Performing complex project crashing analysis with aid of particle swarm optimization algorithm

The project crashing analysis is to minimize the required cost while meeting a specified deadline. In practice, this problem may be of great complexity because industrial professionals often encounter nonlinear, discrete, or even piecewise discontinuous activity time–cost functions. To handle such complexity and provide enough flexibility, the present study develops and tests a particle swarm optimization (PSO) algorithm to aid in the project crashing analysis. The optimization results include direct and project cost curves, both of which can assist project managers in performing what-if analyses on the project deadline and budget. The application of the proposed algorithm is demonstrated through an example project and a real-life case. The results illustrate the promising performance of the proposed algorithm. Insights are also given to the influence of algorithm parameters.