Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization

• Main Authors: Giovanni Calabrò, Vincenza Torrisi, Giuseppe Inturri, Matteo Ignaccolo
• Format: Article
• Language: English
• Published: SpringerOpen 2020-04-01
• Series: European Transport Research Review
• Subjects: Ant Colony optimization; Vehicle routing problem; Multi-agent simulation, logistics
• Online Access: http://link.springer.com/article/10.1186/s12544-020-00409-7

Abstract This paper presents the first results of an agent-based model aimed at solving a Capacitated Vehicle Routing Problem (CVRP) for inbound logistics using a novel Ant Colony Optimization (ACO) algorithm, developed and implemented in the NetLogo multi-agent modelling environment. The proposed methodology has been applied to the case study of a freight transport and logistics company in South Italy in order to find an optimal set of routes able to transport palletized fruit and vegetables from different farms to the main depot, while minimizing the total distance travelled by trucks. Different scenarios have been analysed and compared with real data provided by the company, by using a set of key performance indicators including the load factor and the number of vehicles used. First results highlight the validity of the method to reduce cost and scheduling and provide useful suggestions for large-size operations of a freight transport service.