Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Optimal Expected-Time Algorithms for Closest Point Problems
ACM Transactions on Mathematical Software (TOMS)
Metaheuristics for the capacitated VRP
The vehicle routing problem
Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
Active guided evolution strategies for large-scale vehicle routing problems with time windows
Computers and Operations Research
The memetic self-organizing map approach to the vehicle routing problem
Soft Computing - A Fusion of Foundations, Methodologies and Applications
An Agent-Oriented Approach for the Dynamic Vehicle Routing Problem
IWAISE '08 Proceedings of the 2008 International Workshop on Advanced Information Systems for Enterprises
Dynamic vehicle routing with stochastic requests
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
International Journal of Applied Logistics
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We consider the dynamic vehicle routing problem (dynamic VRP). In this problem, new customer demands are received along the day. Hence, they must be serviced at their locations by a set of vehicles in real time. The approach to address the problem is a hybrid method which manipulates the self-organizing map (SOM) neural network into a population based evolutionary algorithm. The method, called memetic SOM, illustrates how the concept of intermediate structure, also called elastic net or adaptive mesh concept, provided by the original SOM can naturally be applied into a dynamic setting. The experiments show that the heuristic outperforms the approaches that were applied to the Kilby et al. 22 problems with up to 385 customers. It performs better with respect to solution quality than the ant colony algorithm MACS-VRPTW, a genetic algorithm, and a multi-agent oriented approach, with a computation time used roughly 100 times lesser.