A neural-driven constructive heuristic for the flexible job shop scheduling problem: An efficient alternative to complex deep learning methods
提出一种用紧凑前馈神经网络替代静态优先规则的构造式启发式算法，通过CMA-ES训练网络，在Brandimarte基准上平均最优性差距0.91%，优于深度强化学习（2.35%）和传统启发式（9.25%），且计算成本降低六倍。
The Flexible Job Shop Scheduling Problem (FJSP) is a complex, NP-hard optimization challenge with significant practical relevance in manufacturing systems. This paper introduces a novel neural-driven constructive heuristic that replaces static priority dispatching rules with a compact, feed-forward neural network. The network evaluates potential operation-machine assignments during the schedule construction process based on features derived from the current partial schedule and job state. To train the network, we employ a black-box optimization approach using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), eliminating the need for labeled data or reward functions. We propose a two-stage framework: general training across a broad range of synthetic or benchmark instances, followed by instance-specific fine-tuning of the network weights to adapt the heuristic for individual problem scenarios. On the Brandimarte benchmarks, our approach yields an average optimality gap of 0.91% (0.32% on the full dataset), outperforming the best deep reinforcement learning methods (2.35%) and the leading traditional dispatching heuristics individually suited to each instance of the problem (9.25%). Although CPLEX constrained programming solver achieves a slightly lower average gap of 0.05%, our method delivers competitive makespans overall and demonstrates superiority on numerous instances at six times lower computational cost. The results highlight the potential of learning-based constructive heuristics as a scalable and adaptable alternative for complex scheduling tasks. • Novel neural-driven heuristic for flexible job shop scheduling (FJSP). • A population of compact neural networks trained using CMA-ES. • A two-stage framework combining general training with instance-specific fine-tuning. • The heuristic achieves near-optimal makespans on Brandimarte benchmark tests. • Demonstrates advantages of population-based shallow learning over DRL in FJSP.