Eco-Friendly Scheduling Model for Construction Projects Utilizing Genetic Algorithms

Islam Elmasoudi, Emad Elbeltagi, Wael Alattyih () and Hossam Wefki
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Islam Elmasoudi: Department of Structural Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Emad Elbeltagi: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Wael Alattyih: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Hossam Wefki: Department of Civil Engineering, Faculty of Engineering, Port Said University, Port Said 42526, Egypt

Sustainability, 2024, vol. 16, issue 24, 1-19

Abstract: An assessment of construction activities related to pollution needs to be conducted during the planning of a given project. Such an assessment is essential to ensure that the resulting pollution does not surpass the environmental threshold limits. This research provides an optimized pollution-based scheduling model in construction projects by applying Genetic Algorithms (GAs). The suggested approach figures out the pollution produced by gasses, noise, and dust for each activity in the project. Then, the whole project’s duration is minimized by optimizing the project schedule using GAs while keeping the different pollutants under threshold limits. In the developed model, each pollutant is handled as a dummy resource and is incorporated into the schedule of construction projects. When the emitted pollutants surpass the allowable limits, as per the given regulations, GAs re-schedule the project tasks so that the pollution levels are reduced and redistributed. The proposed framework is presented as being practically applicable through an actual case study. The results show that the proposed GA model improves the pollution leveling process more efficiently than the standard resource leveling technique in Microsoft Project, producing fewer pollution histogram moments of the X and Y axes with 9.4% and 2.2%, respectively. Sensitivity analysis reveals that the best solutions for this case study are obtained when population size, offspring generation, crossover rate, and mutation rate equal 100, 50, 0.95, and 0.05, respectively. The model can aid in reducing construction projects’ environmental impact during the project planning and construction stages, which benefits decision-makers and project planners.

Keywords: pollution; re-schedule; genetic algorithms; optimization; sustainability; construction projects (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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