Abstract
We consider the job shop scheduling problem with fuzzy sets modelling uncertain durations and flexible due dates. With the goal of maximising due-date satisfaction, we propose a memetic algorithm that combines intensification and diversification by integrating local search in a genetic algorithm. Experimental results illustrate the synergy between both components of the algorithm as well as its potential to provide good solutions.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abdullah, S., Abdolrazzagh-Nezhad, M.: Fuzzy job-shop scheduling problems: a review. Inf. Sci. 278, 380–407 (2014)
Bierwirth, C.: A generalized permutation approach to job shop scheduling with genetic algorithms. OR Spectr. 17, 87–92 (1995)
Błażewicz, J., Domschke, W., Pesch, E.: The job shop scheduling problem: conventional and new solution techniques. Eur. J. Oper. Res. 93, 1–33 (1996)
Dubois, D., Fargier, H., Fortemps, P.: Fuzzy scheduling: modelling flexible constraints vs. coping with incomplete knowledge. Eur. J. Oper. Res. 147, 231–252 (2003)
González-Rodríguez, I., Puente, J., Vela, C.R.: A multiobjective approach to fuzzy job shop problem using genetic algorithms. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds.) CAEPIA 2007. LNCS (LNAI), vol. 4788, pp. 80–89. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75271-4_9
González Rodríguez, I., Vela, C.R., Puente, J., Varela, R.: A new local search for the job shop problem with uncertain durations. In: Proceedings of the ICAPS-2008, pp. 124–131. AAAI Press (2008)
Graham, R., Lawler, E., Lenstra, J., Kan, A.R.: Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discret. Math. 4, 287–326 (1979)
Lei, D.: Solving fuzzy job shop scheduling problems using random key genetic algorithm. Int. J. Adv. Manuf. Technol. 49, 253–262 (2010)
Ono, I., Yamamura, M., Kobayashi, S.: A genetic algorithm for job-shop scheduling problems using job-based order crossover. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 547–552. IEEE (1996)
Palacios, J.J., Derbel, B.: On maintaining diversity in MOEA/D: application to a biobjective combinatorial FJSP. In: Proceedings of GECCO 2015, pp. 719–726. ACM (2015)
Palacios, J.J., González, M.A., Vela, C.R., González-Rodríguez, I., Puente, J.: Genetic tabu search for the fuzzy flexible job shop problem. Comput. Oper. Res. 54, 74–89 (2015)
Palacios, J.J., Puente, J., Vela, C.R., González-Rodríguez, I.: Benchmarks for fuzzy job shop problems. Inf. Sci. 329, 736–752 (2016)
Palacios, J.J., Vela, C.R., González-Rodríguez, I., Puente, J.: Schedule generation schemes for job shop problems with fuzziness. In: Schaub, T., Friedrich, G., O’Sullivan, B. (eds.) Proceedings of ECAI 2014, pp. 687–692. IOS Press, Amsterdam (2014)
Sakawa, M., Kubota, R.: Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms. Eur. J. Oper. Res. 120, 393–407 (2000)
Sakawa, M., Mori, T.: An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate. Comput. Ind. Eng. 36, 325–341 (1999)
Acknowledgements
This research has been supported by the Spanish Government under research grant TIN2016-79190-R.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Palacios, J.J., Vela, C.R., González-Rodríguez, I., Puente, J. (2017). A Memetic Algorithm for Due-Date Satisfaction in Fuzzy Job Shop Scheduling. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-59740-9_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59739-3
Online ISBN: 978-3-319-59740-9
eBook Packages: Computer ScienceComputer Science (R0)