Constraint Programming Algorithms for Route Planning Exploiting Geometrical Information

TL;DR

This paper presents new constraint programming algorithms that leverage geometric information to improve route planning, focusing on the Euclidean TSP and potentially extending to related problems like VRP.

Contribution

The work introduces innovative CLP-based algorithms that exploit geometric data for more efficient route planning solutions.

Findings

Algorithms show improved efficiency in Euclidean TSP

Potential applicability to Euclidean VRP

Framework supports future extensions

Abstract

Problems affecting the transport of people or goods are plentiful in industry and commerce and they also appear to be at the origin of much more complex problems. In recent years, the logistics and transport sector keeps growing supported by technological progress, i.e. companies to be competitive are resorting to innovative technologies aimed at efficiency and effectiveness. This is why companies are increasingly using technologies such as Artificial Intelligence (AI), Blockchain and Internet of Things (IoT). Artificial intelligence, in particular, is often used to solve optimization problems in order to provide users with the most efficient ways to exploit available resources. In this work we present an overview of our current research activities concerning the development of new algorithms, based on CLP techniques, for route planning problems exploiting the geometric information intrinsically present in many of them or in some of their variants. The research so far has focused in particular on the Euclidean Traveling Salesperson Problem (Euclidean TSP) with the aim to exploit the results obtained also to other problems of the same category, such as the Euclidean Vehicle Routing Problem (Euclidean VRP), in the future.