71 citations · 249 across the 11 of their papers we have counts for
13 papers
Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
Robbert Reijnen, Igor G. Smit, Hongxiang Zhang +3
Job shop scheduling problems address the routing and sequencing of tasks in a job shop setting. Despite significant interest from operations research and machine learning communiti…
Revisit the Algorithm Selection Problem for TSP with Spatial Information Enhanced Graph Neural Networks
Ya Song, Laurens Bliek, Yingqian Zhang
Algorithm selection is a well-known problem where researchers investigate how to construct useful features representing the problem instances and then apply feature-based machine l…
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
Robbert Reijnen, Yingqian Zhang, Hoong Chuin Lau +1
The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable success in solving combinatorial optimization problems (COPs). Nonetheless, the performance of ALNS r…
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…
Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning
Bram Cals, Yingqian Zhang, Remco Dijkman +1
In e-commerce markets, on time delivery is of great importance to customer satisfaction. In this paper, we present a Deep Reinforcement Learning (DRL) approach for deciding how and…
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning
Reza Refaei Afshar, Yingqian Zhang, Murat Firat +1
This paper proposes a Deep Reinforcement Learning (DRL) approach for solving knapsack problem. The proposed method consists of a state aggregation step based on tabular reinforceme…
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…
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…
Column generation based math-heuristic for classification trees
Murat Firat, Guillaume Crognier, Adriana F. Gabor +2
This paper explores the use of Column Generation (CG) techniques in constructing univariate binary decision trees for classification tasks. We propose a novel Integer Linear Progra…
Fair task allocation in transportation
Qing Chuan Ye, Yingqian Zhang, Rommert Dekker
Task allocation problems have traditionally focused on cost optimization. However, more and more attention is being given to cases in which cost should not always be the sole or ma…
Learning optimization models in the presence of unknown relations
Sicco Verwer, Yingqian Zhang, Qing Chuan Ye
In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that…