AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A novel DRL-based approach that integrates bidirectional scheduling with graph-theoretic features to effectively solve JSSP is introduced, which achieves superior performance compared to traditional heuristics and classic DRL methods, while maintaining competitive results against recent state-of-the-art approaches.
This study proposes a structurally simple, low-node-density disjunctive graph model, then performs feature extraction using Graph Neural Networks (GNNs), and finally optimize convergence using the Migrating Birds Optimization—an intelligent optimization algorithm proven effective for scheduling decision problems.
An adaptive proximal policy optimization algorithm is proposed that enhances the representation capability of the decision-making network by introducing a learnable adaptive gating mechanism and improves the stability and feasibility of solutions by incorporating an ε-greedy strategy and a masking mechanism.
The Heterogeneous Graph Transformer (HGT)‐Scheduler is proposed, a reinforcement learning framework that explicitly models the JSSP as a heterogeneous graph, demonstrating that explicitly modeling edge semantics improves reinforcement learning for intelligent job shop scheduling.
Experimental results indicate that the proposed HGNN-DRL framework is effective for fast and intelligent scheduling decision-making in FJSP-AGV environments.
PGMPO is proposed, a novel learning framework consisting of a simple but effective multi-policy modeling approach that allows a single network to represent multiple decision-makers, and a preference-driven model optimization method that effectively guides policies to learn diverse and specialized problem-solving strate...
This work addresses the resulting Multi-Objective Flexible Job Shop Scheduling Problem by proposing a deep reinforcement learning framework that jointly minimizes makespan and energy cost, and evaluates the approach against NSGA-II and Joined Heuristics on synthetic instances.
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