# Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network

## TL;DR

This paper proposes a novel deep learning approach to approximate solutions for the Travelling Officer Problem, transforming it into a classification task and demonstrating its effectiveness on real-world data.

## Contribution

It introduces a customized deep neural network architecture for the Travelling Officer Problem and analyzes key architectural factors influencing performance.

## Key findings

- The network effectively approximates traditional solutions.
- Architectural components significantly impact performance.
- Demonstrated on real-world parking violation data.

## Abstract

Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques. Deep neural networks (DNNs) are a potentially promising and under-explored solution to solve these problems due to their powerful function approximation abilities, and their fast feed-forward computation. In this paper, we outline a method for converting an orienteering problem into a classification problem, and design a customised multi-layer deep learning network to approximate traditional optimisation solutions to this problem. We test the performance of the network on a real-world parking violation dataset, and conduct a generic study that empirically shows the critical architectural components that affect network performance for this problem.