Construction of Project Management using Multi-Objective Evolutionary Algorithm with Simulated Binary Crossover

In recent years, Construction Project Management (CPM) is defined as planning, controlling and coordinating construction from inception to completion phase. The CPM ensures projects are delivered in on time, within budget (cost) to meet quality and safety requirements for monitoring project planning with inadequate risk management and decreased profitability. Hence, this research proposes Multi-Objective Evolutionary Algorithm-Simulated Binary Crossover (MOEA-SBC) for minimizing cost, maximizing quality and reduced construction time in small commercial spaces. Initially, Building Information Model (BIM) contains detailed information about building components for planning and scheduling project with enhanced resource allocation. After that, preprocessing step includes data profiling and data validation with Industry Foundation Classes (IFC) filter to remove duplicate data in formatting units with conversion of values from meters to feet against industry standards and regulations. Finally, MOEA-SBC is used for optimization which achieved large commercial spaces for construction projects with enhanced cost efficiency and flexibility in project management. The proposed MOEA-SBC achieved better results such as Mean Value (MV) is (1555.30), Variance (V) is (86.2342) and Standard Deviation (SD) is (8.7452) when compared to existing Nondominated Sorting Genetic Algorithm (NSGA-III).