42 citations · 190 across the 13 of their papers we have counts for
13 papers
Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling
Yuan Sun, Su Nguyen, Dhananjay Thiruvady +3
Constraint programming (CP) is a powerful technique for solving constraint satisfaction and optimization problems. In CP solvers, the variable ordering strategy used to select whic…
Adaptive Population-based Simulated Annealing for Uncertain Resource Constrained Job Scheduling
Dhananjay Thiruvady, Su Nguyen, Yuan Sun +3
Transporting ore from mines to ports is of significant interest in mining supply chains. These operations are commonly associated with growing costs and a lack of resources. Large…
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Jieyi Bi, Yining Ma, Jiahai Wang +4
Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i.e., uniform). To tackle the consequent cross-distribut…
Ranking Constraint Relaxations for Mixed Integer Programs Using a Machine Learning Approach
Jake Weiner, Andreas T. Ernst, Xiaodong Li +1
Solving large-scale Mixed Integer Programs (MIP) can be difficult without advanced algorithms such as decomposition based techniques. Even if a decomposition technique might be app…
Adaptive Solution Prediction for Combinatorial Optimization
Yunzhuang Shen, Yuan Sun, Xiaodong Li +2
This paper aims to predict optimal solutions for combinatorial optimization problems (COPs) via machine learning (ML). To find high-quality solutions efficiently, existing work use…
Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring
Yunzhuang Shen, Yuan Sun, Xiaodong Li +2
Column Generation (CG) is an effective method for solving large-scale optimization problems. CG starts by solving a sub-problem with a subset of columns (i.e., variables) and gradu…
Learning Primal Heuristics for Mixed Integer Programs
Yunzhuang Shen, Yuan Sun, Andrew Eberhard +1
This paper proposes a novel primal heuristic for Mixed Integer Programs, by employing machine learning techniques. Mixed Integer Programming is a general technique for formulating…
Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand
Sheng Wang, Yuan Sun, Christopher Musco +1
In this paper, we make a first attempt to incorporate both commuting demand and transit network connectivity in bus route planning (CT-Bus), and formulate it as a constrained optim…
Instance Space Analysis for the Car Sequencing Problem
Yuan Sun, Samuel Esler, Dhananjay Thiruvady +3
We investigate an important research question for solving the car sequencing problem, that is, which characteristics make an instance hard to solve? To do so, we carry out an insta…
On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection
Sheng Wang, Yuan Sun, Zhifeng Bao
This paper presents a thorough evaluation of the existing methods that accelerate Lloyd's algorithm for fast k-means clustering. To do so, we analyze the pruning mechanisms of exis…
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning
Yuan Sun, Sheng Wang, Yunzhuang Shen +3
This paper introduces an enhanced meta-heuristic (ML-ACO) that combines machine learning (ML) and ant colony optimization (ACO) to solve combinatorial optimization problems. To ill…
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman Problems
Yuan Sun, Andreas Ernst, Xiaodong Li +1
Combinatorial optimization plays an important role in real-world problem solving. In the big data era, the dimensionality of a combinatorial optimization problem is usually very la…
Improving MMD-GAN Training with Repulsive Loss Function
Wei Wang, Yuan Sun, Saman Halgamuge
Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational…