Modeling and multi-objective optimization of time, greenhouse gas emissions, and resources for sustainable construction projects

Traditionally, project scheduling and environmental impact assessment are carried out separately which is illsuited for modern organizations as creating a project plan that optimizes for environmental goals can have an adverse effect on project time and resource utilization efficiency. This study aims to address this issue by considering project time, greenhouse gas emissions, and resource-leveling objectives simultaneously. Multiobjective Grey Wolf Optimization algorithm has been modified by incorporating genetic operators and a local search heuristic and its performance was compared with the elitist non-dominated sorting genetic algorithm II on six assessment metrics. The computational experiments exhibited that the modified multi-objective Grey Wolf Optimization algorithm outdid the non-dominated sorting genetic algorithm on five out of six assessment metrics. Two case studies have been used to demonstrate the applicability and validity of the presented model. In the obtained Pareto front solutions for the real-life case study, the range of time, greenhouse gas emissions, and resource deviation have been 594-835 (days), 19,870,953.1-20,938,469.6 (kgCO2-e) and 67,204.93-141,915.55 (unitless) respectively. The examination of tradeoffs between objectives showed that greenhouse gas emissions can be decreased if the makespan and resource deviation are allowed a slight increase at various decision points. The presented model can help practitioners and researchers make practical tradeoffs between the conflicting goals of duration, greenhouse gas emissions, and resource optimization.