Affiliation:
1. Department of Mechanical Engineering, University of Canterbury, Kirkwood Ave, Christchurch 8140, New Zealand
Abstract
The transport of freight involves numerous intermediate steps, such as freight consolidation, truck allocation, and routing, all of which exhibit high day-to-day variability. On the delivery side, drivers usually cover specific geographic regions, also known as clusters, to optimise operational efficiency. A crucial aspect of this process is the effective allocation of resources to match business requirements. The discrete-event simulation (DES) technique excels in replicating intricate real-world operations and can integrate a multitude of stochastic variables, thereby enhancing its utility for decision making. The objective of this study is to formulate a routing architecture that integrates with a DES model to capture the variability in freight operations. This integration is intended to provide robust support for informed decision-making processes. A two-tier hub-and-spoke (H&S) architecture was proposed to simulate stochastic routing for the truck fleet, which provided insights into travel distance and time for cluster-based delivery. Real industry data were employed in geographic information systems (GISs) to apply the density-based spatial clustering of applications with noise (DBSCAN) clustering method to identify customer clusters and establish a truck plan based on freight demand and truck capacity. This clustering analysis and simulation approach can serve as a planning tool for freight logistics companies and distributors to optimise their resource utilisation and operational efficiency, and the findings may be applied to develop plans for new regions with customer locations and freight demands. The original contribution of this study is the integration of variable last-mile routing and an operations model for freight decision making.
Funder
Callaghan Innovation New Zealand
Transport Research Scholarship from the Ministry of Transport New Zealand
Subject
Electrical and Electronic Engineering,Artificial Intelligence,Urban Studies
Reference63 articles.
1. Huang, L.J. (2014, January 23–24). The key technology research for logistics vehicle monitoring system based on GPS. Proceedings of the 4th International Conference on Intelligent System and Applied Material, GSAM 2014, Taiyuan, China.
2. City Logistics in historic centers: Multi-Criteria Evaluation in GIS for city of Salvador (Bahia Brazil);Delgado;Case Stud. Transp. Policy,2019
3. Transportation route optimization with cost object in China;Wang;Clust. Comput.,2016
4. Smart city for sustainable urban freight logistics;Pan;Int. J. Prod. Res.,2021
5. Cyber physical system-enabled synchronization mechanism for pick-and-sort ecommerce order fulfilment;Kong;Comput. Ind.,2020