Vehicle-Routing-Problem-with-Backhaul

Vehicle Routing Problem with Backhaul (VRPB); Open vehicle routing problem; Lagrangian decomposition; Lagrangian relaxation algorithm; Clustering algorithm; CPLEX optimization solver; Python implementation

This repository is created to publicly share datasets and Python codes for the VRPB model proposed in: Parviziomran, I., Mahmoudi, M., 2020. A Lagrangian decomposition approach for the standard vehicle routing problem with backhauls.

In this repository you will find the following folders:

1. "Six Nodes" constitutes of three python codes corresponding to Section 5.3 in the foregoing reference.
2. "GJ" constitutes of 68 benchmark instances for the standard VRPB proposed by Goetschalckx and Jacobs-Blecha (1989) as well as two python codes corresponding to the Lagrangian relaxation algorithm in parallel and sequential layout (see Section 5.1).
3. "TV" constitutes of 33 benchmark instances for the standard VRPB proposed by Toth and Vigo (1997) as well as two python codes corresponding to the Lagrangian relaxation algorithm in parallel and sequential layout (see Section 5.1).
4. "Lansing-MI" constitutes of three randomly generated datasets with 100, 250, and 500 customers/nodes as well as python code related to the Lagrangian relaxation algorithm with cluster-first, route-second layout (see Sections 4.4 and 5.2).

References

Goetschalckx, M. and Jacobs-Blecha, C., 1989. The vehicle routing problem with backhauls. European Journal of Operational Research, 42(1), pp.39-51.

Toth, P., Vigo, D., 1997. An exact algorithm for the vehicle routing problem with backhauls. Transportation Science. 31(4), 372-385.

Cite our paper as:

Parviziomran, I., Mahmoudi, M., 2020. A Lagrangian decomposition approach for the standard vehicle routing problem with backhauls.