Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem
Annals of Operations Research - Special issue on Tabu search
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Swarm intelligence
Performance Measures for Dynamic Environments
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
A dynamic vehicle routing problem with time-dependent travel times
Computers and Operations Research
A Hybrid Approach for the Dynamic Vehicle Routing Problem with Time Windows
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Particle swarm with speciation and adaptation in a dynamic environment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Dynamic vehicle routing using genetic algorithms
Applied Intelligence
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
An Improved Evolutionary Algorithm for Dynamic Vehicle Routing Problem with Time Windows
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery
Computers and Operations Research
Adaptive granular local search heuristic for a dynamic vehicle routing problem
Computers and Operations Research
Dynamic vehicle routing with stochastic requests
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Restricted dynamic heterogeneous fleet vehicle routing problem with time windows
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
A space search optimization algorithm with accelerated convergence strategies
Applied Soft Computing
An ant colony algorithm for the multi-compartment vehicle routing problem
Applied Soft Computing
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Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the dynamic vehicle routing problem (DVRP), new orders arrive when the working day plan is in progress. In this case, routes must be reconfigured dynamically while executing the current simulation. The DVRP is an extension of a conventional routing problem, its main interest being the connection to many real word applications (repair services, courier mail services, dial-a-ride services, etc.). In this article, a DVRP is examined, and solving methods based on particle swarm optimization and variable neighborhood search paradigms are proposed. The performance of both approaches is evaluated using a new set of benchmarks that we introduce here as well as existing benchmarks in the literature. Finally, we measure the behavior of both methods in terms of dynamic adaptation.