A multi-agent system for solving the Dynamic Capacitated Vehicle Routing Problem with stochastic customers using trajectory data mining

• Published in: Expert systems with applications 2022-06, Vol.195, p.116602, Article 116602
• Main Authors: Fonseca-Galindo, Juan Camilo, de Castro Surita, Gabriela, Neto, José Maia, de Castro, Cristiano Leite, Lemos, André Paim
• Format: Article
• Language: English
• Subjects: Algorithms; Big Data; Customers; Data mining; Dynamic Capacitated Vehicle Routing Problem with stochastic customers; E-commerce logistics; Heuristic; Logistics; Multi-agent systems; Multiagent systems; Packages; Route planning; Vehicle routing
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.116602

The worldwide growth of e-commerce has created new challenges for logistics companies, such as delivering products quickly and cheaply. This paper presents a heuristic to solve the last-mile route creation problem dynamically. The heuristic is based on a multi-agent system integrated with trajectory data mining techniques to extract territorial patterns and use them to solve the Dynamic Capacitated Vehicle Routing Problem with Stochastic Customers. Our solution approach is focused on a linear-time heuristic that depends only on the Warehouse system configurations and not on the total number of packages processed, which is suitable for express delivery logistics companies that must process a large number of packages per day. We compare our proposal with benchmark algorithms from the literature; additionally, we evaluate its performance and robustness under different scenarios. Results show that our solution approach is effective for scenarios in which routes must be set dynamically from a continuous stream of packages. •Trajectory data mining can improve last-mile routes.•Stochastic and dynamic VRP solutions make cross-docking operations more efficient.•Multi-agent systems allow efficiently implementing the dynamic VRP in warehouse systems.