Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Optimization of Resource-Constrained Project Using Genetic Algorithm
Construction projects face various challenges that can lead to cost overruns and delays. The timely completion of a project is critical to avoid such circumstances. To address these challenges, contractors adopt a time–cost trade-off approach, which involves increasing resources to speed up the project, leading to an increase in direct costs. This paper proposes a genetic algorithm (GA) model that optimizes the crashing time of the activities while considering resource constraints and minimizing the increase in indirect costs. The GA model suggests three resource allocation alternatives for each activity, resulting in optimized project duration and cost. The proposed GA model is applied to a hypothetical construction project consisting of 19 activities and a duration of 73 days. The first resource allocation alternative is operating under normal circumstances (1 shift), the second is to crash the time by increasing the resources (1 shift), while the third is to operate under 2 shifts. The results show that the proposed GA model effectively optimizes the time–cost trade-off of the construction project by finding the most optimum resource allocation alternative for each activity. The proposed GA model provides a practical tool for contractors to optimize the time–cost trade-off of resource-constrained construction projects. It enables contractors to evaluate multiple resource allocation alternatives and determine the most optimum cost and time for the project. The results of this study can be used to improve the efficiency of construction project management and reduce the risk of cost overruns and delays.
Optimization of Resource-Constrained Project Using Genetic Algorithm
Construction projects face various challenges that can lead to cost overruns and delays. The timely completion of a project is critical to avoid such circumstances. To address these challenges, contractors adopt a time–cost trade-off approach, which involves increasing resources to speed up the project, leading to an increase in direct costs. This paper proposes a genetic algorithm (GA) model that optimizes the crashing time of the activities while considering resource constraints and minimizing the increase in indirect costs. The GA model suggests three resource allocation alternatives for each activity, resulting in optimized project duration and cost. The proposed GA model is applied to a hypothetical construction project consisting of 19 activities and a duration of 73 days. The first resource allocation alternative is operating under normal circumstances (1 shift), the second is to crash the time by increasing the resources (1 shift), while the third is to operate under 2 shifts. The results show that the proposed GA model effectively optimizes the time–cost trade-off of the construction project by finding the most optimum resource allocation alternative for each activity. The proposed GA model provides a practical tool for contractors to optimize the time–cost trade-off of resource-constrained construction projects. It enables contractors to evaluate multiple resource allocation alternatives and determine the most optimum cost and time for the project. The results of this study can be used to improve the efficiency of construction project management and reduce the risk of cost overruns and delays.
Optimization of Resource-Constrained Project Using Genetic Algorithm
Lecture Notes in Civil Engineering
Desjardins, Serge (Herausgeber:in) / Poitras, Gérard J. (Herausgeber:in) / Nik-Bakht, Mazdak (Herausgeber:in) / Ahmed, Sweilam (Autor:in) / Berlant, Arab (Autor:in) / Omar, Sawan (Autor:in) / Yasmeen, Essawy (Autor:in) / Osama, Hosny (Autor:in)
Canadian Society of Civil Engineering Annual Conference ; 2023 ; Moncton, NB, Canada
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2023, Volume 3 ; Kapitel: 34 ; 481-492
16.10.2024
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
| British Library Online Contents | 2013
| British Library Online Contents | 2008