71 citations · 76 across the 4 of their papers we have counts for
4 papers
Algorithms for slate bandits with non-separable reward functions
Jason Rhuggenaath, Alp Akcay, Yingqian Zhang +1
In this paper, we study a slate bandit problem where the function that determines the slate-level reward is non-separable: the optimal value of the function cannot be determined by…
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…
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…