TL;DR
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.
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 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.
A Mixed Integer Nonlinear Programming (MINLP) model with the objective of a weighted sum of long-term average task completion rate, total latency and energy consumption is established, which improves the task completion rate by 4% in high load scenarios and achieves a better balance between latency and energy consumpti...
To address the challenges of multi-service congestion and load imbalance in Low Earth Orbit (LEO) networks, stemming from highly dynamic spatio-temporal characteristics and constrained link capacities, this paper proposes a joint optimization method for routing and load balancing based on Graph Neural Networks (GNN) an...
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system pote...
This study provides a novel datadriven paradigm for the equitable allocation and dynamic flow of resources within the framework of smart education by proposing an End-to-End Balanced Scheduling Model Based on Heterogeneous Graph Attention Encoding and Proximal Policy Decoding.
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