Multi-criteria decision making for site selection of freight macro consolidation centres in the Solent region

The growing urbanisation in the UK is causing a rise in urban freight volumes. This in turn increases negative impacts on the area such as pollution, congestion, etc. In addition, there is usually a shortage of urban space in cities including in the Solent region and its cities such as Portsmouth (the focus of this project and paper). A Macro Consolidation Centre (MCC) is one method to mitigate this problem by consolidating freight flows between different suppliers and the last mile hubs or customers.
Transport is the largest contributor of emissions, with total carbon emissions of 24% produced in the UK and 22% in Portsmouth in 2020. Freight transportation contributes a large proportion of air pollution, which has negative environmental and social impacts. By consolidating the multiple shipments into a single shipment, an MCC can reduce the number of delivery vehicles and reduce the vehicle mileage with less fuel use, therefore reducing the impact of vehicle emissions.

The MCC project addressed in this paper is part of the Solent Future Transport Zone project (FTZ) funded by the UK’s Department for Transport and led by Solent Transport.

The problem of finding the best location for MCC is a multi-criteria decision-making problem, which involves a set of decision criteria. This paper considers developing a multi-criteria-decision support framework using an Analytic Hierarchy Process (AHP) to find a suitable site for MCC to serve the city of Portsmouth, then extending the methodology for the Solent region. The criteria proposed in this paper includes the sustainability criteria: Economic, Social and Environmental with sub-criteria that are identified and categorised within the main criteria. The locations considered for MCC are selected based on real GIS and geographic data from Portsmouth. The proposed multi-criteria selection framework comprises the following four steps: Development of hierarchical structure for the AHP, identification of the weights, data collection and finally, selection of the suitable location of a MCC. The proposed approach is evaluated with a case study of an MCC in Portsmouth. The results demonstrate that the proposed multi-criteria framework is capable of finding the best location for the MCC in Portsmouth.