3 citations · 5 across the 4 of their papers we have counts for
4 papers
Self-Evaluation for Job-Shop Scheduling
Imanol Echeverria, Maialen Murua, Roberto Santana
Combinatorial optimization problems, such as scheduling and route planning, are crucial in various industries but are computationally intractable due to their NP-hard nature. Neura…
Offline reinforcement learning for job-shop scheduling problems
Imanol Echeverria, Maialen Murua, Roberto Santana
Recent advances in deep learning have shown significant potential for solving combinatorial optimization problems in real-time. Unlike traditional methods, deep learning can genera…
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem
Imanol Echeverria, Maialen Murua, Roberto Santana
Recent advancements in the flexible job-shop scheduling problem (FJSSP) are primarily based on deep reinforcement learning (DRL) due to its ability to generate high-quality, real-t…
Solving the flexible job-shop scheduling problem through an enhanced deep reinforcement learning approach
Imanol Echeverria, Maialen Murua, Roberto Santana
In scheduling problems common in the industry and various real-world scenarios, responding in real-time to disruptive events is essential. Recent methods propose the use of deep re…