Ant colony optimization algorithm for stochastic project crashing problem in PERT networks using MC simulation

This paper describes a new approach based on ant colony optimization (ACO) metaheuristic and Monte Carlo (MC) simulation technique, for project crashing problem (PCP) under uncertainties. To our knowledge, this is the first application of ACO technique for the stochastic project crashing problem (SPCP), in the published literature. A confidence-level-based approach has been proposed for SPCP in program evaluation and review technique (PERT) type networks, where activities are subjected to discrete cost functions and assumed to be exponentially distributed. The objective of the proposed model is to optimally improve the project completion probability in a prespecified due date based on a predefined probability. In order to solve the constructed model, we apply the ACO algorithm and path criticality index, together. The proposed approach applies the path criticality concept in order to select the most critical path by using MC simulation technique. Then, the developed ACO is used to solve a nonlinear integer mathematical programming for selected path. In order to demonstrate the model effectiveness, a large scale illustrative example has been presented and several computational experiments are conducted to determine the appropriate levels of ACO parameters, which lead to the accurate results with reasonable computational time. Finally, a comparative study has been conducted to validate the ACO approach, using several randomly generated problems.