Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
The dynamic pickup and delivery problem with time windows
The dynamic pickup and delivery problem with time windows
Learning, anticipation and time-deception in evolutionary online dynamic optimization
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching
Transportation Science
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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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.