AIConfPaper
← Search

Igor G. Smit

2 accepted papers

2026

Neural Multi-Objective Combinatorial Optimization for Flexible Job Shop Scheduling Problems

Igor G. Smit, Yaoxin Wu, Pavel Troubil, Yingqian Zhang, Wim P.M. Nuijten
ICLR 2026poster

Neural combinatorial optimization (NCO) has made significant advances in applying deep learning techniques to efficiently and effectively solve single-objective flexible job shop scheduling problems (FJSPs). However, the more practical multi-objective FJSPs (MOFJSPs) remain underexplored, limiting t…

Cited by 0Source ↗Scholar ↗
2025

Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems

Igor G. Smit, Yaoxin Wu, Pavel Troubil, Yingqian Zhang, Wim P.M. Nuijten
AAAI 2025technical

Neural combinatorial optimization (NCO) has gained significant attention due to the potential of deep learning to efficiently solve combinatorial optimization problems. NCO has been widely applied to job shop scheduling problems (JSPs) with the current focus predominantly on deterministic problems.…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗