Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms

TL;DR

This paper explores using deep neural networks to enhance heuristic algorithms for utilitarian combinatorial assignment, aiming to improve solution quality and efficiency.

Contribution

It introduces a novel approach of integrating deep learning with heuristics to guide combinatorial assignment algorithms, showing promising preliminary results.

Findings

Neural network-guided heuristics can produce higher quality solutions.

The approach shows potential for faster solution generation.

Preliminary results are promising for future development.

Abstract

This paper presents preliminary work on using deep neural networks to guide general-purpose heuristic algorithms for performing utilitarian combinatorial assignment. In more detail, we use deep learning in an attempt to produce heuristics that can be used together with e.g., search algorithms to generate feasible solutions of higher quality more quickly. Our results indicate that our approach could be a promising future method for constructing such heuristics.