A comprehensive simulation study on optimal scheduling for software projects
Frank Padberg · 2004
Software projects often suffer from unexpected rework and delays. Therefore, project scheduling remains a difficult task for the managers. We compute optimal scheduling strategies for a set of small sample projects. The computations are based on a stochastic Markov decision model for software projects which focuses on capturing the feedback between concurrent development activities. The sample projects differ in certain characteristics of the project or product, such as the strength of the coupling between the components or the degree of specialization of the teams on the tasks. We use extensive simulations to compare the performance of the optimal policy against the list policies for any given setting. For our sample projects, we find that the best list policy in general, is not optimal. The performance gap is the larger the higher the degree of specialization of the teams is. On the other hand, the stronger the coupling between the components, the smaller is the improvement which the optimal policy achieves over the best list policy.