Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics
developed with decades of research to solve large-scale MIP instances encountered in …

The Earth observation satellites (EOSs) scheduling problem is generally considered as a
complex combinatorial optimization problem due to various technical constraints. It is …

Graph neural networks (GNNs) have been widely used to predict properties and heuristics of
mixed-integer linear programs (MILPs) and hence accelerate MILP solvers. This paper …

In this article, we define a new routing problem that arises in the last‐mile delivery of parcels,
in which customers can be served either directly at home by a capacitated truck, or possibly …

In line with the growing trend of using machine learning to help solve combinatorial
optimisation problems, one promising idea is to improve node selection within a mixed …

Branch-and-cut is the most widely used algorithm for solving integer programs, employed by
commercial solvers like CPLEX and Gurobi. Branch-and-cut has a wide variety of tunable …

Unit commitment (UC) is a computationally challenging problem with the increasing
modeling scale and tight time limit. As a mixed-integer linear programming (MILP) problem …

This paper presents a novel approach exploiting machine learning to enhance the efficiency
of the branch-and-price algorithm. The focus is, specifically, on problems characterized by …

Solving combinatorial optimization problems involves a two-stage process that follows the
model-and-run approach. First, a user is responsible for formulating the problem at hand as …

In many operational applications, it is necessary to routinely find, within a very limited time
window, provably good solutions to challenging mixed-integer linear programming (MILP) …