An event-driven optimization framework for dynamic vehicle routing

The real-time operation of a fleet of vehicles introduces challenging optimization problems. In this work, we propose an event-driven framework that anticipates unknown changes arising in the context of dynamic vehicle routing. The framework is intrinsically parallelized to take advantage of modern multi-core and multi-threaded computing architectures. It is also designed to be easily embeddable in decision support systems that cope with a wide range of contexts and side constraints. We illustrate the flexibility of the framework by showing how it can be adapted to tackle the dynamic vehicle routing problem with stochastic demands.

► Event-driven multiple scenario approach for dynamic optimization problems
► Flexible dynamic optimization framework embeddable in decision support systems
► The framework intrinsically handles the parallelization of time‐consuming tasks.
► We illustrate the framework on the dynamic vehicle routing problem with stochastic demands (DVRPSD).
► Compares favorably against current methods for the DVRPSD with stronger assumptions