Abstract
We study the chance-constrained vehicle routing problem (CCVRP), a version of the vehicle routing problem (VRP) with stochastic demands, where a limit is imposed on the probability that each vehicle’s capacity is exceeded. A distinguishing feature of our proposed methodologies is that they allow correlation between random demands, whereas nearly all existing methods for the stochastic VRP require independent demands. We first study an edge-based formulation for the CCVRP, in particular addressing the challenge of how to determine a lower bound on the number of trucks required to serve a subset of customers. We then investigate the use of a branch-and-cut-and-price (BCP) algorithm. While BCP algorithms have been considered the state of the art in solving the deterministic VRP, few attempts have been made to extend this framework to the stochastic VRP.
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Notes
- 1.
Note that \(\mathbb {P}\{ D(S) \le b\} \ge 1-\epsilon \iff Q_{1-\epsilon }( D(S) ) \le b\).
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Acknowledgments
Fukasawa was supported by NSERC Discovery Grant RGPIN-05623. Luedtke was supported by NSF grants CMMI-0952907 and CMMI-1130266, and ONR award N00014-15-1-2268.
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Dinh, T., Fukasawa, R., Luedtke, J. (2016). Exact Algorithms for the Chance-Constrained Vehicle Routing Problem. In: Louveaux, Q., Skutella, M. (eds) Integer Programming and Combinatorial Optimization. IPCO 2016. Lecture Notes in Computer Science(), vol 9682. Springer, Cham. https://doi.org/10.1007/978-3-319-33461-5_8
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