A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1401.3794v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
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We consider several Vehicle Routing Problems (VRP) with profits, which seek to select a subset of customers, each one being associated with a profit, and to design service itineraries. When the sum of profits is maximized under distance constraints, the problem is usually called team orienteering problem. The capacitated profitable tour problem seeks to maximize profits minus travel costs under capacity constraints. Finally, in the VRP with private fleet and common carrier, some customers can<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1401.3794v2">arXiv:1401.3794v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/al5mqsulzrhfpio3uliud3d5mi">fatcat:al5mqsulzrhfpio3uliud3d5mi</a> </span>
more »... delegated to an external carrier subject to a cost. Three families of combined decisions must be taken: customers selection, assignment to vehicles, and sequencing of deliveries for each route. We propose a new neighborhood search for these problems which explores an exponential number of solutions in pseudo polynomial time. The search is conducted with standard VRP neighborhoods on an "exhaustive" solution representation, visiting all customers. Since visiting all customers is usually infeasible or sub-optimal, an efficient "Select" algorithm, based on resource constrained shortest paths, is repeatedly used on any new route to find the optimal subsequence of visits to customers. The good performance of these neighborhood structures is demonstrated by extensive computational experiments with a local search, an iterated local search and a hybrid genetic algorithm. Intriguingly, even a local-improvement method to the first local optimum of this neighborhood achieves an average gap of 0.09% on classic team orienteering benchmark instances, rivaling with the current state-of-the-art metaheuristics. Promising research avenues on hybridizations with more standard routing neighborhoods are also open.
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