Cooperative coevolution with an improved resource allocation for large-scale multi-objective software project scheduling

摘要

The existing literature of search-based software project scheduling merely studied to schedule a small to medium-scale project in static scenarios, while little work has considered to schedule a large-scale software project with uncertainties. However, many real-world software projects involve a large number of tasks and employees. Meanwhile, they are confronted with uncertain environments. To tackle such problems, this paper constructs a mathematical model for the large-scale multi-objective software project scheduling problem, and proposes a cooperative coevolutionary multi-objective genetic algorithm to solve the established model. In our model, more practical features of human resources and tasks are captured in the context of large-scale projects than the previous studies. Two efficiency related objectives of duration and cost are considered together with robustness to uncertainties and employees' satisfaction to allocations subject to various realistic constraints. Three novel strategies are incorporated in the proposed algorithm, which include the problem feature-based variable decomposition method, the improved computational resource allocation mechanism and the problem-specific subcomponent optimizer. To evaluate the performance of the proposed algorithm, empirical experiments have been performed on 15 randomly generated large-scale software project scheduling instances with up to 2048 decision variables, and three instances derived from real-world software projects. Experimental results indicate that on most of the 15 random instances and three real-world instances, the proposed algorithm achieves significantly better convergence performance than several state-of-the-art evolutionary algorithms, while maintaining a set of well-distributed solutions. Thus, it can be concluded that the proposed algorithm has a promising scalability to decision variables on software project scheduling problems. We also demonstrate how different compromises among the four objectives can offer software managers a deeper insight into various trade-offs among many objectives, and enabling them to make an informed decision. (C) 2020 Elsevier B.V. All rights reserved.