Figure 5
Coupling Two Train Sets of Different Types (e.g., White Framed and Gray Framed) at Two Positions Using Two Orientations (Tick = White Filled, Tock = Gray Filled) Results in 16 Possible Train Compositions
Source publication
Deutsche Bahn (DB) operates a large fleet of rolling stock (locomotives, wagons, and train sets) that must be combined into trains to perform rolling stock rotations. This train composition is a special characteristic of railway operations that distinguishes rolling stock rotation planning from the vehicle scheduling problems prevalent in other ind...
Contexts in source publication
Context 1
... these reasons, technical, organizational, and marketing considerations give rise to many train composition rules that have significant combinatorial complexity. We provide an illustration in Figure 5. ...
Citations
Graphs and hypergraphs are popular models for data structured representation. For example, traffic data, weather data, and animal skeleton data are all described by graph structures. Interval-valued fuzzy sets change the membership function of general fuzzy sets from single value functions to interval-valued functions, and thus describe the fuzzy attributes of things in terms of fuzzy intervals, which is more in line with the characteristics of fuzzy objectives. This paper aims to define the bipolar interval-valued fuzzy hypergraph to reveal the inner relationship of fuzzy data, and give some characterizations of it. The characteristics of bipolar interval-valued intuitionistic fuzzy hypergraph and bipolar interval-valued Pythagorean fuzzy hypergraph are studied. In addition, we discuss the characteristics of the bipolar interval-valued fuzzy threshold graph. Finally, some instances are presented as the applications of bipolar interval-valued fuzzy hypergraphs.
This paper proposes different algorithms to tackle the Generalized Train Unit Shunting Problem (G-TUSP). This is the pre-operational problem of managing rolling stock in a station, between arrivals and departures. It includes four sub-problems: the Train Matching Problem, the Track Assignment Problem, the Shunting Routing Problem, and the Shunting Maintenance Problem. In our algorithms, we consider different combinations for the integrated or sequential solutions of these sub-problems, typically considered independently in the literature. We assess the performance of the algorithms proposed in real-life and fictive instances representing traffic in Metz-Ville station, which includes four shunting yards. It is a main junction between two dense traffic lines in the east of France. In a thorough experimental analysis, we study the contribution of each sub-problem to the difficulty of the G-TUSP, and we identify the best algorithms. The outcomes of our algorithms are superior to solutions manually designed by experienced railway practitioners.
