Fuzzy Enabled Hybrid Genetic Algorithm–Particle Swarm Optimization Approach to Solve TCRO Problems in Construction Project Planning

Abstract

One of the most challenging tasks of a construction project planner is to simultaneously minimize the total project cost and total project duration while considering issues related to optimal resource allocation and resource leveling. Therefore, project planners face complicated multivariate, time-cost-resource optimization (TCRO) problems that require time-cost-resource tradeoff analysis. The hybrid GA–PSO approach is presented to solve complex, TCRO problems in construction project planning. The proposed approach uses the fuzzy set theory to characterize uncertainty about the input data (i.e., time, cost, and resources required to perform an activity) in this hybrid approach. The proposed fuzzy-enabled hybrid GA–PSO approach is applied to solve two optimization problems that are found in the construction project planning literature. It is shown that the proposed fuzzy enabled hybrid GA–PSO approach is superior to existing optimization algorithms at finding better project schedule solutions with less total project cost, less total project duration, and less total variation in resource allocation. The results also show that the proposed approach is faster than existing methods in processing time for solving complex TCRO problems in construction project planning.