Abstract
Multi-start local search heuristics Remove and Reinsert that is based on a simple schedule constructing heuristics is tested on several benchmark instances of the job shop scheduling problem. The heuristics provides very good near optimal solutions within reasonably short computation time. The implementation within a plant simulation software is compared to the build-in genetic algorithm.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ylipää, T.: Correction, prevention and elimination of production disturbances. PROPER project description, Department of Product and Production Development (PPD), Chalmers University of Technology, Gothenhurg (2002)
Debevec, M., Simic, M., Herakovic, N.: Virtual factory as an advanced approach for production process optimization. Int. J. Simul. Model. 13(1), 66–78 (2014)
Herakovic, N., Metlikovic, P., Debevec, M.: Motivational lean game to support decision between push and pull production strategy. Int. J. Simul. Model. 13(4), 433–446 (2014)
Pindeo, M.L.: Scheduling: Theory, Algorithms, and Systems. Springer New York Dordrecht Heidelberg London, Springer Science + Business Media, LLC (2012)
Naderi, B., Fatemi Ghomi, S.M.T., Aminnayeri, M.: A high performing metaheuristic for job shop scheduling with sequence-dependent setup times. Appl. Soft Comput. 10, 703–710 (2010)
Ombuki, B., Ventresca, M.: Local search genetic algorithms for the job shop scheduling problem. Appl. Intell. 21, 99–109 (2004)
Eskandari, H., Rahaee, M.A., Memarpour, M., Hasannayebi, E., Malek, S.A.: Evaluation of different berthing scenarios in Shahid Rajaee container terminal using discrete-event simulation. In: Simulation Conference (WSC), Winter (2013)
Zerovnik, J.: A heuristics for the probabilistic traveling salesman problem. In: Rupnik, V. Bogataj, M. (eds.) Proceedings of the International Symposium on Operational Research (SOR’95), pp. 165–172, Slovenian Society Informatika, Ljubljana (1995)
Zerovnik, J.: Heuristics for NP-hard optimization problem: simpler is better! Logistic Sustain. Transp. 6(1), 1–10 (2015)
Mladenović, N., Hansen, P. and Brimberg, J.: Sequential clustering with radius and split criteria. Cent. Eur. J. Oper. Res. 21 (Supplement-1): 95–115 (2013)
Hansen, P., Mladenović, N.: Variable neighbourhood search methods. In: Encyclopedia of Optimization, pp. 3975–3989 (2009)
Tecnomatix Plant Simulation, Siemens PLM Software. http://www.emplant.de/english/fact%20sheet%20plant%20simulation.pdf. Accessed on 26 June 2017
Lawrence, S.: Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement). Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, Pennsylvania (1984)
Fisher, H. Thompson, G.L.: Probabilistic learning combinations of local job-shop scheduling rules. In: Muth, J.F., Thompson, G.L. (eds.) Industrial Scheduling, pp. 225–251. Prentice Hall, Englewood Cliffs, New Jersey (1963)
Brest, J., Zerovnik, J.: An approximation algorithm for the asymmetric traveling salesman problem. Ric. Operativa 28, 59–67 (1999)
Acknowledgements
This research work has been funded by the GOSTOP program, contract no. C3330-16-529000, co-financed by Slovenia and the EU under ERDF. The second author was supported in part by ARRS, the Research Agency of Slovenia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Zupan, H., Žerovnik, J., Herakovič, N. (2018). Local Search with Discrete Event Simulation for the Job Shop Scheduling Problem. In: Borangiu, T., Trentesaux, D., Thomas, A., Cardin, O. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing. Studies in Computational Intelligence, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-73751-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-73751-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73750-8
Online ISBN: 978-3-319-73751-5
eBook Packages: EngineeringEngineering (R0)