تسطیح منابع پروژه تحت شرایط فازی-تصادفی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، مدیریت صنعتی، گرایش تحقیق در عملیات، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

2 استاد، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

چکیده

با توجه به نقش مهمی که منابع در موفقیت یا شکست پروژه‌ها بازی می‌کنند، در 60 سال اخیر پژوهش‌های بسیاری در زمینه تسطیح منابع انجام گرفته است. نخستین پژوهش‌ها شرایط اجرای پروژه را قطعی در نظر گرفتند ولی پژوهش‌های بعدی به سمت غیر قطعی بودن شرایط پروژه سوق پیدا کردند. برخی از این پژوهش‌های غیرقطعی شرایط اجرای پروژه را تنها فازی و برخی آن را تنها تصادفی فرض کردند. پس از معرفی تئوری فازی-تصادفی پژوهش‌های مدیریت پروژه شرایط اجرای یک پروژه را فازی-تصادفی دانستند. بنابراین با توجه به جای خالی این رویکرد در تسطیح منابع، پژوهش کمی و توسعه‌ای پیش‌رو یک مدل تسطیح منابع فازی-تصادفی چند هدفه توسعه داد. در این پژوهش زمان انجام پروژه به صورت یک متغیر فازی-تصادفی در نظر گرفته شده است. در نهایت مدل ارایه شده که در زمره مدل‌های NP-hard قرار می‌گیرد، توسط یک الگوریتم NSGA-II توسعه داده شده، در نرم افزار Matlab حل شد. این الگوریتم توسط دو الگوریتم دیگر یعنی الگوریتم کنترل و الگوریتم آماده‌ساز متغیر تصمیم و یک حافظه کنترل کننده، برای حل مشکل تنوع پروژه‌ها، توسعه داده شد. نوآوری این پژوهش در دو مورد قابل ذکر است، اول اینکه مدل تسطیح منابع چندهدفه به صورت فازی-تصادفی ارایه شد و مورد دوم اینکه برای حل آن الگورتیم NSGA-II توسعه داده شد. در پایان نیز تکرارپذیری، همگرایی، کارآیی و اعتبار الگوریتم پیشنهادی آزمایش، مورد بحث و تایید قرار گرفت.

کلیدواژه‌ها

عنوان مقاله [English]

Leveling of Project Resources under Fuzzy- Stochastic Conditions

نویسندگان [English]

  • Farnoosh khaledian 1
  • Mansour Momeni 2
1 PhD Student, Industrial Management, Operations Research, School of Management, University of Tehran, Tehran, Iran
2 Professor, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
چکیده [English]

Due to the vital role resources play in the project's success or failure, in the last 60 years, much research has been done in the field of resource-leveling. The first studies considered the conditions of the project to be definite, but the following researches led to the uncertainty of the project conditions. Some of these uncertain studies assumed that the project conditions were only fuzzy, and some assumed that they were only stochastic. After introducing fuzzy-stochastic theory, project management research considered the conditions for a project to be fuzzy-stochastic. Due to the gap of this approach in resource leveling, this quantitative and developing research developed a multi-objective fuzzy-random resource-leveling model. In this research, the project execution time is considered as a fuzzy-random variable. Finally, the proposed model, which is among the NP-hard models, was solved by an NSGA-II algorithm in Matlab software. Two other algorithms developed this algorithm, namely control algorithm and variable decision preparation algorithm, and a control-memory, to solve the problem of project's diversity. The innovation of this research is noteworthy in two cases. The first is that the multi-objective resource-leveling model was presented in a fuzzy-random manner, and the second is that the NSGA-II algorithm was developed to solve it. Finally, the proposed algorithm's reproducibility, convergence, efficiency, and validity were discussed and approved.

کلیدواژه‌ها [English]

  • Resource Leveling
  • Fuzzy-Stochastic
  • Improved NSGA-II Algorithm

مراجع

[1]          Aliyeh, K. F., Sarvandy. (2018). Mathematical modeling of project scheduling problem with resource constraint approach and solving it using meta-heuristic algorithms. Modern Research in Decision Making.
[2]          Zamani, A., Khanzadi, M., Jabal Ameli, M. S., & Sarhadi, M. (2017). Developing a Framework for Applying Risk Management in a Fuzzy Environment for Implementing Construction Projects Value Engineering: Case of Khorramshahr Port. Management Research in Iran, 21(3), 139-166.
[3]          Abbasnejad, T., Behboudi, M. R., Sahelizadegan, F., & Mahmoodi, J. (2017). Strategic performance measurement of employees based on project efficiency and effectiveness. Iranian Journal of Management Studies, 10(1), 207-236. (In Persian).
[4]          Neumann, K., Schwindt, C., & Zimmermann, J. (2012). Project scheduling with time windows and scarce resources: temporal and resource-constrained project scheduling with regular and nonregular objective functions: Springer Science & Business Media.
[5]          Ameer, A. A. A., & Mohammed, S. R. (2011). Optimization Of Resource Allocation And Leveling Using Genetic Algorithms. Journal of Engineering, 17(4), 929-947.
[6]          Li, H., Wang, M., & Dong, X. (2019). Resource leveling in projects with stochastic minimum time lags. Journal of construction engineering and management, 145(4), 04019015.
[7]          Yousefi Hanoomarvar, A., Amiri, M., Olfat, L., & Naser Sadrabadi, A. (2021). Time-Cost-Quality Trade Off in PERT Networks Using Neural Network and Evolutionary Algorithms. Modern Research in Decision Making, 6(1), 92-122.
[8]          Burgess, A., & Killebrew, J. B. (1962). Variation in activity level on a cyclical arrow diagram. Journal of Industrial Engineering, 13(2), 76-83.
[9]          Mattila, K. G., & Abraham, D. M. (1998). Resource leveling of linear schedules using integer linear programming. Journal of construction engineering and management, 124(3), 232-244.
[10]        Rieck, J., Zimmermann, J., & Gather, T. (2012). Mixed-integer linear programming for resource leveling problems. European Journal of Operational Research.
[11]        Coughlan, E. T., Lübbecke, M. E., & Schulz, J. (2015). A branch-price-and-cut algorithm for multi-mode resource leveling. European Journal of Operational Research. 245(1), 70-80.
[12]        Bandelloni, M., Tucci, M., & Rinaldi, R. (1994). Optimal resource leveling using non-serial dyanamic programming. European Journal of Operational Research, 78(2), 162-177.
[13]        Geng, J.-q., Weng, L.-p., & Liu, S.-h. (2011). An improved ant colony optimization algorithm for nonlinear resource-leveling problems. Computers & Mathematics with Applications, 61(8), 2300-2305.
[14]        Leu, S.-S., & Hung, T.-H. (2002). An optimal construction resource leveling scheduling simulation model. Canadian Journal of Civil Engineering. 275-267, (2)29.
[15]        Leu, S.-S., Chen, A.-T., & Yang, C.-H. (1999). A fuzzy optimal model for construction resource leveling scheduling. Canadian Journal of Civil Engineering, 26(6), 673-684.
[16]        Wang, S., & Watada, J. (2012). Fuzzy stochastic optimization: theory, models and applications: Springer Science & Business Media.
[17]        Alipouri, Y., Sebt, M. H., Ardeshir, A., & Zarandi, M. H. F. (2020). A mixed-integer linear programming model for solving fuzzy stochastic resource constrained project scheduling problem. Operational Research, 1-21.
[18]        Gang, J., Xu, J., & Xu, Y. (2013). Multiproject resources allocation model under fuzzy random environment and its application to industrial equipment installation engineering. Journal of Applied Mathematics, 2013.
[19]        Fan, G.-M., & Huang, H.-J. (2017). A novel binary differential evolution algorithm for a class of fuzzy-stochastic resource allocation problems. Paper presented at the 2017 13th IEEE International Conference on Control & Automation (ICCA).
[20]        Leu, S.-S., Yang, C.-H., & Huang, J.-C. (2000). Resource leveling in construction by genetic algorithm-based optimization and its decision support system application. Automation in Construction, 10(1), 27-41.
[21]        Savin, D., Alkass, S., & Fazio, P. (1996). Construction resource leveling using neural networks. Canadian Journal of Civil Engineering, 23(4), 917-925.
[22]        Son, J., & Skibniewski, M. J. (1999). Multiheuristic approach for resource leveling problem in construction engineering: Hybrid approach. Journal of construction engineering and management, 125(1), 23-31.
[23]        Hossein Hashemi Doulabi, S., Seifi, A., & Shariat, S. Y. (2011). Efficient hybrid genetic algorithm for resource leveling via activity splitting. Journal of construction engineering and management, 137(2), 137-146.
[24]        Ashuri, B., & Tavakolan, M. (2012). Fuzzy enabled hybrid genetic algorithm–particle swarm optimization approach to solve TCRO problems in construction project planning. Journal of construction engineering and management, 138(9), 1065-1074.
[25]        Li, H., & Demeulemeester, E. (2016). A genetic algorithm for the robust resource leveling problem. Journal of Scheduling, 19(1), 43-60.
[26]        Li, H., Xu, Z., & Demeulemeester, E. (2015). Scheduling policies for the stochastic resource leveling problem. Journal of construction engineering and management, 141(2), 04014072.
[27]        Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
[28]        Heon Jun, D., & El-Rayes, K. (2011). Multiobjective optimization of resource leveling and allocation during construction scheduling. Journal of construction engineering and management, 137(12), 1080-1088.
[29]        Masoomi, Z., Mesgari, M. S., & Hamrah, M. (2013). Allocation of urban land uses by Multi-Objective Particle Swarm Optimization algorithm. International Journal of Geographical Information Science, 27(3), 542-566.
[30]        Deb, K. (2011). Multi-objective optimisation using evolutionary algorithms: an introduction. In Multi-objective evolutionary optimisation for product design and manufacturing (pp. 3-34): Springer.
پژوهش های نوین در تصمیم گیری
دوره 6، شماره 3
مهر 1400
صفحه 129-154

فایل ها

هم رسانی

ارجاع به این مقاله

آمار