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Most of these papers are focused from the operators’ perspective, trying to minimize the total distance of the vehicles. This is the objective also in Desrosiers et al. (1986) where a forward dynamic programming algorithm is used for the single vehicle DARP. In Psarfatis (1986), two different algorithms for the static version of the multi-vehicle DARP are compared. One of these algorithms is based on a clustering technique. Clustering is a technique also used by many others, among which Jaw et al.

1986) developed a method based on clustering for the static version of the multi-vehicle DARP with service quality constraints. A large number of clusters are constructed through column generation. Experiments have been made on instances of 50 to 250 requests, as well as on a real-life problem consisting of 2545 requests. In the experiments, the clustering approach is also compared to a parallel insertion heuristic. The experiments show that the clustering algorithm improves both the quality of the solutions as well as the computation times.

The information needed by the algorithm. If a tool used for such planning also should be possible to use for analyzing how different scenarios affect customers, the tool must be able to also include much more information. One example of this is the representation of the most important piece of information in any transport problem, the network. In most transport planning tools the network is represented by links and nodes where each link connects a from-node and a to-node, but without describing the actual geometry of the link.

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