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Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers

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Abstract

The vehicle routing problem with stochastic demands and customers (VRPSDC) requires finding the optimal route for a capacitated vehicle that delivers goods to a set of customers, where each customer has a fixed probability of requiring being visited and a stochastic demand. For large instances, the evaluation of the cost function is a primary bottleneck when searching for high quality solutions within a limited computation time. We tackle this issue by using an empirical estimation approach. Moreover, we adopt a recently developed state-of-the-art iterative improvement algorithm for the closely related probabilistic traveling salesman problem. We integrate these two components into several metaheuristics and we show that they outperform substantially the current best algorithm for this problem.

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Acknowledgments

This research has been supported by “E-SWARM – Engineering Swarm Intelligence Systems”, an European Research Council Advanced Grant awarded to Marco Dorigo (Grant Number 246939). The authors acknowledge support from the Fonds de la Recherche Scientifique, F.R.S.-FNRS of the French Community of Belgium.

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Authors and Affiliations

  1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA

    Prasanna Balaprakash

  2. IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Mauro Birattari, Thomas Stützle & Marco Dorigo

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  1. Prasanna Balaprakash
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  2. Mauro Birattari
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  4. Marco Dorigo
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Correspondence to Prasanna Balaprakash.

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Balaprakash, P., Birattari, M., Stützle, T. et al. Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers. Comput Optim Appl 61, 463–487 (2015). https://doi.org/10.1007/s10589-014-9719-z

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