39 citations · 64 across the 11 of their papers we have counts for
11 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…
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…
Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems
Hirad Assimi, Frank Neumann, Markus Wagner +1
Topology optimisation of trusses can be formulated as a combinatorial and multi-modal problem in which locating distinct optimal designs allows practitioners to choose the best des…
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…
Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
Mohammad Nabi Omidvar, Danial Yazdani, Juergen Branke +3
This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems.…
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…
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…
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…