Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms

Yıl 2018, Cilt: 22 Sayı: 2, 615 - 627, 15.08.2018

M. Hanefi Calp , M. Ali Akcayol

Öz

Projects consist of interconnected dimensions such as objective, time, resource and environment. Use of these dimensions in a controlled way and their effective scheduling brings the project success. Project scheduling process includes defining project activities, and estimation of time and resources to be used for the activities. At this point, the project resource-scheduling problems have begun to attract more attention after Program Evaluation and Review Technique (PERT) and Critical Path Method (CPM) are developed one after the other. However, complexity and difficulty of CPM and PERT processes led to the use of these techniques through artificial intelligence methods such as Genetic Algorithm (GA). In this study, an algorithm was proposed and developed, which determines critical path, critical activities and project completion duration by using GA, instead of CPM and PERT techniques used for network analysis within the scope of project management. The purpose of using GA was that these algorithms are an effective method for solution of complex optimization problems. Therefore, correct decisions can be made for implemented project activities by using obtained results. Thus, optimum results were obtained in a shorter time than the CPM and PERT techniques by using the model based on the dynamic algorithm. It is expected that this study will contribute to the performance field (time, speed, low error etc.) of other studies.

Anahtar Kelimeler

Project management, CPM; PERT; Project scheduling; Genetic algorithm; Optimization

Kaynakça

• [1] Yildiz, S. 2001. Resource Leveling and Earned Value Analysis in Project Management: an Application in Construction Sector, Master Thesis, Baskent University Institute of Social Sciences, Ankara, 7-37.
• [2] Sivri, G. 2001. Monitoring and controlling progress in construction projects and an application for project information management system, Master Thesis, Istanbul Technical University Institute of Social Sciences, Istanbul, 1-48.
• [3] Kutlu, N. T. 2001. Project planning techniques and a study on application of PERT technique in construction sector, Dokuz Eylul University Journal of Social Sciences Institute, Izmir, 3 (2): 164-207.
• [4] Duncan, W. R. 1996. A Guide to the Project Management Body of Knowledge, Project Management Institute, USA, 3-27.
• [5] Ozdemir, G. 2006. The genetic algorithm methods used in resource constrained project scheduling problems and their comparison, Master Thesis, Ankara University Institute of Social Sciences.
• [6] Hoscan, Y. 1988. Package programs developed for CPM/PERT methods and solution used to check the project, Anadolu University, Eskisehir.
• [7] Leu, S. S., & Yang, C. H. 1999. A genetic-algorithm-based resource-constrained construction scheduling system. Construction Management & Economics, 17(6), 767-776.
• [8] Deb, K. 2001. Multi-objective optimization using evolutionary algorithms (Vol. 16). John Wiley & Sons.
• [9] Goldberg, D.E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, USA.
• [10] Figlali, A. and Engin, O. 2002. Reproduction method optimization in flow-type scheduling with Genetic Algorithms, Journal of ITU/d, Turkey, 1 (1): 1-7.
• [11] Chang, C. K., Christensen, M. J., Zhang, T. 2001. Genetic algorithms for project management, Annals of Software Engineering, 11: 107-139.
• [12] Kim, K. W., Yun, Y. S., Yoon, J. M., Gen, M., Yamazaki, G. 2005. Hybrid genetic algorithm with adaptive abilities for resource-constrained multiple project scheduling, Computers in Industry, 56: 143-160.
• [13] Ozturk, A. 1984. Operations Research, Uludag University Press.
• [14] Trietsch, D., Mazmanyan, L., Gevorgyan, L. Baker, K.R. 2012. Modeling activity times by the Parkinson distribution with a lognormal core: theory and validation, European Journal of Operational Research, 216, 386–396.
• [15] PROTEC. 2012. Project Management-PERT, http://www.protec.com.tr/index.php?option=com_content&view=article&id=135&Itemid=67&lang=tr.
• [16] Leu, S.S., Chen, A. T., Yang, C.H. 2001. A GA-based fuzzy optimal model for construction time-cost trade-off, Int. J. Proj. Manage. 19, 47–58.
• [17] Azaron, A., Perkgoz, C. and Sakawa, M. 2005. A genetic algorithm approach for the time-cost trade-off in PERT networks, Applied Mathematics and Computation, Vol. 168, 1317–1339.
• [18] Baradaran, S., Fatemi Ghomi, S. M., Mobini, T. M., Hashemin, S. S. 2010. A hybrid scatter search approach for resource-constrained project scheduling problem in PERT-type networks", Advances in Engineering Software, 41(7-8): 966-975.
• [19] Yakhchali, S. H. 2012. A path enumeration approach for the analysis of critical activities in fuzzy networks, Elsevier, Information Sciences, V:204, 30 October, 23-35.
• [20] Pan, N. H., Hsaio, P. W., Chen, K.Y. 2008. A study of project scheduling optimization using Tabu Search algorithm, Eng. Appl. of AI, 21(7): 1101-1112.
• [21] Abbasi, G. Y. and Mukattash, A.M. 2001. Crashing PERT networks using mathematical programming, International Journal of Project Management, 19: 181-188.
• [22] Haixiang, G., Kejun, Z., Chang, D., Lanlan, L. 2001. Intelligent optimization for project scheduling of the first mining face in coal mining, Expert Systems with Application, 37(2): 1294-1301.
• [23] Hashemin, S. S., Fatemi Ghomi, S. M. T. Modarres M. 2012. Optimal constrained non-renewable resource allocation in PERT networks with discrete activity times, Sciencedirect, Scientia Iranica, Available Online 30 April 2012.
• [24] Ke, H. and Liu, B. 2005. Project scheduling problem with mixed uncertainty of randomness and fuzziness, Proceedings of the First International Conference on Complex Medical Engineering, Takamatsu, Japan, May 15-18, 957-962.
• [25] Baar, T., Brucker, P., Knust, S. 1997. Tabu-search algorithms for the resource constrained project scheduling problem, Technical Report, Osnabrück.
• [26] Demirel, N., Gokcen, H., Akcayol M. A. and Demirel, E. 2011. A Hybrid Genetic Algorithm for Multistage Integrated Logistics Network Optimization Problem, Journal of the Faculty Engineering and Architecture of Gazi University, Vol 26, No 4, 929-936, 2011.
• [27] Satheesh Kumar, N. and Raj Kumar, R. 2014. Study On Application Of Genetic Algorithm In Construction Resource Levelling, IJIRSET Volume 3, Special Issue 2, April 2014.
• [28] Rajeevan, M., Nagavinothini, R. 2015. Time Optimization for Resource-Constrained Project Scheduling Using Meta-heuristic Approach, IJSETR Vol. 4 March 2015.
• [29] Hussain, W., Trivedi, M. K., Kansal R. 2015. Optimization of Construction Resource Allocation and Levelling Using Genetic Algorithm, IJIRSET Vol. 4, Issue.
• [30] Chitra, K., & Halder, P. 2017. Scheduling Project Crashing Time Using Linear Programming Approach: Case Study. International Journal of Research in Industrial Engineering, 6(4), 283-292.
• [31] Darwin, C. 2015. (Trans.). The Origin of Species, İstanbul, Universal Publishing.
• [32] Goldberg, D. E. 2016. Genetic Algorithms, Pearson Education India.
• [33] Karaboga, D. 2014. Artificial Intelligence Optimization Algorithms, Ankara, Nobel Academic Publishing.
• [34] Kramer, O. 2017. Genetic Algorithm Essentials, Springer.
• [35] Haznedar, B., Arslan, M. T., & Kalınlı, A. 2017. Training ANFIS structure using genetic algorithm for liver cancer classification based on microarray gene expression data, Sakarya University Journal of Science, 21(1), 54-62.
• [36] Köse, U. 2017. Development of Artificial Intelligence Based Optimization Algorithms, Ph.D Thesis, The Graduate School Of Natural and Applied Science of SELCUK University, Computer Engineering ABD.
• [37] John, A. K., & Krishnakumar, K. 2017. Performing multiobjective optimization on perforated plate matrix heat exchanger surfaces using genetic algorithm. International Journal for Simulation and Multidisciplinary Design Optimization, 8, A3.
• [38] Dener, M., Calp, M. H. 2018. Solving the exam scheduling problems in central exams with genetic algorithms, Mugla Journal of Science and Technology, 4(1), 102-115.