Learning-Enhanced Differential Evolution for Multi-Mode Resource-Constrained Multi-Project Scheduling Problem in Industrial Prefabrication

Efficient scheduling in industrial prefabrication environments—such as Prefabricated Bathroom Unit (PBU) production—faces increasing challenges due to resource limitations, overlapping projects, and complex task dependencies. To address these challenges, this paper presents a Learning-Enhanced Differential Evolution (LEDE) framework for solving the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP). The MRCMPSP models the operational difficulty of coordinating interdependent activities across multiple PBU projects under limited resource availability. To address the computational intractability of this NP-hard problem, we first formulate a mixed-integer linear programming (MILP) model, and then develop an adaptive DE-based metaheuristic. The proposed LEDE method co-evolves activity sequencing and mode assignment using floating-point encodings, incorporating strategy switching, parameter adaptation, elitism, stagnation handling, and rank-based crossover control. Evaluated on real-world production data from the PBU industry, the algorithm produces high-quality solutions with strong scalability. These results demonstrate its practical potential as a decision-support tool for dynamic, resource-constrained industrial scheduling.