Abstract
SearchCol is a recently proposed approach hybridizing column generation, problem specific algorithms and distinct well known metaheuristics (VNS, Tabu Search, Simulated Annealing, etc.). SearchCol allows to solve several combinatorial optimization problems by applying column generation to a given decomposition model, and using one of the available metaheuristics to search for an integer solution combining the previously generated columns, which are components of the problem. A new evolutionary algorithm (EA) was proposed as the first population based metaheuristic included in SearchCol. This EA uses a representation of individuals based on the generated columns and has been used to obtain integer solutions for a new model for the Bus Drivers Rostering problem (BDRP). Special features of this EA include local search and elitism. This paper presents a computational study evaluating the new population based heuristic (EA) versus two single solution heuristics: VNS and Simulated Annealing, exploiting different configurations of the framework on a set of benchmark instances for the BDRP.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading (1984)
Talbi, E.G.: Metaheuristics: From Design to Implementation. Wiley, New York (2009)
Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, vol. 57, pp. 457–474. Springer, US (2003)
Puchinger, J., Raidl, G.R.: Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification. In: Mira, J., Álvarez, J.R. (eds.) First International Work-Conference on the Interplay Between Natural and Artificial Computation. Springer, Las Palmas, Spain (2005)
Dumitrescu, I., Stützle, T.: Combinations of local search and exact algorithms. In: Cagnoni, S., Johnson, C., Cardalda, J.R., Marchiori, E., Corne, D., Meyer, J.-A., Gottlieb, J., Middendorf, M., Guillot, A., Raidl, G., Hart, E. (eds.) Applications of Evolutionary Computing, vol. 2611, pp. 211–223. Springer, Berlin (2003)
Dumitrescu, I., Stützle, T.: Usage of exact algorithms to enhance stochastic local search algorithms. In: Maniezzo, V., Stützle, T., Voß, S. (eds.) Matheuristics, vol. 10, pp. 103–134. Springer, US (2010)
Alvelos, F., de Sousa, A., Santos, D.: SearchCol: metaheuristic search by column generation. In: Blesa, M., Blum, C., Raidl, G., Roli, A., Sampels, M. (eds.) Hybrid Metaheuristics, vol. 6373, pp. 190–205. Springer, Berlin/Heidelberg (2010)
Desaulniers, G., Desrosiers, J., Solomon, M.M.: Column Generation. Springer, New York (2005)
Barbosa, V., Respício, A., Alvelos, F.: A hybrid metaheuristic for the bus driver rostering problem. In: Vitoriano, B., Valente, F. (eds.) ICORES 2013—2nd International Conference on Operations Research and Enterprise Systems, pp. 32–42. SCITEPRESS, Barcelona (2013)
Barbosa, V., Respício, A., Alvelos, F.: Genetic algorithms for the SearchCol ++ framework: application to drivers’ rostering. In: Oliveira, J.F., Vaz, C.B., Pereira, A.I. (eds.) IO2013—XVI Congresso da Associação Portuguesa de Investigação Operacional, pp. 38–47. Instituto Politécnico de Bragança, Bragança (2013)
Ernst, A.T., Jiang, H., Krishnamoorthy, M., Sier, D.: Staff scheduling and rostering: a review of applications, methods and models. Eur. J. Oper. Res. 153, 3–27 (2004)
Ernst, A.T., Jiang, H., Krishnamoorthy, M., Owens, B., Sier, D.: An annotated bibliography of personnel scheduling and rostering. Ann. Oper. Res. 127, 21–144 (2004)
Van den Bergh, J., Beliën, J., De Bruecker, P., Demeulemeester, E., De Boeck, L.: Personnel scheduling: a literature review. Eur. J. Oper. Res. 226, 367–385 (2013)
Moz, M., Respício, A., Pato, M.: Bi-objective evolutionary heuristics for bus driver rostering. Public Transport 1, 189–210 (2009)
Dorne, R.: Personnel shift scheduling and rostering. In: Voudouris, C., Lesaint, D., Owusu, G. (eds.) Service Chain Management, pp. 125–138. Springer, Berlin Heidelberg (2008)
Ruibin, B., Burke, E.K., Kendall, G., Jingpeng, L., McCollum, B.: A hybrid evolutionary approach to the nurse rostering problem. IEEE Trans. Evol. Comput. 14, 580–590 (2010)
Respício, A., Moz, M., Vaz Pato, M.: Enhanced genetic algorithms for a bi-objective bus driver rostering problem: a computational study. Int. Trans. Oper. Res. 20, 443–470 (2013)
Alvelos, F., Sousa, A., Santos, D.: Combining column generation and metaheuristics. In: Talbi, E.-G. (ed.) Hybrid Metaheuristics, vol. 434, pp. 285–334. Springer (2013)
Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? J. Comput. Syst. Sci. 37, 79–100 (1988)
Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24, 1097–1100 (1997)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Barbosa, V., Respício, A., Alvelos, F. (2015). Comparing Hybrid Metaheuristics for the Bus Driver Rostering Problem. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-19857-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19856-9
Online ISBN: 978-3-319-19857-6
eBook Packages: EngineeringEngineering (R0)