In project scheduling, it is feasible to reduce the duration of a project by allocating additional resources to its activities. However, crashing the project schedule will impose additional costs. Numerous research has focused on optimizing the trade-off between time and cost to achieve a set of non-dominated solutions. However, the majority of the research on time-cost trade-off problem developed methods for relatively simple problems including up to eighteen activities, which are not representing the complexity of real-life construction projects. In this work a Particle Swarm Optimization (PSO) technique is presented for Pareto oriented optimization of the complex discrete time-cost trade-off problems. The proposed PSO engages novel principles for representation and position-updating of the particles. The performance of the PSO is compared to the existing methods using a well-known 18-activity benchmark problem. A 63-activity problem is also included in computational experiments to validate the efficiency and effectiveness of the proposed PSO for a more complex problem. The results indicate that the proposed method provides a powerful alternative for the Pareto front optimization of DTCTPs
