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Tytuł artykułu

A Flexible and Adaptive Hyper-heuristic Approach for (Dynamic) Capacitated Vehicle Routing Problems

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Języki publikacji
EN
Abstrakty
EN
We present a self-adaptive hyper-heuristic capable of solving static and dynamic instances of the capacitated vehicle routing problem. The hyper-heuristic manages a generic sequence of constructive and perturbative low-level heuristics, which are gradually applied to construct or improve partial routes. We present some design considerations to allow the collaboration among heuristics, and to find the most promising sequence. The search process is carried out by applying a set of operators which constructs new sequences of heuristics, i.e., solving strategies. We have used a general and low-computational cost parameter control strategy, based on simple reinforcement learning ideas, to assign non-arbitrary reward/penalty values and guide the selection of operators. Our approach has been tested using some standard state-of-the-art benchmarks, which present different topologies and dynamic properties, and we have compared it with previous hyper-heuristics and several well-known methods proposed in the literature. The experimental results have shown that our approach is able to attain quite stable and good quality solutions after solving various problems, and to adapt to dynamic scenarios more naturally than other methods. Particularly, in the dynamic case we have obtained high-quality solutions when compared with other algorithms in the literature. Thus, we conclude that our self-adaptive hyper-heuristic is an interesting approach for solving vehicle routing problems as it has been able (1) to guide the search for appropriate operators, and (2) to adapt itself to particular states of the problem by choosing a suitable combination of heuristics.
Wydawca
Rocznik
Strony
29--60
Opis fizyczny
Bibliogr. 43 poz., tab., wykr.
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autor
autor
  • Departamento de Informatica, Universidad Tecnica Federico Santa Marýa, Avenida Espa�na 1680, Casilla 110-V, 2390123 Valparaýso, Chile., pgarrido@inf.utfsm.cl
Bibliografia
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  • [4] Burke, E., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Qu, R.: A survey and classification of hyperheuristics, Journal of Heuristics, to appear.
  • [5] Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology, Handbook of Metaheuristics, 57, 2003, 457-474.
  • [6] Burke, E., Petrovic, S., Qu, R.: Case-based heuristic selection for timetabling problems, Journal of Scheduling, 9(2), 2006, 115-132.
  • [7] Burke, E., Silva, J. L., Soubeiga, E.: Multi-objective hyper-heuristic approaches for space allocation and timetabling, Meta-heuristics: Progress as Real Problem Solvers, 32, 2005, 129-158.
  • [8] Chakhlevitch, K., Cowling, P.: Hyperheuristics: Recent Developments, in: Adaptive and Multilevel Metaheuristics, vol. 136 of Studies in Computational Intelligence, Springer, 2008, 3-29.
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  • [11] Dorigo, M., Stützle, T.: Ant Colony Optimization (Bradford Books), The MIT Press, 2004.
  • [12] Dowsland, K., Soubeiga, E., Burke, E.: A simulated annealing based hyperheuristic for determining shipper sizes for storage and transportation, European Journal of Operational Research, 179(3), 2007, 759-774.
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  • [16] Garrido, P., Castro, C.: Stable solving of CVRPs using hyperheuristics, Genetic and Evolutionary Computation Conference, GECCO '09, ACM, 2009.
  • [17] Garrido, P., Castro, C., Monfroy, E.: Towards a flexible and adaptable hyperheuristic approach for VRPs, 2009 International Conference on Artificial Intelligence, IC-AI 2009, CSREA Press, 2009.
  • [18] Garrido, P., Riff, M.: Collaboration between hyperheuristics to solve strip-packing problems, 12th International Fuzzy Systems Association World Congress, IFSA 2007, 4529, Springer-Verlag, 2007.
  • [19] Gendreau, M., Guertin, F., Potvin, J., S´eguin, R.: Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries, Transportation Research Part C: Emerging Technologies, 14(3), 2006, 157-174.
  • [20] Gendreau, M., Potvin, J.-Y., Bräysy, O., Hasle, G., Løkketangen, A.: Metaheuristics for the vehicle routing problem and its extensions: a categorized bibliography, in: The Vehicle Routing Problem: Latest Advances and New Challenges, vol. 43 of Operations Research/Computer Science Interfaces Series, Springer-Verlag, 2008, 143-169.
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  • [23] Kilby, P., Prosser, P., Shaw, P.: Dynamic VRPs: a study of scenarios, Technical Report APES-06-1998, University of Strathclyde, Glasgow, Scotland, 1998.
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  • [26] Laporte, G., Gendreau, M., Potvin, J., Semet, F.: Classical and modern heuristics for the vehicle routing problem, International Transactions in Operational Research, 7, 2000, 285-300.
  • [27] Lysgaard, J., Letchford, A., Eglese, R.: A new branch-and-cut algorithm for the capacitated vehicle routing problems, Mathematical Programming, 100(2), 2004, 423-445.
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  • [29] Montemanni, R., Gambardella, L., Rizzoli, A., Donati, A.: A new algorithm for a dynamic vehicle routing problem based on ant colony system, Proceedings of ODYSSEUS 2003, Palermo, Italy, 2003.
  • [30] Montemanni, R., Gambardella, L., Rizzoli, A., Donati, A.: Ant colony system for a dynamic vehicle routing problem, Journal of Combinatorial Optimization, 10(4), 2005, 327-343.
  • [31] Montero, E., Riff, M.: Calibrating strategies for evolutionary algorithms, IEEE Congress on Evolutionary Computation, CEC 2007, IEEE, 2007.
  • [32] Nannen, V., Eiben, A.: Efficient relevance estimation and value calibration of evolutionary algorithm parameters, IEEE Congress on Evolutionary Computation, CEC 2007, IEEE, 2007.
  • [33] Özcan, E., Kalender, M., Burke, E.: A greedy gradient-simulated annealing hyperheuristic, 10th International Conference on Parallel Problem Solving From Nature, Dortmund, Germany, 2008.
  • [34] Özcan, E., Uyar, S., Burke, E.: A greedy hyper-heuristic in dynamic environments, Genetic and Evolutionary Computation Conference (Conference Companion), GECCO '09, ACM, 2009.
  • [35] Pankratz, G.: Dynamic vehicle routing by means of a genetic algorithm, International Journal of Physical Distribution & Logistics Management, 35(5), 2005, 362-383.
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  • [37] Rego, C.: A subpath ejection chain method for the vehicle routing problem, Management Science, 44(10), 1996, 1447-1459.
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  • [39] Ross, P., Marin-Blázquez, J. G., Schulenburg, S., Hart, E.: Learning a procedure that can solve hard binpacking problems: a new GA-based approach to hyperheurstics, Genetic and Evolutionary Computation Conference, GECCO '03, ACM, 2003.
  • [40] Soubeiga, E.: Development and application of hyperheuristics to personnel scheduling, Ph.D. Thesis, University of Nottingham, United Kingdom, 2003.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0027-0014
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