Efficient network defense policies via GNN-enhanced reinforcement learning DOI
Shoukun Xu, Yongyong Shi, Lin Shi

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(8)

Published: June 5, 2025

Language: Английский

Addressing Due Date and Storage Restrictions in the S-Graph Scheduling Framework DOI Creative Commons
Krisztián Attila Bakon, Tibor Holczinger

Machines, Journal Year: 2025, Volume and Issue: 13(2), P. 131 - 131

Published: Feb. 9, 2025

This paper addresses the Flexible Job Shop Scheduling Problem (FJSP) with objective of minimizing both earliness/tardiness (E/T) and intermediate storage time (IST). An extended S-graph framework that incorporates E/T IST minimization while maintaining structural advantages original approach is presented. The further enhanced by integrating linear programming (LP) techniques to adjust machine assignments operation timings dynamically. following four methodological approaches are systematically analyzed: a standalone for minimization, an combined hybrid LP comprehensive addressing IST. Computational experiments on benchmark problems demonstrate efficacy proposed methods, showing efficiency smaller instances offering improved solution quality more complex scenarios. research provides insights into trade-offs between computational across different problem configurations policies. work contributes field production scheduling versatile capable multi-objective nature modern manufacturing environments.

Language: Английский

Citations

0

Efficient network defense policies via GNN-enhanced reinforcement learning DOI
Shoukun Xu, Yongyong Shi, Lin Shi

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(8)

Published: June 5, 2025

Language: Английский

Citations

0