Dergiler / Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2018 / Cilt: 22 - Sayı: 2

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

Sayfa
615–627
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It is expected that this study will contribute to the performance field (time, speed, low error etc.) of other studies.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Projects consist of interconnected dimensions such as objective, time, resource and environment.
📊
Temel Bulgular & Veri:Use of these dimensions in a controlled way and their effective scheduling brings the project success.
💡
Sonuç & Katkı:It is expected that this study will contribute to the performance field (time, speed, low error etc.) of other studies.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Project management · CPM · PERT

Özet

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.

🔖

Bu Makaleye Atıf Yap

CALP, M., AKCAYOL, M. A. (2018). Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 22 - Sayı: 2, 615–627. https://ajindex.com/articles/310519

Kaynakça (16)

  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,
  16. Leu, S.S., Chen, A. T., Yang, C.H. 2001. A GA-based fuzzy optimal model for co