activity
20142023
most citedGeneralization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman Problems

39 citations · 59 across the 13 of their papers we have counts for

collaborators

14 papers

math.OC2023★ 1 cited

The Agricultural Spraying Vehicle Routing Problem With Splittable Edge Demands

Qian Wan, Rodolfo García-Flores, Simon A. Bowly +2

In horticulture, spraying applications occur multiple times throughout any crop year. This paper presents a splittable agricultural chemical sprayed vehicle routing problem and for…

cs.AI2022★ 2 cited

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…

cs.NE2022

An Efficient Merge Search Matheuristic for Maximising the Net Present Value of Project Schedules

Dhananjay R. Thiruvady, Su Nguyen, Christian Blum +1

Resource constrained project scheduling is an important combinatorial optimisation problem with many practical applications. With complex requirements such as precedence constraint…

math.OC2022

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…

math.OC2022

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…

math.OC2021★ 7 cited

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…