Genetic Programming with Activity Group Selection for Dynamic Multi-mode Resource-Constrained Project Scheduling Problems

Efficient scheduling under uncertain durations and constrained resources remains a core challenge in the dynamic multi-mode resource-constrained project scheduling problem. Genetic programming (GP) has proven effective for evolving scheduling rules; however, conventional strategies often assess activities separately, disregarding potential synergies among modes of concurrently executable tasks. To overcome this limitation, we develop a group-based strategy that selects from feasible combinations of activity-mode tuples. New terminals are designed to capture the attributes of these combinations and embed them into a GP framework. Experimental evaluation across multiple scenarios demonstrates that our group-based GP approach outperforms conventional GP methods based on individual activity prioritisation.