Modelling and solving the split-delivery vehicle routing problem, considering loading constraints and spoilage of commodities

In a typical supply chain, an aggregation hub is a strategically located facility that collects, processes and distributes commodities. This requires determining the collection/distribution fleet size, and the shortest routes to/from the served locations. Perishable commodities further require considering the order of collection/delivery and the loading positions inside the trucks. In this paper, the split-delivery vehicle routing problem (SDVRP) is modelled and solved, considering multiple perishable commodities, loading constraints, cross-docking and a heterogeneous fleet of trucks. The objective is to manage a fleet of trucks operating from an aggregation hub in order to minimise the total transportation and products spoilage costs. The problem is first formulated using as a novel mixed integer linear programming (MILP) model. Due to the complexity of the problem, a hybrid ant colony optimisation (ACO) algorithm is proposed to solve the problem. The ACO is embedded with local search (LS) techniques to improve its exploitation capabilities and convergence speed. Experiments are conducted to assess the performance of the ACO/LS algorithm in solving a number of benchmarked SDVRP instances. Results show that the proposed algorithm has an acceptable performance regarding the SDVRP, and can further handle all the aspects considered in the current problem efficiently.