In this paper, we present a review of deterministic software for solving convex MINLP
problems as well as a comprehensive comparison of a large selection of commonly …

The branch-and-bound (B&B) algorithmic framework has been used successfully to find
exact solutions for a wide array of optimization problems. B&B uses a tree search strategy to …

Combinatorial optimization problems are typically tackled by the branch-and-bound
paradigm. We propose a new graph convolutional neural network model for learning branch …

We present the design and implementation of a custom discrete optimization technique for
building rule lists over a categorical feature space. Our algorithm produces rule lists with …

Recently, unmanned aerial vehicles (UAVs) have attracted lots of attention because of their
high mobility and low cost. This article investigates a communication system assisted by …

The design of strategies for branching in Mixed Integer Programming (MIP) is guided by
cycles of parameter tuning and offline experimentation on an extremely heterogeneous …

Abstract A recent Graph Neural Network (GNN) approach for learning to branch has been
shown to successfully reduce the running time of branch-and-bound algorithms for Mixed …

Determining environmentally-and economically-optimal energy systems designs and
operations is complex. In particular, the integration of weather-dependent renewable energy …

Many optimal decision problems in scientific, engineering, and public sector applications
involve both discrete decisions and nonlinear system dynamics that affect the quality of the …

This is a review of the literature on variants and extensions of the standard location-routing
problem published since the last survey, by Nagy and Salhi, appeared in 2006. We propose …