farabi pharmaceutical company). Research in Production and Operations Management, 9(1), pp.79-106. 10.22108/jpom.2018.92395.0. [InPersian].
28. Vakilzadeh, M., Shayanfar, M., ZabihiSamani, M.and Ravanshadnia, M.,2022. Providing a model forsafety risk in construction projects using fuzzy expertsystem and genetic algorithm. The Journal of Productivity Management, 16(62), pp.99-12210.30495/qjopm.2021.1929803.3145. [In Persian].
29. Gheidar-Kheljani, J., Karimi Govareshki, M.H.,Babaei, M. and Masjedi, S., 2021. Risk assessment in production process of benzoic acid using HAZOP technique and fuzzy mathematics. Iranian Chemical Engineering Journal, 20(118), pp.50-65.10.22034/ijche.2021.269203.1089. [In Persian].
30. Rose, K.H., 2013. Guide to the project management body of knowledge (PMBOK guide). Project management Journal, 44(3), pp.e1-e1.10.1002/pmj.21345.
31. R Nik, E. and Elmi, F., 2021. Developmene of a decision support system framwork and project risk assesment with a combined multi-criteria decision-making and simulation approach. Sharif Journal of Industrial Engineering & Management, 36(2.2), pp.49-61. 10.24200/j65.2020.54821.2074. [InPersian].
32. Ghasemi, F., Doosti-Irani, A. and Aghaei, H., 2023. Applications, shortcomings, and new advances of job safety analysis (JSA): findings from a systematic review. Safety and Health at Work, 14(2), pp.153-162. https://doi.org/10.1016/j.shaw.2023.03.006. [InPersian].
33. Ghoushchi, S.J., Yousefi, S. and Khazaeili, M., 2019. Extended FMEA approach based on the z-moora and
fuzzy bwm for prioritization of failures. Applied Soft Computing, 81,p.105505. https://doi.org/10.1016/j.asoc.2019.105505
34. Cruz-Rivero, L., Méndez-Hernández, M.L., MarOrozco, C.E., Aguilar-Lasserre, A.A., BarbosaMoreno, A. and Sánchez-Escobar, J., 2022. Functional evaluation using fuzzy FMEA for a noninvasive measurer for methane and carbone
dioxide. Symmetry, 14(2), p.421.https://doi.org/10.3390/sym14020421.
35. Baghery, M., Yousefi, S. and Rezaee, M.J., 2018. Risk measurement and prioritization of auto parts manufacturing processes based on process failure analysis, interval data envelopment analysis and grey relational analysis. Journal of Intelligent Manufacturing, 29(8), pp.1803-1825. https://doi.org/10.1007/s10845-016-1214-1.
36. Jafarzadeh-Ghoushchi, S. and Rahman, M.N.A., 2016. Performance study of artificial neural network modelling to predict carried weight in the transportation system. International Journal of Logistics Systems and Management, 24(2), pp.200-212. https://doi.org/10.1504/IJLSM.2016.076473.
37. Hosseinzadeh, M., Mehregan, M.R. and Ghomi, M., 2019. Identifying and analyzing supply chain risks of
saipa automobile company using the coso model and social network analysis (SNA). Research in Production and Operations Management, 10(1), pp.111-132. 10.22108/jpom.2018.107972.1093. [InPersian].
38. Dabbagh, R. and Yousefi, S., 2019. Hybrid decisionmaking approach based on FCM and MOORA for occupational health and safety risk analysis. Journal of Safety Research, 71, pp.111-123. https://doi.org/10.1016/j.jsr.2019.09.021
39. Tsai, S.B., Yu, J., Ma, L., Luo, F., Zhou, J., Chen, Q.and Xu, L., 2018. Study on solving the production process problems of the photovoltaic cell industry. Renewable and Sustainable Energy Reviews, 82, pp.3546-3553.
https://doi.org/10.1016/j.rser.2017.10.105
40. Golkhani, F., Ghotbi Ravandi, M.R., Baesmat, S. and Abasi Balochkhane, F., 2018. The use of failure mode effects analysis (FMEA) and analytic hierarchy process (AHP) methods to determine the most important safety hazards. Health Education and Health Promotion, 6(1), pp.17-21. 10.29252/HEHP.6.1.17 . [In Persian].
41. Jeet, K. and Dhir, R., 2012. Bayesian and fuzzy approach to assess and predict the maintainability of software: a comparative study. International Scholarly Research Notices, 2012(1), p.202980.10.5402/2012/202980
42. Grossman, D. and Domingos, P., 2004. Learning bayesian network classifiers by maximizing conditional likelihood. Proceedings of the TwentyFirst International Conference on Machine learning , pp. 46. https://doi.org/10.1145/1015330.1015339
43. Adusei-Poku, K., 2005. Operational risk management-implementing a bayesian network for foreign exchange and money market settlement (doctoral dissertation, Niedersächsische Staats-und Universitätsbibliothek Göttingen).
http://dx.doi.org/10.53846/goediss-3010
44. Yekkeh, H., Jafari, S.M., Mahmoudi, S.M. and ShamiZanjani, M., 2021. Designing the adaptive fuzzy-neural inference system to measure the benefits of knowledge management in the organization. Iranian Journal of Information Processing and Management, 37(1), pp.288-303. https://doi.org/10.52547/jipm.37.1.288. [In Persian].
45. Zaman, M. and Hassan, A., 2021. Fuzzy heuristics and decision tree for classification of statistical
feature-based control chart patterns. Symmetry, 13(1), p.110. https://doi.org/10.3390/sym13010110
46. Zarandi, M.F., Alaeddini, A. and Turksen, I.B., 2008. Hybrid fuzzy adaptive sampling–run rules for shewhart control charts. Information Sciences, 178(4), pp.1152-1170.https://doi.org/10.1016/j.ins.2007.09.028
47. Bhowmik, P., Udgata, G. and Trivedi, S., 2022. Risk assessment in construction industry using a fuzzy logic. In Recent Developments in Sustainable Infrastructure (ICRDSI-2020)—Structure and Construction Management: Conference Proceedings from ICRDSI-2020, 1 , pp. 517-526. https://doi.org/10.1007/978-981-16-8433-3_44
48. Nikmanesh, M., Feili, A. and Sorooshian, S., 2023. Employee productivity assessment using fuzzy inference system. Information, 14(7), p.423. https://doi.org/10.3390/info14070423
مهندسی صنایع و مدیریت
دوره 40، شماره 2
اسفند 1403
صفحه 57-72

فایل ها

هم رسانی

ارجاع به این مقاله

آمار