Computationally Intelligent Online Dynamic Vehicle Routing by Explicit Load Prediction in an Evolutionary Algorithm

Abstract

In this paper we describe a computationally intelligent approach to solving the dynamic vehicle routing problem where a fleet of vehicles needs to be routed to pick up loads at customers and drop them off at a depot. Loads are introduced online during the actual planning of the routes. The approach described in this paper uses an evolutionary algorithm (EA) as the basis of dynamic optimization. For enhanced performance, not only are currently known loads taken into consideration, also possible future loads are considered. To this end, a probabilistic model is built that describes the behavior of the load announcements. This allows the routing to make informed anticipated moves to customers where loads are expected to arrive shortly. Our approach outperforms not only an EA that only considers currently available loads, it also outperforms a recently proposed enhanced EA that performs anticipated moves but doesn’t employ explicit learning. Our final conclusion is that under the assumption that the load distribution over time shows sufficient regularity, this regularity can be learned and exploited explicitly to arrive at a substantial improvement in the final routing efficiency.

This work is part of DEAL (Distributed Engine for Advanced Logistics) supported as project EETK01141 under the Dutch EET programme.