Parallel savings based heuristics for the delivery problem
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Cyclic transfer algorithms for multivehicle routing and scheduling problems
Operations Research
A Subpath Ejection Method for the Vehicle Routing Problem
Management Science
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
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Classical heuristics for the capacitated VRP
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Real-Time Dispatching of Guided and Unguided Automobile Service Units with Soft Time Windows
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Drive: Dynamic Routing of Independent Vehicles
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Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
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Transportation Science
Notes on dynamic vehicle routing - the state of the art -
Notes on dynamic vehicle routing - the state of the art -
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A Hybrid Approach for the Dynamic Vehicle Routing Problem with Time Windows
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Journal of Heuristics
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Proceedings of the 8th annual conference on Genetic and evolutionary computation
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Proceedings of the 8th annual conference on Genetic and evolutionary computation
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Computers and Operations Research
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Applied Intelligence
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Comparing two models to generate hyper-heuristics for the 2d-regular bin-packing problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Computers and Industrial Engineering
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
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Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Vehicle routing and scheduling with dynamic travel times
Computers and Operations Research
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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EuroGP'07 Proceedings of the 10th European conference on Genetic programming
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IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
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EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
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Journal of Heuristics
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
HH-evolver: a system for domain-specific, hyper-heuristic evolution
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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In this paper we propose and evaluate an evolutionary-based hyper-heuristic approach, called EH-DVRP, for solving hard instances of the dynamic vehicle routing problem. A hyper-heuristic is a high-level algorithm, which generates or chooses a set of low-level heuristics in a common framework, to solve the problem at hand. In our collaborative framework, we have included three different types of low-level heuristics: constructive, perturbative, and noise heuristics. Basically, the hyper-heuristic manages and evolves a sophisticated sequence of combinations of these low-level heuristics, which are sequentially applied in order to construct and improve partial solutions, i.e., partial routes. In presenting some design considerations, we have taken into account the allowance of a proper cooperation and communication among low-level heuristics, and as a result, find the most promising sequence to tackle partial states of the (dynamic) problem. Our approach has been evaluated using the Kilby's benchmarks, which comprise a large number of instances with different topologies and degrees of dynamism, and we have compared it with some well-known methods proposed in the literature. The experimental results have shown that, due to the dynamic nature of the hyper-heuristic, our proposed approach is able to adapt to dynamic scenarios more naturally than low-level heuristics. Furthermore, the hyper-heuristic can obtain high-quality solutions when compared with other (meta) heuristic-based methods. Therefore, the findings of this contribution justify the employment of hyper-heuristic techniques in such changing environments, and we believe that further contributions could be successfully proposed in related dynamic problems.