مدل هیبریدی روش زنجیره بحرانی و الگوریتم انتخاب مبتنی ‌بر الگوی پارتو برای انتخاب پروژه

شهرکی, محمد رضا; چرویده, جلیل

doi:10.22065/jsce.2024.426854.3283

مدل هیبریدی روش زنجیره بحرانی و الگوریتم انتخاب مبتنی ‌بر الگوی پارتو برای انتخاب پروژه

نوع مقاله : علمی - پژوهشی

نویسندگان

محمد رضا شهرکی ¹

جلیل چرویده ²

¹ دانشیار، دانشکده مهندسی صنایع، دانشگاه سیستان و بلوچستان، زاهدان، ایران

² گروه مهندسی صنایع- دانشکده مهندسی - دانشگاه سیستان و بلوچستان- زاهدان-ایران

10.22065/jsce.2024.426854.3283

چکیده

پروژه‌ مجموعه‌ای از فعالیت‌ها است که برای رسیدن به هدفی خاص انجام می‌شود و باید در زمان مشخص شده، با هزینه برآورد شده و کیفیت تعیین شده به انجام برسد. مسئله انتخاب یک پروژه از میان چندین پروژه تحت شرایط محدودیت منابع، مسئله‌ای بسیار مهم در حوزۀ مدیریت پروژه محسوب می‌شود. زنجیره بحرانی از روش‌های نوین مورد استفاده در برنامه‌ریزی پروژه‌‌ها است، که توجه بسیاری از پژوهشگران را به خود معطوف نموده است. در مطالعۀ حاضر، مسئله انتخاب یک پروژه از میان چندین پروژه با در نظر گرفتن اهدافی شامل انجام پروژه در کمترین زمان و هزینه و در بالاترین سطح کیفیت با استفاده از رویکرد زنجیره بحرانی، مدل‌سازی شده است. مدل پیشنهادی برای سه پروژه در اندازه‌ های متفاوت و با استفاده از تکنیک زنجیره بحرانی و الگوریتم فراابتکاری PESA-II اجرا گردیده است. نتایج نشان داد که الگوریتم PESA-II عملکرد مناسبی داشته و از نظر تابع هدف زمان، هزینه و کیفیت جواب‌ های الگوریتم PESA-II نسبت به الگوریتم های دیگر برتری دارد و لذا می‌توان نتیجه گرفت که الگوریتم PESA-II موجب تولید جواب‌هایی با مقادیر پارتو بهتر در سه تابع هدف زمان، هزینه و کیفیت نسبت به سایر الگوریتم‌ ها می‌گردد. با توجه به اینکه نتایج بدست آمده شامل ترکیبی از مقادیر سه تابع هدف زمان، هزینه و کیفیت می‌باشند، این امر مدیران پروژه را قادر‌ می‌سازد که با توجه به نیاز و سیاست‌ های خود از نظر زمانی، هزینه‌ ای و کیفی بهینه‌ ترین ترکیب را انتخاب نمایند.

کلیدواژه‌ها

پروژه

زنجیره

بحرانی

محدودیت

منابع

پارتو

موضوعات

بهینه سازی

عنوان مقاله English

Hybrid model of critical chain method and selection algorithm based on Pareto pattern for project selection

نویسندگان English

Mohammad Reza Shahraki ¹

Jalil Charvideh ²

¹ Associate Professor, Faculty of Industrial Engineering, University of Sistan and Baluchestan, Zahedan, Iran

² Department of Industrial Engineering - Faculty of Engineering -University of Sistan and Baluchestan - Zahedan - Iran

چکیده English

A project is a set of activities that are carried out to achieve a specific goal and must be completed in a specified time, with an estimated cost and a specified quality. The problem of choosing a project among several projects under the conditions of resource limitations is considered a very important problem in the field of project management. Critical chain is one of the new methods used in project planning, which has attracted the attention of many researchers. In the present study, the problem of selecting a project from among several projects has been modeled by considering the goals of completing the project in the least time and cost and at the highest level of quality using the critical chain approach. The proposed model has been implemented for three projects of different sizes using the critical chain technique and PESA-II meta-heuristic algorithm. The results showed that the PESA-II algorithm performed well and in terms of the objective function of time, cost and quality of the solutions of the PESA-II algorithm is superior to other algorithms and therefore it can be concluded that the PESA-II algorithm produces solutions with better Pareto values. In three objective functions, time, cost and quality are compared to other algorithms. Considering that the obtained results include a combination of the values of the three objective functions of time, cost and quality, this enables the project managers to choose the most optimal combination in terms of time, cost and quality according to their needs and policies.

کلیدواژه‌ها English

Project

Selection

Critical

Chain

Resource

Constraints

Pareto

[1] Bineshian, M., Safari, S., Abbasi, R., and Momeni, M. (2018). “Optimization of organization portfolio; clustering approach and fuzzy multi-criteria decision making”, Modern Researches in decision making, 3(2), pp. 81-106.

[2] Mahdavi, I., Hemmatian, M., Taheri Amiri, M. J., and ghenaat, O. (2019). “Presentation of exact and metaheurestic solution method for minimization project completion time with considering budget constraint problem”, Journal of Structural and Construction Engineering, 6 (3), pp. 41-56.

[3] Alemtabriz, A., Ayough, A. and Baniasadi, M. (2016). “Presentation and Solution of Critical Chain Project Scheduling Problem (CCPSP) model with consideration of feeding buffer”, Journal of Industrial Management Studies, 14(42), pp. 31-59.

[4] Soltani, R., Jolai, F., and Zandieh, M. (2010). “Two Robust Meta-Heuristics for Scheduling Multiple Job Classes on a Single Machine with Multiple Criteria”, Expert Systems with Applications, 37(8), pp. 5951-5959.

[5] Peteghem, V.V., and Vanhoucke, M. (2010). “A Genetic Algorithm for the Preemptive and Non-Preemptive Multi-Mode Resource-Constrained Project Scheduling Problem”, European Journal of Operational Research, 201(2), pp. 409–418.

[6] Shen, L, Chua, D. (2008). “An Investigation of Critical Chain and Lean Project Scheduling”, 16th Annual Conference of the International Group for Lean Construction, United States.

[7] startton, R. (2009). “Critical Chain Project Management Theory and Practice”, POMS 20th Annual Conference, USA.

[8] Wei-Xin, W., Xu, W., Xian-Long, G., and Lei, D. (2014). "Multi-objective optimization model for multi-project scheduling on critical chain", Advances in Engineering Software, Vol 68, pp. 33–39.

[9] Hazir, O., Houari, M., and Erel, E. (2010). “Robust scheduling and robustness measures for the discrete time/cost tradeoff problem”, European Journal of Operational Research, 207(2), pp. 633-643.

[10] Wuliang, P, Xueying, L, Yong ping, H. (2013). “A Genetic Algorithm for Solving Multi-Mode Critical Chain Project Scheduling Problem”, Journal of Convergence Information Technology, 8(3), pp. 666-673.

[11] Liu, zh., Yang, L., Deng, R., and Tian, J. (2017). “An effective approach with feasible space decomposition to solve resource-constrained project scheduling problems”, Automation in Construction, Vol 75, pp. 1-9.

[12] Taheri Amiri, M. J., Haghighi, F., Eshtehardian, E., and Abessi, O., (2018). “Multi-project time-cost optimization in critical chain with resource constraints”, KSCE Journal of Civil Engineering, 22(10), pp. 3738-3752.

[13] Taheri Amiri, M.J., Haghighi, F., Eshtehardian, E., and Abessi, O., (2019). “Time-Cost-Quality trade off in Critical Chain Method with multi-mode activities by Multi Objective Particle Swarm Optimization”, Journal of Structural Engineering and Construction. 6(1), pp. 134-154.

[14] Taheri Amiri, M. J., Haghighi, F. R., Eshtehardian, E., Hemmatian, M., and Khaleqnejad, R. (2020).” Optimization of time, cost and quality in critical chain method in multi-project Scheduling and resource constraints with considering utility function”, Journal of Structural and Construction Engineering, 7 (3), pp. 87-108.

[15] Taheri Amiri, M. J., Hemmatian, M., and Asghari, A. (2021). “Prioritize and Optimal Selection of Projects with Best to Worth method VIKOR and Mathematical Programming”, Journal of Structural and Construction Engineering, (In Persian) (2020) doi: 10.22065 / jsce.2020.209215.2000.

[16] Shahraki, M. R., & charvideh, J. (2022). Optimizing project selection using Tabu Search algorithm according to time, cost and quality and resource constraints in the critical chain method. Journal of Structural and Construction Engineering, 9(3), 43-64. doi: 10.22065/jsce.2021.274317.2368.

[17] Jones, D.F., Mirrazavi, S.K. and Tamiz, M. (2002). “Multi-objective meta- heuristic: An overview of the current state-of-the-art”, European Journal of Operational Research, 137(1), pp. 1-9.

[18] Chen, G., and Junhua, L., (2019). “a diversity ranking based evolutionary algorithm for multi objective and many objective optimization”, Swarm and Evolution. Computation, Vol. 48, pp. 274-287.

[19] Lv, L., Zhao, J., Wang, J., and Fan, T., (2019). “Multi objective firefly algorithm based on compensation factor and elite learning”, Future. Gener. Comput. Syst. Vol.91, pp. 37-47.

[20] Laio, Y., Silva, T. V., Herthel, A. B., and Subramanian, A., (2019). “A multi objective evolutionary algorithm for a class of mean-variance portfolio selection problems”, Expert Systems with Applications, Vol. 133, pp. 225-241.

[21] Corne, D., Jerram, N. R., Knowles, J. D., and Oates, M., (2001). “PESA-II: Region-based selection in evolutionary multi objective optimization”, Proceedings of the Genetic and Evolutionary Computation Conference, University of Reading, UK, pp. 283-290.

[22] Poursalehi, N., Zolfaghari, A., and Minuchehr, A. (2012). “Performance comparison of zeroes order nodal expansion methods in 3D rectangular geometry”, Nuclear Engineering and Design. Vol. 252, pp. 248-266.

[23] Tran, D. H., Cheng, M. Y., and Cao, M. T., (2015). “Hybrid multiple objective artificial bee colony with differential evolution for the time-cost-quality trade off problem”, Knowledge-Based Systems, 74(1), pp. 176-186.

[24] Feng, C., Liu, L., and Burns, S. (2000). “Stochastic Construction Time Cost Trade-off Analysis”, Journal of Computing in Civil Engineering, 14(2), pp. 117-126.

[25] Hegazi, T., (2011). “Optimization of construction time-cost trade-off analysis using genetic algorithms”, Canadian Journal of Civil Engineering, 26(6), pp. 685-697.

[26] Zhang, L., Du, J., and Zhang, S. (2014). “Solution to Time-Cost-Quality Trade-off Problem in Construction Projects Based on Immune Genetic Particle Swarm Optimization”, Journal of Management in Engineering, 30(2), pp. 163- 172.

[27] El-Rayes, K., and Kandil, A. A. (2005). “Time-Cost-Quality Trade-off Analysis for Highway Constructions”, Journal of Construction Engineering and Management, 131(4), pp. 447-486 (2005).