A priority-based genetic algorithm approach for solving multiple alternative project scheduling problems with resource constraints and variable activity times

In resource constrained multiple project environments, it is expected that multiple projects under a single scheduling umbrella will deliver benefit which is not achievable if the projects were scheduled independently. However, most of the time, there often exists alternative ways for performing each project. This type of problem is called resource constrained multiple project scheduling problem with alternative projects (rc-mPSP/aP). Additionally, in real-world, the duration of activates in a project are subject to change during the scheduling period due to the changes in environment. In this research, a genetic algorithm approach is constructed in order to efficiently solve the rc-mPSP/aP with variable activity times. The proposed genetic algorithm approach is specifically constructed to reflect the alternative project selection and the multiple project scheduling problems together in the exclusive problem.