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Constraint-Based Fleet Design Optimisation for Multi-compartment Split-Delivery Rich Vehicle Routing

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Principles and Practice of Constraint Programming (CP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10416))

Abstract

We describe a large neighbourhood search (LNS) solver based on a constraint programming (CP) model for a real-world rich vehicle routing problem with compartments arising in the context of fuel delivery. Our solver supports both single-day and multi-day scenarios and a variety of real-world aspects including time window constraints, compatibility constraints, and split deliveries. It can be used both to plan the daily delivery operations, and to inform decisions on the long-term fleet composition. We show experimentally the viability of our approach.

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Notes

  1. 1.

    Such constraints are rather common in the literature, and reflect the presence or absence of flow (or debit) meters at the replenishing plant or on the vehicles.

  2. 2.

    This limit allows to optimise the routing using an existing fleet.

  3. 3.

    Depending on the destruction strategy, the same value of \(dr\) can cause different numbers of variables to be relaxed. The per-variable timeout used in the repair step mitigates this disparity. Moreover, relaxing the problem “semantically” rather than randomly allows us to preserves the structure of the solution.

  4. 4.

    Because only a limited number of attempts is made at each \(dr\) level, restarting the search with \(dr= {\underline{dr}}\) allows us to try (again) non-expensive relaxations in case we previously missed a possibility for improvement.

  5. 5.

    Available at https://github.com/tunnuz/gecode-lns.

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Urli, T., Kilby, P. (2017). Constraint-Based Fleet Design Optimisation for Multi-compartment Split-Delivery Rich Vehicle Routing. In: Beck, J. (eds) Principles and Practice of Constraint Programming. CP 2017. Lecture Notes in Computer Science(), vol 10416. Springer, Cham. https://doi.org/10.1007/978-3-319-66158-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-66158-2_27

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