Improved ant colony optimization algorithms for determining project critical paths

Abstract

In project management, a project can be represented as a network in two ways; namely, activity-on-arc (AoA) and activity-on-node (AoN). Two recent papers have shown that ant colony optimization (ACO) could find critical path(s) in projects represented as AoA networks. This paper points out that the number and placement of logical dummy activities associated with AoA-based networks can pose serious problems. To get around the problems, an ACO technique based on AoN networks is then proposed. For comparison, the two existing AoA-based ACO algorithms were reproduced and modified into AoN-based algorithms. Moreover, the proposed ACO algorithm was applied to AoA networks as well. All six algorithms were tested with several benchmark problems. The test results strongly indicate that AoN-based ACO algorithms are more effective and efficient in finding critical paths than AoA-based algorithms.