Improved genetic algorithm based on time windows decomposition for solving resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) is one of the project scheduling problems which are widely used in construction and many industrial disciplines. The challenge of the problem is to design some appropriate search mechanism for finding solutions in feasible space. An improved genetic algorithm based on time window decomposition is proposed in this paper. Three derivation methods are applied to increase population diversity. The sampling count allocation strategy and the use of destructive lower bounds improve the search efficiency. The computational experiments on PSPLIB show that the proposed approach is more effective than that only using the decomposition mechanism and is competitive in solving two real-life cases. This research illustrates that continuously changing the search subspaces has potential advantages, which may be useful for studying RCPSP using other evolutionary algorithms in future. Some other better results may be obtained by using machine learning methods to flexibly determine the sampling times for each individual.

Automated summary

In this paper, an improved genetic algorithm based on time window decomposition is proposed to solve the resource-constrained project scheduling problem (RCPSP). The experimental results show that the proposed approach is competitive in solving real-life cases and provides useful insights for future research on RCPSP using other evolutionary algorithms.