Incorporating Practicability into Genetic Algorithm-Based Time-Cost Optimization

Optimization problems in construction scheduling, such as time-cost optimization, can be effectively solved using genetic algorithms (GAs). This paper presents an approach that makes GA-based time-cost optimization viable for real world problems. Practicability is incorporated through the integration of a project management system to the GA system. The approach takes advantage of the powerful scheduling functionality of the project management system in evaluating project completion dates during optimization. The approach ensures that all scheduling parameters, including activity relationships, lags, calendars, constraints, resources, and progress, are considered in determining the project completion date, thus allowing comprehensive and realistic evaluations to be made during optimization.