A Multi-Agent System for Solving the Dynamic Capacitated Vehicle Routing
  Problem with Stochastic Customers using Trajectory Data Mining
  The worldwide growth of e-commerce has created new challenges for logistics
companies, one of which is being able to deliver products quickly and at low
cost, which reflects directly in the way of sorting packages, needing to
eliminate steps such as storage and batch creation. Our work presents a
multi-agent system that uses trajectory data mining techniques to extract
territorial patterns and use them in the dynamic creation of last-mile routes.
The problem can be modeled as a Dynamic Capacitated Vehicle Routing Problem
(VRP) with Stochastic Customer, being therefore NP-HARD, what makes its
implementation unfeasible for many packages. The work's main contribution is to
solve this problem only depending on the Warehouse system configurations and
not on the number of packages processed, which is appropriate for Big Data
scenarios commonly present in the delivery of e-commerce products.
Computational experiments were conducted for single and multi depot instances.
Due to its probabilistic nature, the proposed approach presented slightly lower
performances when compared to the static VRP algorithm. However, the
operational gains that our solution provides making it very attractive for
situations in which the routes must be set dynamically.
