71 citations · 75 across the 4 of their papers we have counts for
5 papers
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Laurens Bliek, Paulo da Costa, Reza Refaei Afshar +19
This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCA…
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
Paulo R. de O. da Costa, Jason Rhuggenaath, Yingqian Zhang +1
Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality…
Machine Learning based Simulation Optimisation for Trailer Management
Dylan Rijnen, Jason Rhuggenaath, Paulo R. de O. da Costa +1
In many situations, simulation models are developed to handle complex real-world business optimisation problems. For example, a discrete-event simulation model is used to simulate…
Remaining Useful Lifetime Prediction via Deep Domain Adaptation
Paulo R. de O. da Costa, Alp Akcay, Yingqian Zhang +1
In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven…
Data-driven Policy on Feasibility Determination for the Train Shunting Problem
Paulo R. de O. da Costa, J. Rhuggenaath, Y. Zhang +3
Parking, matching, scheduling, and routing are common problems in train maintenance. In particular, train units are commonly maintained and cleaned at dedicated shunting yards. The…