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

Abstract

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. Özet: Projeler; amaç, zaman, kaynak ve çevre olmak üzere birbirleriyle ilişkili birtakım boyutlardan oluşmaktadır. Bu boyutların kontrollü kullanılması ve etkin planlanması proje başarısını getirmektedir. Proje planlama süreci, proje faaliyetlerinin tanımlanması ve projedeki faaliyetler için zaman ve kaynak tahmini yapılması süreçlerini kapsar. Bu noktada, proje kaynak planlama problemleri, Program Değerlendirme ve Gözden Geçirme Tekniği (PERT) ve Kritik Yol Metodu (CPM) birbiri ardına geliştirildikten sonra daha fazla dikkat çekmiştir. Bununla birlikte, CPM ve PERT işlemlerinin karmaşıklığı ve zorluğu, bu teknikleri Genetik Algoritma (GA) gibi yapay zeka yöntemleri ile kullanmaya itmiştir. Bu çalışmada, proje yönetimi kapsamında şebeke analizi için kullanılan CPM ve PERT tekniklerinin yerine, GA kullanılarak kritik yol, kritik faaliyet ve proje tamamlanma süresini belirleyen bir algoritma önerilmiş ve geliştirilmiştir. GA kullanılmasının amacı, bu algoritmaların karmaşık optimizasyon problemlerin çözümünde etkili bir yöntem olmasıdır. Böylece, elde edilen sonuçlar kullanılarak gerçekleştirilecek proje faaliyetleriyle ilgili doğru kararlar alınabilmektedir. Nitekim, geliştirilen dinamik algoritma ile CPM ve PERT tekniklerinden daha kısa sürede optimum sonuçlara ulaşılmıştır. Çalışmanın diğer çalışmaların performans alanına (zaman, hız, düşük hata vb.) katkı sağlaması beklenmektedir.

Systematic Review of the Time-Cost Optimization Models in Construction Management

“…CPM and PERT are widely used techniques in planning and scheduling large and complex projects [40,41]. They provide graphical representation of the activities that constitute the projects and the associated estimated cost and duration of each activity [42,43].…”

Modelling Building Renovation Using PERT

“…Therefore, to a large extent some level of uncertainty must be incorporated into the planning. PERT method offers this advantage because it is established on probability estimation of operation times and completion duration of the project [12,13,14].…”

Uncertainty Management Associated With Project Scheduling in Mine Construction: A Coal Mining Case Study

“…Martens and Vanhoucke (2017) proposed a different approach to project management in the construction industry based on reacting and correcting unexpected events throughout the project management. Other studies have employed a variety of optimization models to construction scheduling problems such as simulated annealing (Azaron, Sakawa, Tavakkoli-Moghaddam, & Safaei, 2007, König & Beißert, 2009, ant colony optimization (Xiong & Kuang, 2008), particle swarm optimization (Guo, Zhu, Ding, & Li, 2010), neural networks (Adeli & Karim, 1997), and genetic algorithms (Guo, Zhu, Ding, & Li, 2010;Eldin & Senouci, 2004;and Calp & Akcayol, 2018). A comprehensive compilation of methods of scheduling optimization and their respective successes and failures can be further explored in (Zhou, Love, Wang, Teo, & Irani, 2013;Lau & NG, 2013).…”