ارائه مدلی برای متعادل سازی سبد پروژه ساختمانی در سازمان های پروژه‌محور مبتنی بر بودجه سازمان و تورم های ساخت و مسکن ایران
محورهای موضوعی : مدیریت
رضا رجبی
1
,
سیامک حاجی یخچالی
2
1 - دانشکده مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 - استادیار دانشکده مهندسی صنایع، پردیس دانشکده های فنی، دانشگاه تهران، تهران، ایران
کلید واژه: متعادل‌سازی سبد, بهینه سازی سبد, مدیریت سبد, جریان نقدی, تورم, دوره های رونق و رکود, الگوریتم های فراابتکاری,
چکیده مقاله :
جریان‌های نقدی ورودی و خروجی سبد در سازمان‌های مبتنی بر پروژه‌های ساختمانی، عمدتاً به دلیل فاصله زمانی بین هزینه‌های انجام شده و درآمدهای کسب شده، در طول دوره سبد پروژه متعادل نیست. ماهیت سبد پروژه های ساختمانی به گونه ای است که باید زمان زیادی صرف شود تا پروژهها تکمیل و آماده فروش شوند تا سازمان به درآمدزایی برسد؛ این موضوع سازمان‌های پروژه‌محور ساختمانی را همواره در خطر مواجهه با جریان نقدی منفی و افزایش فشار مالی قرار داده است. بنابراین، هدف این پژوهش به حداکثر رساندن سود سبد پروژه از دیدگاه سازمان است و در آن پارامترهای تورم هزینه های ساخت، تورم قیمت مسکن در دوره‌های رونق و رکود و میزان آورده سازمان (بودجه) درنظرگرفته می شود. همچنین حداکثر تراز منفی جریان نقدی تجمعی سبد به میزان تزریق سرمایه سازمان محدود می گردد تا جواب بهینه مدل کاملاً متناسب با آورده نقدی و سرمایه در دسترس سازمان باشد. برای حل مساله بهینه یابی از سه الگوریتم فراابتکاری چرخه آب (WCA)، تکامل تفاضلی (DE) و بهینه سازی مبتنی بر یاددهی-یادگیری (TLBO) استفاده می شود. برای اعتبارسنجی مدل، مجموعه ای از پنج پروژه ساختمانی واقعی در تهران بهینه شده است. نتایج بهینه سازی در مقایسه با تجزیه و تحلیل انجام شده توسط دفتر مدیریت پروژه (PMO)، روش سنتی، نشان دهنده بهبود قابل توجهی در مقدار تابع هدف است. علاوه بر این، WCA در مقایسه با سایر الگوریتم‌ها، عملکرد بهتری را در این مساله نشان داد.
Cash inflows and outflows in construction project-based organizations often lack balance due to delays between expenses and revenue throughout the duration of portfolio, primarily attributable to the temporal discrepancy between incurred expenditures and realized revenues. The intrinsic characteristics of construction project portfolio necessitate a long period for project completion and subsequent readiness for sale to facilitate revenue generation for the organization. consequently, organizations reliant on construction projects now run the danger of experiencing severe negative cash flow and mounting financial strain. Thus, the goal of this study is to maximize profit from the perspective of the organization, taking into account factors such as construction costs inflation, house prices inflation during boom-and-bust cycles, and the organization's capital. The amount of cumulative capital injection sets the maximum negative level of cumulative portfolio cash flow so that the optimal solution of the model is fully proportional to the capital brought into portfolio. Three meta-heuristic algorithms including water cycle algorithm, differential evolution and teaching-learning-based optimization are used to solve the algorithm. A portfolio of five real construction projects in Tehran is optimized in order to verify the model. The objective function showed a notable improvement when the optimization findings were compared to the analysis carried out using the traditional method by the Project Management Office (PMO). Additionally, WCA performed better in this problem when compared to other algorithms.
Abbasianjahromi, H., & Rajaie, H. (2012). Developing a project portfolio selection model for contractor firms considering the risk factor. Journal of Civil Engineering and Management, 18(6), 879–889. https://doi.org/10.3846/13923730.2012.734856
Akbaş, S., Dalkilic, T.E., & Aksoy, T.G. (2022). Study on portfolio selection based on fuzzy linear programming. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 30(2), 211–230. https://doi.org/10.1142/S021848852250009X
Alavipour, S.R., & Arditi, D. (2018). Optimizing financing cost in construction projects with fixed project duration. Journal of Construction Engineering and Management, 144(4), 04018012. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001451 Arditi, D., Koksal, A., & Kale, S. (2000). Business failures in the construction industry. Engineering, Construction and Architectural Management, 7(2), 120–132. https://doi.org/10.1108/eb021137
Aritua, B., Smith, N.J., & Bower, D. (2009). Construction client multi-projects–A complex adaptive systems perspective. International Journal of Project Management, 27(1), 72–79. https://doi.org/10.1016/j.ijproman.2008.02.005
Asadujjaman, M., Rahman, H.F., Chakrabortty, R.K., & Ryan, M.J. (2020). An Immune Genetic Algorithm for Resource Constrained Project Scheduling Problem with Discounted Cash Flows. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEE. 1179–1183. https://doi.org/10.1109/IEEM45057.2020.9309728
Barbosa, P.S.F., & Pimentel, P.R. (2001). A linear programming model for cash flow management in the Brazilian construction industry. Construction Management and Economics, 19(5), 469–479. https://doi.org/10.1080/01446193.2001.9709623
Borghesi, A., & Gaudenzi, B. (2013). Risk Management: How to Assess, Transfer and Communicate Critical Risks, Springer, Milan.
Boussabaine, A.H. & Kaka, A.P. (1998). A neural networks approach for cost flow forecasting. Construction Management and Economics, 19(4), 471–479. https://doi.org/10.1080/014461998372240
Casault, S., Groen, A.J., & Linton, J.D. (2013). Selection of a portfolio of R&D projects. In: Handbook on the theory and practice of program evaluation. Cheltenham: Edward Elgar Publishing, 89–115.
Cooper, R.G., Edgett, S.J., & Kleinschmidt, E.J. (1997). Portfolio management in new product development: Lessons from the leaders—I. Research-Technology Management, 40(5), 16–28. https://doi.org/10.1080/08956308.1997.11671152
Cox, J., & Ludvigson, S.C. (2021). Drivers of the Great Housing Boom-Bust: Credit Conditions, Beliefs, or Both? Real Estate Economics, 49(3), 843–875. https://doi.org/10.1111/1540-6229.12303
Curtis, C.C, Garin, J., & Mehkari. M.S. (2017). Inflation and the evolution of firm-level liquid assets. J Bank Finance, 81, 24–35. https://doi.org/10.1016/j.jbankfin.2017.04.008
Das, S.R., Kaznachey, D., & Goyal, M. (2014). Computing optimal rebalance frequency for log-optimal portfolios. Quantitative Finance, 14(8), 1489–1502. https://doi.org/10.1080/14697688.2014.887219
Dixit, V., & Tiwari, M.K. (2020). Project portfolio selection and scheduling optimization based on risk measure: a conditional value at risk approach. Ann Oper Res, 285, 9–33. https://doi.org/10.1007/s10479-019-03214-1
Drenovak, M., & Ranković, V. (2014). Markowitz portfolio rebalancing with turnover monitoring. Ekonomski horizonti, 16(3), 211–223. https://doi.org/ 10.5937/ekonhor1403211D
Ghasemzadeh, F., Archer, N., & Iyogun, P. (1999). A zero-one model for project portfolio selection and scheduling. Journal of the Operational Research Society, 50(7), 745–755. https://doi.org/10.1057/palgrave.jors.2600767
Gupta, P., Mittal, G., & Mehlawat, M.K. (2013). Expected value multiobjective portfolio rebalancing model with fuzzy parameters. Insurance. Mathematics and Economics, 52(2), 190–203. https://doi.org/10.1016/j.insmatheco.2012.12.002
Han, S.H., Diekmann, J.E., Lee, Y., & Ock, J.H. (2004). Multicriteria financial portfolio risk management for international projects. Journal of construction engineering and management, 130(3), 346–356. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:3(346)
He, Y., Zhang, J., & He, Z. (2019). Metaheuristic algorithms for multimode multiproject scheduling with the objective of positive cash flow balance. IEEE Access. 7, 157427–157436. https://doi.org/10.1109/ACCESS.2019.2944746
Hegazy, T. (1999). Optimization of resource allocation and leveling using genetic algorithms. Journal of construction engineering and management, 125(3), 167–175. https://doi.org/10.1061/(ASCE)0733-9364(1999)125:3(167)
Huang, C.C., Chu, P.Y., & Chiang, Y.H. (2008). A fuzzy AHP application in government-sponsored R&D project selection. Omega, 36(6), 1038–1052. https://doi.org/10.1016/j.omega.2006.05.003
Jalaee, S.A., Shakibaei, A., Horry, H.R., Akbarifard, H., GhasemiNejad, A., Robati, F.N., & Zarin, N.A. (2021). A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran. MethodsX, 8, 101226. https://doi.org/10.1016/j.mex.2021.101226
Karaboğa, D., & Ökdem, S. (2004). A simple and global optimization algorithm for engineering problems: differential evolution algorithm. Turkish Journal of Electrical Engineering & Computer Sciences, 12(1), 53–60.
Katoch, S., Chauhan, S.S., & Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80, 8091–8126. https://doi.org/10.1007/s11042-020-10139-6
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization, Proceedings of ICNN'95-International conference on neural networks. Perth, WA, Australia, IEEE. 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Kumar, P., Panda, G., & Gupta, U.C. (2015). Portfolio rebalancing model with transaction costs using interval optimization. Opseach, 52(4), 827–860. https://doi.org/10.1007/s12597-015-0210-0
Kumar, S., Tejani, G.G., Pholdee, N., Bureerat, S. & Jangir, P. (2022). Multi-objective teaching-learning-based optimization for structure optimization. Smart Science, 101(1), 56–67. https://doi.org/10.1080/23080477.2021.1975074
Li, Q., Qin, Z., & Yan, Y. (2022). Uncertain random portfolio optimization model with tail value-at-risk. Soft Computing, 26, 9385–9394. https://doi.org/10.1007/s00500-022-07249-8
Markowitz, H.M. (1952). Portfolio selection. The journal of finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x Masoumi, R., & Touran, A. (2016). A framework to form balanced project portfolios. Proceedings of the Construction Research Congress 2016, May 31- June 2 2016, San Juan, Puerto Rico, ASCE. 1772–1781. https://doi.org/10.1061/9780784479827.177
Mirjalili, S. (2019). Genetic Algorithm. In: Evolutionary Algorithms and Neural Networks. Studies in Computational Intelligence, 780, 43–55. Springer, Cham. https://doi.org/10.1007/978-3-319-93025-1
Mittal, G., & Mehlawat, M.K. (2014). A multiobjective portfolio rebalancing model incorporating transaction costs based on incremental discounts. Optimization, 63(10), 1595–1613. https://doi.org/10.1080/02331934.2014.891032
Musarat, M.A., Alaloul, W.S., & Liew, M.S. (2021). Impact of inflation rate on construction projects budget: a review. Ain Shams Eng J. Optimization, 12(1), 407–414. https://doi.org/10.1016/j.asej.2020.04.009
Platje, A., Seidel, H., & Wadman, S. (1994). Project and portfolio planning cycle: project-based management for the multiproject challenge. International Journal of Project Management, 12(2), 100–106. https://doi.org/10.1016/0263-7863(94)90016-7
PMI (2017). The Standard for Portfolio Management, Project Management Institute Inc, (fourth ed.), Pennsylvania. Purnus, A., & Bodea, C.N. (2015). Financial management of the construction projects: a proposed cash flow analysis model at project portfolio level. Organization, technology & management in construction: an international journal, 7(1), 1217–1227. https://doi.org/10.5592/otmcj.2015.1.6
Qin, Z., Kar, S., & Zheng, H. (2016). Uncertain portfolio adjusting model using semi absolute deviation. Soft Computing, 20, 717–725. https://doi.org/10.1007/s00500-014-1535-y
Rosłon, J., Książek-Nowak, M., Nowak, P., & Zawistowski, J. (2020). Cash-flow schedules optimization within life cycle costing (LCC). Sustainability, 12(19), 8201. https://doi.org/10.3390/su12198201
Sadollah, A., Eskandar, H., Bahreininejad, A., & Kim, J.H. (2015). Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Applied Soft Computing, 30, 58–71. https://doi.org/10.1016/j.asoc.2015.01.050
Samer Ezeldin, A., & Ali, G.G. (2017). Cash flow optimization for construction portfolios, International Conference on Sustainable Infrastructure, Reston, VA. ASCE. 26–37. https://ascelibrary.org/doi/abs/10.1061/9780784481202.003
Sanchez, H., Robert, B., Bourgault, M., & Pellerin, R. (2009). Risk management applied to projects, programs, and portfolios. International journal of managing projects in Business, 2(1), 14–35. https://doi.org/10.1108/17538370910930491
Shahid, M., Ashraf, Z., Shamim, M., & Ansari, M.S. (2023). Solving constrained portfolio optimization model using stochastic fractal search approach. International Journal of Intelligent Computing and Cybernetics, 16(2), 223–249. https://doi.org/10.1108/IJICC-03-2022-0086
Shiha, A., & Hosny, O. (2019). A Multi-Objective Model for Enterprise Cash Flow Management. In Proceedings, Annual Conference-Canadian Society for Civil Engineering.
Son, P.V.H., Duy, N.H.C., & Dat, P.T. (2021). Optimization of construction material cost through logistics planning model of dragonfly algorithm—particle swarm optimization. KSCE Journal of Civil Engineering, 25(7), 2350–2359. https://doi.org/10.1007/s12205-021-1427-5
Wang, M., Xu, F., & Wang, G. (2014). Sparse portfolio rebalancing model based on inverse optimization. Optimization Methods and Software, 29(2), 297–309. https://doi.org/10.1080/10556788.2012.700309
Woodside-Oriakhi, M., Lucas, C., & Beasley, J.E. (2013). Portfolio rebalancing with an investment horizon and transaction costs. Omega, 41(2), 406–420. https://doi.org/10.1016/j.omega.2012.03.003
Yu, J.R., & Lee, W.Y. (2011). Portfolio rebalancing model using multiple criteria. European Journal of Operational Research. 209(2), 166–175. https://doi.org/10.1016/j.ejor.2010.09.018
