Computational Intelligence for Solving Difficult Transportation Problems

Abstract

Intelligent Transportation Systems (ITSs) are providing a broad range of services to all the actors involved in any type of transportation activities. Path planning is a common transportation problem, which can be extremely difficult when an exact solution is sought. The quality of heuristic approaches to this problem is therefore important, as they are now implemented as planning modules into almost all the ITSs on the market. The goal of this paper is to analyze several distributed reinforcement learning techniques involving the collective experience of artificial ants when approaching a transportation problem. Traveling Salesman Problem (TSP), a well-known NP-hard transportation problem, was chosen for computational experiments. In order to assess the behavior of the investigated solving methods, two medium-size TSP instances, covering large areas, are proposed. The results are compared with those provided by a state-of-the-art technique, namely the highly effective Lin-Kernighan(LK) heuristic procedure for generating optimum and near-optimum solutions for the TSP. This paper connects the academic Operational Research with societal needs in Transportation and Logistics.