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intervals from start to destination We use a weighted criterion function that weights the total number of track changes required in order to reach the destination with the total distance of the routings between the tracks involved In general, the planner will try to avoid track changes, so the weight of a track change will dominate the routing distance An additional feature of the sequence is that it has to resemble the necessity of visiting the cleaning track somewhere over time Therefore, the algorithm will have to include the possibility of stating a set of intermittent nodes (ie, intervals on the cleaning track) from which at least one has to be included in the nal sequence before the departure track is reached.

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Finally, it has to be possible for the planner to block several tracks that may not be included at all in the nal sequence, because they have to be reserved for other purposes The problem can again be solved using a K-shortest-path algorithm We tailored the algorithm of Section 1742 to solve this problem ef ciently We rst determine the shortest routings between all tracks at which this train may be located The network for this rst step therefore consists of only one node per track We repeat the Dijkstra (1959) shortest-path algorithm for each track to determine the shortest distance between tracks, resulting in an O(11 N 3) complexity (with N the number of 2 tracks) Because all arc lengths are non-negative, this is better than obtained by Floyd (1962) or Dantzig (1967) Note that this step has to be performed only once.

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Next, the algorithm creates a new graph, consisting of intervals as nodes (see Figure 178) In general, this network will contain far more nodes, depending on the utilization of the tracks over time We apply the extended K-shortest-path algorithm of Figure 177 to this network, using weights for track changes and total mileage The condition of including at least one of the intermittent nodes in the K shortest paths has been taken care of by applying a two-stage solution process The rst stage determines in a single pass of the algorithm the K shortest paths from source node to each intermittent node The second stage applies the same algorithm for nding the K shortest paths from the destination node to each intermittent node.

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This approach is more ef cient than applying a single-pass all-K-shortest-paths approach [based on Floyd (1962)] that determines the K shortest paths between each pair of nodes in the network The two-stage solution approach has proven to.

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