Heuristic Embedded Genetic Algorithm for Heterogeneous Project Scheduling Problems

Over the last few decades, many solution approaches have been developed for solving different variants of resource-constrained project scheduling problems (RCPSPs). In most of them, it is assumed that a project consists of some homogeneous activities that require all types of resources over the entire project horizon. On the contrary, many real-world projects consist of heterogeneous activities that use different types of resources at different time instants during the project execution. The application of existing approaches, developed for RCPSPs with homogeneous activities, in solving RCPSPs with heterogeneous activities is computationally expensive. In this paper, we propose a heuristic embedded genetic algorithm to address RCPSPs with heterogeneous activities. Two heuristics are proposed to obtain high-quality feasible solutions. The first heuristic is based on priority rules while the second one based on a new neighbourhood swapping matrix. To evaluate the performance of the proposed algorithm, we solve a number of real-world and modified test problems, and the obtained results are compared with an existing algorithm. It is found that the proposed approach obtains high-quality solutions with a significantly lower computational time compared to other algorithms.