A multi-tiered vehicle routing problem with global cross-docking. (January 2022) - Smith, A.; Toth, P.; Bam, L.; van Vuuren, J.H.
Abstract: A new "rich" variation on the multi-objective vehicle routing problem (VRP), called the multi-tiered vehicle routing problem with global cross-docking (MTVRPGC), is introduced in this paper. With respect to previously studied VRPs, the MVRPTGC includes the following novel features: (i) segregation of facilities into different tiers that distinguish them in terms of different processing and storage capabilities, (ii) cross-docking at a pre-specified subset of facilities in the network (a feature referred to as global cross-docking ), and (iii) the possibility of spill-over into subsequent planning periods of demand for facility visitation. The problem originated from a real-life application concerning the collection and delivery of pathology specimens in the transportation network of a pathology health-care service provider. Other industrial applications may, however, benefit from this type of VRP, such as mail sorting. A mixed integer linear programming (MILP) model for this VRP is proposed, and tested computationally in respect of seventeen small hypothetical test instances. A multi-objective ant colony optimisation (MACO) algorithm for solving larger real-world instances of the MTVRPGC is also proposed. The solutions returned by the MACO algorithm are compared with those achieved by the MILP in respect to sixteen instances and also compared to actual collection and delivery routes of a real pathology healthcare service provider operating in South Africa and it is found that adopting the routes suggested by the algorithm results in substantial improvements of all the objectives pursued relative to the status quo . Highlights: Introduction of a novel class of multi-criteria vehicle routing problems. A comparison of the newly proposed problem and existing VRPs in the literature. Validation of the novel model using an MILP solver in respect of 16 test instances. Validation of a novel MACO algorithm, through comparison of a real-life instance.