Development of Fuzzy Enabled Shuffled Frog Leaping Algorithm with Activity Splitting Allowed in Construction Project Scheduling

Evolutionary Algorithms (EAs) as appropriate tools to optimize multi-objective Time-Cost-Resource problems have been applied to optimize construction projects in the last two decades. However, studies on improving the convergence ratio and processing time in the most applied algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in construction engineering and management domains remain poorly understood. We apply our fuzzy enabled Shuffled Frog Leaping Algorithm (SFLA) approach to solve optimization problem, which is found in the construction project planning literature. Our proposed approach uses splitting allowed during execution of activities as a significant reality of actual construction projects in this hybrid algorithm. It is shown that our proposed fuzzy enabled SFLA approach with activity splitting allowed is superior than existing optimization algorithms in finding better project schedule solutions with less total project cost, less total project duration, and less total variations of resource allocation.