A Bio-Inspired Approach for a Dynamic Railway Problem

We consider the railway traveling salesman problem, denoted RTSP, in which a salesman using the railway network wishes to visit a certain number of cities to carry out his/her business, starting and ending at the same city, and having the goal to minimize the overall time of the journey. The RTSP is NP-hard and it is related to the generalized traveling salesman problem. The work done so far deals with static problems, where all the data are known in advance, i.e. before the optimization has started. The technological advances of the last few years give rise to a new class of problems, namely the dynamic railway traveling salesman problems, where new information are received as time progresses and must be dynamically incorporated into an evolving schedule. In this paper a dynamic railway traveling salesman problem is examined and a solving strategy, based on the ant colony optimization, is proposed. Finally, computational results are reported for real-world and synthetic data.