Towards a Robust Planning of Train Shunting and Servicing

Abstract

Due to growing demand of sustainable transport, the number of passengers carried by railways is increasing rapidly. Passenger railway operators have to create denser timetables and expand their rolling stock, which leads to logistic challenges on the hubs of the railway network. The hubs consist of train stations and their surrounding shunting yards. Trains that are not operating the timetable are parked, cleaned and maintained on the shunting yards. The planning of all shunting movements and maintenance activities on a hub is still largely done by hand, but with the increasing number of trains this puzzle becomes more and more difficult. In this thesis we focus on developing methods to support and improve the planning of the railway hubs. We present algorithms to generate plans for the integrated shunting, maintenance scheduling and staff assignment problem, and show that these solution methods can solve real-world problem instances of shunting yards and railway hubs in the Netherlands. We also study at the robustness of these plans: can the plan still be implemented properly if small, frequent disruptions such as delays occur? We show that we can create robust plans by guiding the solution methods to plans with beneficial properties such as slack. The planning method presented in this thesis has been tested by the passenger railway company NS at various hubs. The method has proved successful and is now being further developed to support the planners of railway hubs in the Netherlands.