Technical gazette, Vol. 28 No. 4, 2021.
Preliminary communication
https://doi.org/10.17559/TV-20200305101811
Scheduling of Construction Projects under Resource-Constrained Conditions with a Specifically Developed Software using Genetic Algorithms
Mursel Erdal*
orcid.org/0000-0002-9338-6162
; Gazi University, Faculty of Technology, Department of Civil Engineering, 06500, Ankara, Turkey
Recep Kanit
orcid.org/0000-0002-2063-345X
; Gazi University, Faculty of Technology, Department of Civil Engineering, 06500, Ankara, Turkey
Abstract
The purpose of this study is to develop a genetic algorithm (GA) based software that can perform resource allocation close to optimum and that can determine the critical path by minimizing the project duration according to the resource profile for a present work schedule and resource pool using a programmable objective function. In this context, the methodology of GAs was presented, the software was developed and the performance of this software was tested with a sample project. With the developed software, by minimizing the activity durations in both constrained and unconstrained resource conditions, projects can be scheduled, total duration and the critical path of the projects can be determined. With this software, any construction company will be able to determine how much time would be required to complete a project at the bidding stage by considering its resources and constraints and can take the required precautions. The main difference of this present study is that the developed code performs minimization of schedule duration integrated with resource allocation and levelling. It also determines the critical path of the final solutions. Both renewable and non-renewable resources are included in the code which is not often considered in the literature. By minimizing project duration and optimizing resource allocation, construction projects can become more sustainable, and the environmental impact of the construction process could be minimized.
Keywords
genetic algorithms; optimization; renewable resource; scheduling; sustainability
Hrčak ID:
260861
URI
Publication date:
22.7.2021.
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