A decision aid algorithm for long-haul parcel transportation based on hierarchical network structure

• Published in: International journal of production research Vol. 61; no. 21; pp. 7198 - 7212
• Main Authors: Gras, Camille, Herr, Nathalie, Newman, Alantha
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 02.11.2023; Taylor & Francis LLC
• Subjects: Algorithms; Computer Science; hierarchical network; Integer programming; Linear programming; long-haul transportation; Mixed integer; Network design; Optimisation; Optimization; parcel transportation; Transportation networks; Transportation problem; Transportation problem (Operations research); Network Design; Long-Haul Transportation; Hierarchical Network; Parcel Transportation; Optimization
• ISSN: 0020-7543; 1366-588X; 1366-588X
• DOI: 10.1080/00207543.2022.2147233

With the explosion of e-commerce, optimising parcel transportation has become increasingly important. We study the long-haul stage of parcel transportation which takes place between sorting centres and delivery depots and is performed on a two-level hierarchical network. In our case study, we describe the application framework of this industrial problem faced by a French postal company: There are two vehicle types that must be balanced over the network on a daily basis, and there are two possible sorting points for each parcel, which allows a better consolidation of parcels. These industrial constraints are formalised in the Long-Haul Parcel Transportation Problem (LHPTP). We present a Mixed Integer Linear Program (MILP) and a hierarchical algorithm with aggregation of demands which uses the MILP as a subroutine. We perform numerical experiments on large-size datasets provided by a postal company, which consist of approximately 2500 demands on a network of 225 sites. These tests enable the tuning of certain parameters resulting in a tailored heuristic for the LHPTP. Our algorithm can serve as a decision aid tool for transportation managers to build daily transportation plans, modeled on solutions produced given daily demand forecasts and can also be used to improve the network design.