13 citations · 36 across the 27 of their papers we have counts for
7 papers · 1 filter
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
Igor G. Smit, Yaoxin Wu, Pavel Troubil +2
Neural combinatorial optimization (NCO) has gained significant attention due to the potential of deep learning to efficiently solve combinatorial optimization problems. NCO has bee…
Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang
The Job Shop Scheduling Problem (JSSP) is a complex combinatorial optimization problem. While online Reinforcement Learning (RL) has shown promise by quickly finding acceptable sol…
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization
Xia Jiang, Yaoxin Wu, Yuan Wang +1
To advance capabilities of large language models (LLMs) in solving combinatorial optimization problems (COPs), this paper presents the Language-based Neural COP Solver (LNCS), a no…
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
Igor G. Smit, Jianan Zhou, Robbert Reijnen +6
Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the applicat…
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization
Jesse van Remmerden, Maurice Kenter, Diederik M. Roijers +3
In this paper, we introduce Multi-Objective Deep Centralized Multi-Agent Actor-Critic (MO- DCMAC), a multi-objective reinforcement learning (MORL) method for infrastructural mainte…
Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking
Igor G. Smit, Zaharah Bukhsh, Mykola Pechenizkiy +3
In collaborative human-robot order picking systems, human pickers and Autonomous Mobile Robots (AMRs) travel independently through a warehouse and meet at pick locations where pick…