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A Variable Neighborhood Search for the Generalized Vehicle Routing Problem with Stochastic Demands

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9026))

Abstract

In this work we consider the generalized vehicle routing problem with stochastic demands (GVRPSD) being a combination of the generalized vehicle routing problem, in which the nodes are partitioned into clusters, and the vehicle routing problem with stochastic demands, where the exact demands of the nodes are not known beforehand. It is an NP-hard problem for which we propose a variable neighborhood search (VNS) approach to minimize the expected tour length through all clusters. We use a permutation encoding for the cluster sequence and consider the preventive restocking strategy where the vehicle restocks before it potentially runs out of goods. The exact solution evaluation is based on dynamic programming and is very time-consuming. Therefore we propose a multi-level evaluation scheme to significantly reduce the time needed for solution evaluations. Two different algorithms for finding an initial solution and three well-known neighborhood structures for permutations are used within the VNS. Results show that the multi-level evaluation scheme is able to drastically reduce the overall run-time of the algorithm and that it is essential to be able to tackle larger instances. A comparison to an exact approach shows that the VNS is able to find an optimal or near-optimal solution in much shorter time.

This work is supported by the Austrian Science Fund (FWF) grant P24660-N23.

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Notes

  1. 1.

    https://www.ads.tuwien.ac.at/w/Research/Problem_Instances#Generalized_Vehicle_Routing_Problem_with_Stochastic_Demands.

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Authors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Benjamin Biesinger, Bin Hu & Günther R. Raidl

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  1. Benjamin Biesinger
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  2. Bin Hu
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  3. Günther R. Raidl
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Correspondence to Benjamin Biesinger .

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Editors and Affiliations

  1. University of Stirling, Stirling, United Kingdom

    Gabriela Ochoa

  2. University of Málaga, Málaga, Spain

    Francisco Chicano

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Biesinger, B., Hu, B., Raidl, G.R. (2015). A Variable Neighborhood Search for the Generalized Vehicle Routing Problem with Stochastic Demands. In: Ochoa, G., Chicano, F. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2015. Lecture Notes in Computer Science(), vol 9026. Springer, Cham. https://doi.org/10.1007/978-3-319-16468-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-16468-7_5

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  • Print ISBN: 978-3-319-16467-0

  • Online ISBN: 978-3-319-16468-7

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