Abstract
A recent line of research concerns the integration of ant colony optimization and constraint programming. Hereby, constraint programming is used for eliminating parts of the search tree during the solution construction of ant colony optimization. In the context of a single machine scheduling problem, for example, it has been shown that the integration of constraint programming can significantly improve the ability of ant colony optimization to find feasible solutions. One of the remaining problems, however, concerns the elevated computation time requirements of the hybrid algorithm, which are due to constraint propagation. In this work we propose a possible solution to this problem by integrating constraint programming with a specific version of ant colony optimization known as Beam-ACO. The idea is to reduce the time spent for constraint propagation by parallelizing the solution construction process as done in Beam-ACO. The results of the proposed algorithm show indeed that it is currently the best performing algorithm for the above mentioned single machine job scheduling problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abramson, D., Giddy, J., Kotler, L.: High performance parametric modeling with nimrod/g: Killer application for the global grid? In: International Parallel and Distributed Processing Symposium (IPDPS), pp. 520–528. IEEE Computer Society Press, Los Alamitos (2000)
Ascheuer, N., Fischetti, M., Grőtschel, M.: Solving the asymmetric travelling salesman problem with time windows by branch-and-cut. Mathematical Programming 90, 475–506 (2001)
Blum, C.: Beam-ACO: hybridizing ant colony optimization with beam search: an application to open shop scheduling. Computers and Operations Research 32, 1565–1591 (2005)
Blum, C., Blesa, M., Roli, A., Sampels, M. (eds.): Hybrid Metaheuristics: An Emerging Approach to Optimization. Studies in Computational Intelligence, vol. 114. Springer, Heidelberg (2008)
Blum, C., Dorigo, M.: The hyper-cube framework for ant colony optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 3, 1161–1172 (2004)
Camazine, S., Deneubourg, J.-L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton University Press, Princeton (2001)
Carpaneto, G., Martello, S., Toth, P.: Algorithms and codes for the assignment problem. Annals of Operations Research 13, 193–223 (1988)
Coello, C.A.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering 191, 1245–1287 (2002)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press, Cambridge (2001)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Glover, F.W., Kochenberger, G.A. (eds.): Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57. Springer, Heidelberg (2003)
López-Ibáñez, M., Blum, C., Thiruvady, D., Ernst, A.T., Meyer, B.: Beam-ACO based on stochastic sampling for makespan optimization concerning the TSP with time windows. In: Evolutionary Computation in Combinatorial Optimization. LNCS, vol. 5482, pp. 97–108. Springer, Heidelberg (2009)
Marriott, K., Stuckey, P.: Programming With Constraints. MIT Press, Cambridge (1998)
Meyer, B., Ernst, A.: Integrating ACO and constraint propagation. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 166–177. Springer, Heidelberg (2004)
Pinedo, M.L.: Planning and Scheduling in Manufacturing and Services. Springer, New York (2005)
Savelsbergh, M.W.P.: Local search in routing problems with time windows. Annals of Operations Research 4, 285–305 (1985)
Stűtzle, T., Hoos, H.H.: Max-min ant system. Future Generation Computer Systems 16, 889–914 (2000)
Gecode Team. Gecode: Generic constraint development environment (2008), http://www.gecode.org
Valente, J.M.S., Alves, R.A.F.S.: Beam search algorithms for the single machine total weighted tardiness scheduling problem with sequence-dependent setups. Computers and Operations Research 35(7), 2388–2405 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Thiruvady, D., Blum, C., Meyer, B., Ernst, A. (2009). Hybridizing Beam-ACO with Constraint Programming for Single Machine Job Scheduling. In: Blesa, M.J., Blum, C., Di Gaspero, L., Roli, A., Sampels, M., Schaerf, A. (eds) Hybrid Metaheuristics. HM 2009. Lecture Notes in Computer Science, vol 5818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04918-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-04918-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04917-0
Online ISBN: 978-3-642-04918-7
eBook Packages: Computer ScienceComputer Science (R0)