Hybrid estimation of distribution algorithm for the quay crane scheduling problem

Applied Soft Computing Journal

Volumn 13, Issue 10, 2013, Pages 4063-4076

  Expósito Izquierdo, Christopher ^a   González Velarde, José Luis ^b   Melián Batista, Belén ^a   Moreno Vega, J Marcos ^a  

^a UNIVERSIDAD DE LA LAGUNA   (Spain)

^b SCHOOL OF ENGINEERING AND SCIENCES   (Mexico)

Author keywords

Container terminal; Estimation of Distribution Algorithm; Heuristics; Local search; Quay Crane Scheduling Problem

Indexed keywords

CONTAINERS; CRANES; LOCAL SEARCH (OPTIMIZATION); OPTIMIZATION; RAILROAD YARDS AND TERMINALS; SCHEDULING; UNLOADING;

CONTAINER TERMINAL; ESTIMATION OF DISTRIBUTION ALGORITHMS; HEURISTICS; LOCAL SEARCH; QUAY CRANE SCHEDULING;

PORT TERMINALS;

EID: 84885290392     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2013.05.006     Document Type: Article

References (44)

1. C.W. Ahn, J. An, J.-C. Yoo, Estimation of particle swarm distribution algorithms: combining the benefits of pso and edas, Information Sciences 192 (0) (2012) 109-119.
2. C. Bierwirth, F. Meisel, A fast heuristic for quay crane scheduling with interference constraints, Journal of Scheduling 12 (2009) 345-360.
3. M. Birattari, Tuning Metaheuristics: A Machine Learning Perspective, 1st ed., Springer Publishing Company, Incorporated, 2005 (2nd printing ed., 2009).
4. M. Birattari, Z. Yuan, P. Balaprakash, T. Stützle, F-race and iterated f-race: an overview, in: T. Bartz-Beielstein, M. Chiarandini, L. Paquete, M. Preuss (Eds.), Experimental Methods for the Analysis of Optimization Algorithms, Springer, Berlin/Heidelberg, 2010, pp. 311-336.
5. E.P. George, J. Box, S. Hunter, W.G. Hunter, Statistics for Experimenters: Design, Innovation, and Discovery, 2nd ed., Wiley-Interscience, 2005.
6. S.-L. Chao, Y.-J. Lin, Evaluating advanced quay cranes in container terminals, Transportation Research Part E: Logistics and Transportation Review 47 (4) (2011) 432-445.
7. J.H. Chen, D.-H. Lee, J. Xin Cao, Heuristics for quay crane scheduling at indented berth, Transportation Research Part E: Logistics and Transportation Review 47 (6) (2011) 1005-1020.
8. S.-H. Chen, M.-C. Chen, Addressing the advantages of using ensemble probabilistic models in estimation of distribution algorithms for scheduling problems, International Journal of Production Economics 141 (1) (2013) 24-33.
9. S.H. Chung, K.L. Choy, A modified genetic algorithm for quay crane scheduling operations, Expert Systems with Applications 39 (4) (2012) 4213-4221.
10. W.W. Daniel, Applied Nonparametric Statistics, PWS-Kent Publishing Company, Boston, 1990.
11. C. Fleurent, F. Glover, Improved constructive multistart strategies for the quadratic assignment problem using adaptive memory, INFORMS Journal on Computing 11 (1999) 198-204.
12. I. García del Amo, D. Pelta, Srcs: a technique for comparing multiple algorithms under several factors in dynamic optimization problems, in: E. Alba, A. Nakib, P. Siarry (Eds.), Metaheuristics for Dynamic Optimization, volume 433 of Studies in Computational Intelligence, Springer, Berlin/Heidelberg, 2013, pp. 61-77.
13. M. Hauschild, M. Pelikan, An introduction and survey of estimation of distribution algorithms, Swarm and Evolutionary Computation 1 (3) (2011) 111-128.
14. M.W. Hauschild, M. Pelikan, K. Sastry, D.E. Goldberg, Using previous models to bias structural learning in the hierarchical boa, in: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO'08, ACM, 2008, pp. 415-422.
15. Y. Hong, S. Kwong, Y. Chang, Q. Ren, Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm, Pattern Recognition 41 (9) (2008) 2742-2756.
16. C. Expósito Izquierdo, J. Luis González Velarde, B. Melián-Batista, J. Marcos Moreno-Vega, Estimation of distribution algorithm for the quay crane scheduling problem, in: D. Alejandro Pelta, N. Krasnogor, D. Dumitrescu, C. Camelia Chira, R. Lung (Eds.), Nature Inspired Cooperative Strategies for Optimization (NICSO 2011), Volume 387 of Studies in Computational Intelligence, Springer, Berlin/Heidelberg, 2012, pp. 183-194.
17. C. Expósito Izquierdo, B. Melián-Batista, J. Marcos Moreno-Vega, An estimation of distribution algorithm for solving the quay crane scheduling problem with availability constraints, in: M. Grana, C. Toro, J. Posada, R.J. Howlett, L.C. Jain (Eds.), KES, Volume 243 of Frontiers in Artificial Intelligence and Applications, IOS Press, 2012, pp. 10-19.
18. C. Expósito Izquierdo, B. Melián-Batista, J. Moreno-Vega Marcos, Premarshalling problem: heuristic solution method and instances generator, Expert Systems with Applications 39 (9) (2012) 8337-8349.
19. B. Jarboui, M. Eddaly, P. Siarry, An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems, Computers & Operations Research 36 (9) (2009) 2638-2646.
20. Y. Jin, A comprehensive survey of fitness approximation in evolutionary computation, Soft Computing 9 (2005) 3-12. (Pubitemid 40097880)
21. K.H. Kim, Y.-M. Park, A crane scheduling method for port container terminals, European Journal of Operational Research 156 (3) (2004) 752-768.
22. P. Legato, R. Trunfio, F. Meisel, Modeling and solving rich quay crane scheduling problems, Computers & Operations Research 39 (9) (2012) 2063-2078.
23. H. Li, Y. Hong, S. Kwong, Subspace estimation of distribution algorithms: to perturb part of all variables in estimation of distribution algorithms, Applied Soft Computing 11 (3) (2011) 2974-2989.
24. L. Wang, C. Fang, An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem, Computers & Operations Research 39 (2) (2012) 449-460, http://dx.doi.org/10.1016/j.cor.2011. 05.008, issn: 0305-0548
25. F. Meisel, The quay crane scheduling problem with time windows, Naval Research Logistics (NRL) 58 (7) (2011) 619-636.
26. F. Meisel, C. Bierwirth, A unified approach for the evaluation of quay crane scheduling models and algorithms, Computers & Operations Research 38 (3) (2011) 683-693.
27. L. Moccia, J.-F. Cordeau, M. Gaudioso, G. Laporte, A branch-and-cut algorithm for the quay crane scheduling problem in a container terminal, Naval Research Logistics 53 (1) (2006) 45-59.
28. M. Flavia Monaco, M. Sammarra, Quay crane scheduling with time windows, one-way and spatial constraints, International Journal of Shipping and Transport Logistics 3 (4) (2011) 454-474.
29. H. Mühlenbein, G. Paaß, From recombination of genes to the estimation of distributions i. binary parameters, in: H.-M. Voigt, W. Ebeling, I. Rechenberg, H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature - PPSN IV, volume 1141 of Lecture Notes in Computer Science, Springer, Berlin/Heidelberg, 1996, pp. 178-187. (Pubitemid 126128306)
30. E. Enda Ridge, D. Kudenko, Tuning an algorithm using design of experiments, in: T. Bartz-Beielstein, M. Chiarandini, L. Paquete, M. Preuss (Eds.), Experimental Methods for the Analysis of Optimization Algorithms, Springer, Berlin/Heidelberg, 2010, pp. 265-286.
31. B. Saavedra-Moreno, S. Salcedo-Sanz, A. Paniagua-Tineo, L. Prieto, A. Portilla- Figueras, Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms, Renewable Energy 36 (11) (2011) 2838-2844.
32. M. Sammarra, J.-F. Cordeau, G. Laporte, M. Monaco, A Tabu search heuristic for the quay crane scheduling problem, Journal of Scheduling 10 (2007) 327-336. (Pubitemid 47501054)
33. K. Sastry, M. Pelikan, D. Goldberg, Efficiency enhancement of estimation of distribution algorithms, in: M. Pelikan, K. Sastry, E. Cantú-Paz (Eds.), Scalable Optimization via Probabilistic Modeling, Volume 33 of Studies in Computational Intelligence, Springer, Berlin/Heidelberg, 2006, pp. 161-185.
34. R. Shah, P. Reed, Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems, European Journal of Operational Research 211 (3) (2011) 466-479.
35. D.J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures, 4th ed., CRC Press, 2007.
36. R. Stahlbock, S. Voß, Operations research at container terminals: a literature update, OR Spectrum 30 (2008) 1-52.
37. D. Steenken, S. Voß, R. Stahlbock, Container terminal operation and operations research - a classification and literature review, OR Spectrum 26 (2004) 3-49. (Pubitemid 40549578)
38. E.-G. Talbi, Metaheuristics: From Design to Implementation, John Wiley & Sons, 2009.
39. I.F.A. Vis, R. de Koster, Transshipment of containers at a container terminal: an overview, European Journal of Operational Research 147 (1) (2003) 1-16.
40. J. Xu, S.Y. Chiu, F. Glover, Fine-tuning a Tabu search algorithm with statistical tests, International Transactions in Operational Research 5 (3) (1998) 233-244.
41. B. Yuan, M. Orlowska, S. Sadiq, Extending a class of continuous estimation of distribution algorithms to dynamic problems, Optimization Letters 2 (2008) 433-443. (Pubitemid 351653794)
42. Q. Zhang, J. Sun, E. Tsang, Combinations of estimation of distribution algorithms and other techniques, International Journal of Automation and Computing 4 (2007) 273-280. (Pubitemid 47167354)
43. Q. Zhang, J.g Sun, E. Tsang, J. Ford, Hybrid estimation of distribution algorithm for global optimisation, Engineering Computations 21 (1) (2003) 91-107.
44. Q. Zhang, J. Sun, E. Tsang, J. Ford, Estimation of distribution algorithm with 2-opt local search for the quadratic assignment problem., in: J. Lozano, P. Larranaga, I. Inza, E. Bengoetxea (Eds.), Towards a New Evolutionary Computation, Volume 192 of Studies in Fuzziness and Soft Computing, Springer, Berlin/Heidelberg, 2006, pp. 281-292. (Pubitemid 44856945)