Grouping-Driven Production Scheduling Optimization for Precast Concrete Components

Precast component production is an important part of industrialization in construction. However, in actual production, it often faces problems such as high resource occupation, long production cycles, and high delivery costs caused by unreasonable scheduling arrangements. This study proposes a hybrid multi-objective optimization framework that combines Q-learning with the non-dominated sorting genetic algorithm, termed QL-NSGA-II, to address the highly coupled problem of component grouping on pallets and production scheduling in precast component manufacturing. The model aims to collaboratively minimize the number of pallets used, the makespan, and the penalty cost. The algorithm constructs a dual Q-table Markov decision architecture, in which the agent adaptively coordinates PC component grouping and scheduling strategies based on reward feedback from the evolutionary state. Through extreme-value tests on multiple benchmark instances from the literature, the results show that the proposed QL-NSGA-II not only achieves the accuracy of the benchmark solutions, but also, in some cases, reduces the makespan and penalty cost by 34.77% and 60.24%, respectively, compared with existing benchmark algorithms. In a real industrial order experiment with 87 components, the algorithm notably outperforms the comparison algorithms across comprehensive convergence metrics such as hypervolume and inverted generational distance, confirming its favorable practical application potential for balancing pallet resource consumption and flow cycle.