الفهرس 
I. IntroductionOnly 14 pages are availabe for public view
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Abstract
In recent years, due to the increase in complexity of scheduling problems research in the field of Multi-objective Scheduling Optimization (MOSP) have increased significantly. Multi-Objective Evolutionary Algorithms (MOEAs) have drawn attention towards its effectiveness and efficiency in solving complex scheduling problems involving two or three objectives with the use of its population-based evolution search strategies involving exploration and exploitation in general. Genetic Algorithms (GA) in particular have been utilized in most of the recent construction scheduling optimization problems due to its production of near optimal Pareto solutions in a fair period of time for almost all objectives. However, the performance of such algorithms is compromised when more than three objectives are being optimized. The problem becomes even more challenging when these objectives have conflicting characteristics which oblige performing trade-offs to the set of optimal solutions in order to get Pareto front where most individuals become non-dominated with respect to each other’s. Multi-criteria decision making (MCDM) is another aspect where methods of deciding the optimal solution from the set of Pareto-optimal solutions is crucial. Moreover, several recent studies have developed approaches for integrating Building Information Modeling (BIM) with Multi-Objective Optimization Problems (MOOP) to gain benefits in different aspects of the construction process including management of construction.
This research provides insights on the most used Multi-objective Evolutionary Algorithms (MOEAs) with a special focus on Genetic Algorithms (GAs) methods and techniques developed to solve the scheduling problems with three or more objectives through a structured literature review. Also, in order to assess the potential of developing models that combine information systems and optimization methods for better decision-making process a review on the use of active BIM-based optimization models and applications demonstrating this integration is provided. This research aims to develop a BIM based multi-objective scheduling model based on a non-dominated sorting genetic algorithm through utilizing the data available in literature along with BIM tools like Revit + Dynamo and optimization programming tools using Python. Finally, gaps in literature are addressed and based on the finding’s recommendations for future research development in this field are provided.