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

TL;DR

This paper introduces a genetic algorithm-based method for optimizing project scheduling in dynamic CPM and PERT networks, aiming to improve efficiency and decision-making over traditional techniques.

Contribution

It develops a novel genetic algorithm approach to determine critical paths and project duration, surpassing CPM and PERT in speed and accuracy for complex network analysis.

Findings

GA provides faster solutions than CPM and PERT.

The method accurately identifies critical activities and project duration.

Results demonstrate improved performance in project scheduling efficiency.

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.