Data-Driven Optimization of Construction Productivity in Commercial Buildings using Genetic Algorithm

This study focuses on the optimization of time, cost, and resource utilization in commercial building construction using a Genetic Algorithm approach. The increasing complexity of modern construction projects necessitates the adoption of advanced techniques beyond traditional scheduling methods. Conventional methods such as CPM and PERT are limited in handling multi-objective optimization
problems involving conflicting project parameters. This research aims to develop an efficient optimization framework that integrates time, cost, and resource constraints. A etailed literature review was conducted to identify research gaps and establish the theoretical foundation of the study. A structured methodology was adopted, including data collection, problem formulation, and model development. Project data such as activity durations, costs, and resource requirements were analyzed