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Covering Trains by Stations or The power of Data Reduction

Covering Trains by Stations or The power of Data Reduction. Karsten Weihe, ALEX98, 1998 Presented by Yantao Song. Overview. Problem description Data Reduction Computational study and experiment results. Problem.

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Covering Trains by Stations or The power of Data Reduction

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  1. Covering Trains by Stationsor The power of Data Reduction Karsten Weihe, ALEX98, 1998 Presented by Yantao Song

  2. Overview • Problem description • Data Reduction • Computational study and experiment results

  3. Problem • Given a set of trains, select a set of stations such that every train meets at least one of these stations and the number of selected trains is minimum.

  4. Formal Problem Description • Given an undirected graph G=(V, E), paths p1, p2……pn in G, and a partition V=V1∪ V2∪…∪ Vm of V into m disjoint vertex classes. • A PCV (path-cover by vertices) is a subset such that every path pimeets at least one vertex in V ‘. • The problem is to find a PCV V’ of minimum size |V’|. • More specifically, among all PCVs of minimum size, V’ should maximize the vector ( | V’∩ V1|, | V’∩ V2|, …, | V’∩ Vm|) lexicographically. • This problem is NP-Hard.

  5. Path plis an ordered sequence (v1l, …, vnll ) of vertices such that {vil, vi+1l} ∈ E for i = 1, …, nl–1. • Vertices and edges may occur more than once in the same path. • If an edge occurs more than once, it may occur several times with the same direction, or opposite direction. • It’s possible that two paths are exactly equal, or exact reverse of another path. • Without losing generality, we can assume that every edge belongs to some paths.

  6. Paper’s background • The data in this paper comes from the central German train railroad company. • Paths are the trains in the time schedules. • V is the union of all stations met by at least one of the trains. • We have one edge {v,w} ∈ E iff v, w are directly connected vertices by at least one train path. • Purpose: find a minimum number of stations. • It may be desirable to prefer some stations over other stations. So we have to “maximize the vector ( | V’∩ V1|, | V’∩ V2|, …, | V’∩ Vm|) lexicographically” as described above.

  7. Data Reduction • For a vertex v∈ V , P(v) denotes the set of all paths pi meeting v. • For a path pi, V(pi) denotes the ordinary set of vertices met by pi, which is unordered and don’t allow repetitions of vertices.

  8. Vertex’s dominance and equivalence • Dominance: Let i, j∈{1,…,m}, v∈Vi w∈Vj , if i<jandP(v)=P(w) or i>=jand P(w) P(v),then we say that v dominate w. • Equivalence: if P(v)=P(w) and i=j, v and w is equivalent.

  9. Path’s dominance and equivalence • Dominance: Let i, j∈{1,…,k}, pi pj , if V(pi) V(pj),then we say that pi dominate pj. • Equivalence: if V(pi) = V(pj),pi , pj is equivalent.

  10. Procedure of reducing vertex • Remove v from V, and all edges incident to v from E. • If u, w∈V are incident to v, there is a path pi which contains u-v-w or w-v-u as a subpath, then an edge {u, w} should be added into E. • All occurrences of v in paths are removed.

  11. Procedures of reducing a path • Remove pi from path set. • Every edge e∈E which doesn’t belong to any path afterward is removed. • Every vertex v∈V whose P(v) is empty afterwards is removed.

  12. If the vertex/path is dominated by or equivalent to some other vertices/paths. Then it’s feasible to be reduced. • At the early stage of reduction, use non-exhaustive vertex reduction; at the end of reduction, use exhaustive reduction. • After reducing, we can get an irreducible core. • An optimal solution to an irreducible core is also an optimal solution to the original instance. • Then we use the brute-force approach to solve the problem.

  13. Computational Study • Experiments based on real world data of Europe train network.

  14. Train classes • Class 0: high-speed trains; • Class 1: other international or long-distance trains; • Class 2: regional trains; • Class 3: local trains; • Class 4: other trains;

  15. Before Reduction

  16. After reduction

  17. For some instances which consist of trivial connected components, even we can get solution for problem only by data reduction. • For the other cases, the number of non-trivial connected components and size of these components are essential to complexity of the problem.

  18. Conclusion and Discussion • In this case, the size of problem can be reduced to 10% of original size.The reduction algorithm is very efficient for this case. • This is an extreme case, can’t be extended to all cases with so high efficiency. But it give us an case that even the problem is NP-Hard, but we still can solve it in affordable time for some real world cases.

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