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Deep learning has been extended to a number of new domains with criticalsuccess, though some traditional orienteering problems such as the TravellingSalesman Problem (TSP) and its variants are not commonly solved using suchtechniques. Deep neural networks (DNNs) are a potentially promising andunder-explored solution to solve these problems due to their powerful functionapproximation abilities, and their fast feed-forward computation. In thispaper, we outline a method for converting an orienteering problem into aclassification problem, and design a customised multi-layer deep learningnetwork to approximate traditional optimisation solutions to this problem. Wetest the performance of the network on a real-world parking violation dataset,and conduct a generic study that empirically shows the critical architecturalcomponents that affect network performance for this problem.

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