Document Type : original-application paper

Authors

Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

Abstract

In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of the project has been used. However, using this method to valuate project time performance by utilizing the time performance index (SPI) by researchers and practitioners has been faced with serious criticism. Therefore, the present study proposes a framework for assessment and prediction of the temporal performance of each of the thread activities in project management. In this framework, using the multi objective league championship algorithm (MOLCA), the initial plan of the projects is optimized and then via using the Kalman Filter prediction method, project execution planning is done such that the projects in conditions of uncertainty could be forecasted and ahead horizon being demonstrated accurately with the least error for project managers. In this paper, in order to ensure the quality of the solutions, the output of the algorithm is compared with genetic algorithms (NSGII) and particle swarm optimization (MOPSO), where results demonstrate the superiority of the proposed algorithm.

Keywords

Main Subjects

دیرانلو، م. (1385). ارائه یک الگوریتم ژنتیک کارا جهت حل مسئله زمان‌بندی پروژه تحت شرایط محدودیت منابع (پایان‌نامه دانشگاه تهران).
سبزه پرور، م. (1389). مرجع درسی و کاربردی کنترل پروژه (نسخه ششم). تهران: نشر ترمه.
احسان اله اشتهاردیان، ع. ا. (1385). بهینه‌سازی موازنه هزینه – زمان: استفاده از الگوریتم ژنتیک و منطق فازی در عدم قطعیت هزینه‌ها. سومین کنفرانس بین‌المللی مدیریت پروژه. تهران، گروه پژوهشی آریانا.
Larson, E. W., & Gray, C. F. (2015). A Guide to the Project Management Body of Knowledge: PMBOK (®) Guide. Project Management Institute.
Azeem, S. A., Hosny, H. E., & Ibrahim, A. H. (2014). Forecasting project schedule performance using probabilistic and deterministic models. HBRC journal, 10(1), 35-42.
Kim, B. C., & Reinschmidt, K. F. (2010). Probabilistic forecasting of project duration using Kalman filter and the earned value method. Journal of construction engineering and management, 136(8), 834-843.
Hass, K. B. (2009). Managing Complex Projects: A New Model. Vienna. VA: Management Concepts.
Demeulemeester, E. L., & Herroelen, W. S. (2006). Project scheduling: a research handbook. Springer Science & Business Media.
Kazemi, F. S., & Tavakkoli-Moghaddam, R. (2011). Solving a Multi-Objective Multi-Mode Resource-Constrained Project Scheduling Problem with Particle Swarm Optimization. International journal of academic research3(1), 103-110.
 Kumar, N., & Vidyarthi, D. P. (2016). A model for resource-constrained project scheduling using adaptive PSO. Soft computing, 20(4), 1565-1580.
He, Z., Liu, R., & Jia, T. (2012). Metaheuristics for multi-mode capital-constrained project payment scheduling. European journal of operational research, 223(3), 605-613.
Gonçalves, J. F., Mendes, J. J., & Resende, M. G. (2008). A genetic algorithm for the resource constrained multi-project scheduling problem. European journal of operational research, 189(3), 1171-1190.
Orji, M. J., & Wei, S. (2013). Project scheduling under resource constraints: a recent survey. International journal of engineering research and technology, 2(2), 1-20.
Padalkar, M., & Gopinath, S. (2016). Are complexity and uncertainty distinct concepts in project management? A taxonomical examination from literature. International journal of project management, 34(4), 688-700.
 Daniel, P. A., & Daniel, C. (2018). Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in project management. International journal of project management, 36(1), 184-197.
Turner, J. R. (2014). Handbook of project-based management. New York, NY: McGraw-hill.
Chen, A. H., & Chyu, C. C. (2008, June). A memetic algorithm for maximizing net present value in resource-constrained project scheduling problem. Proceedings of 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence) (pp. 2396-2403). Hong Kong, China: IEEE.
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1-20.
Kashan, A. H. (2009, December). League championship algorithm: a new algorithm for numerical function optimization. Proceedings of 2009 international conference of soft computing and pattern recognition (pp. 43-48). Malacca, Malaysia: IEEE.
Hegazy, T., & Kassab, M. (2003). Resource optimization using combined simulation and genetic algorithms. Journal of construction engineering and management, 129(6), 698-705.
Feng, C. X. J., Saal, A. L., Salsbury, J. G., Ness, A. R., & Lin, G. C. (2007). Design and analysis of experiments in CMM measurement uncertainty study. Precision engineering, 31(2), 94-101.
Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations research, 21(5), 1154-1157.
Kim, B. C. (2007). Forecasting project progress and early warning of project overruns with probabilistic methods. Texas A&M University.
Browning, T. R. (2014). Managing complex project process models with a process architecture framework. International journal of project management, 32(2), 229-241.
Ramasesh, R. V., & Browning, T. R. (2014). A conceptual framework for tackling knowable unknown unknowns in project management. Journal of operations management, 32(4), 190-204.
Qureshi, S. M., & Kang, C. (2015). Analysing the organizational factors of project complexity using structural equation modelling. International journal of project management, 33(1), 165-176.
Saunders, F. C., Gale, A. W., & Sherry, A. H. (2015). Conceptualising uncertainty in safety-critical projects: A practitioner perspective. International journal of project management, 33(2), 467-478.
Attarzadeh, M., & Chua, D. K. (2011). Performance risk evaluation of long term infrastructure projects (PPP-BOT projects) using probabilistic methods. EPPM, Singapore, 20-21.
Kim, B. C. (2011). Probabilistic performance risk evaluation of infrastructure projects: First international symposium on uncertainty modeling and analysis and management (ICVRAM 2011); and fifth international symposium on uncertainty modeling and anaylsis (ISUMA) (pp. 292-299). Hyattsville, Maryland, United States: American Society of Civil Engineers.