Abstract
This paper presents a comparative study of two metaheuristic optimization algorithms for solving the Total Weighted Tardiness problem in its single-machine mode, which represents the most frequent scheduling and sorting problems occurring in the industrial environment. The metaheuristics evaluated in this study were Tabu Search, and Simulated Annealing, because they have shown satisfactory results in this kind of problems. The performance of each algorithm was evaluated by means of the total tardiness and the execution time using instances of 40, 50, and 100 jobs extracted from the OR-Library. The Simulated Annealing algorithm was found to be the most efficient method, being the one that found the best solutions in comparison to Tabu Search, nonetheless, Tabu Search found the results in the shortest time. A difference approximated of twenty-four units between Simulated Annealing and Tabu search was found in the total tardiness value when a set of 125 instances were executed. On the other hand, Tabu Search required only the 13% of the time execution required by Simulated Annealing.
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
Similar content being viewed by others
References
Uchronski, M.: Parallel algorithm with blocks for a single-machine total weighted tardiness scheduling problem. Appl. Sci. 11, 2069 (2021). https://doi.org/10.3390/app11052069
Gupta, S., Kumari, R., Singh, R.P.: Lunar cycle inspired PSO for single machine total weighted tardiness scheduling problem. Evol. Intell. 14(3), 1355–1366 (2021). https://doi.org/10.1007/s12065-020-00556-9
Zhang, J., Ding, G., Zou, Y., Qin, S., Fu, J.: Review of job shop scheduling research and its new perspectives under Industry 4.0. J. Intell. Manuf. 30(4), 1809–1830 (2017). https://doi.org/10.1007/s10845-017-1350-2
Jianzhong, D., Leung, J.: Minimizing total tardiness on one machine is np-hard. Math. Oper. Res. 35, 483–495 (1990)
Jun, S., Lee, S.: Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction. Int. J. Prod. Res. (2020). https://doi.org/10.1080/00207543.2020.1741716
González, M., Vela, C.: An efficient memetic algorithm for total weighted tardiness minimization in a single machine with setups, Appl. Soft Comput. https://doi.org/10.1016/j.asoc.2015.07.050
Abitz1, D., Hartmann, T., Middendorf, M.: A weighted population update rule for PACO applied to the single machine total weighted tardiness problem. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 4–12, June 2020. https://doi.org/10.1145/3377930.3390182
Bilge, Ü., Kurtulan, M., Kırac, F.: A Tabu search algorithm for the single machine total weighted tardiness problem. Eur. J. Oper. Res. 176(3), 1423–1435 (2007)
Simon, D.: Evolutionary Optimization Algorithms. Wiley, New York (2013)
Beasley, J.E.: OR-library. http://people.brunel.ac.uk/~mastjjb/jeb/info.html. Accessed May 2022
Schaller, J., Valente, J.: A comparison of metaheuristic procedures to schedule jobs in a permutation flow shop to minimize total earliness and tardiness. Int. J. Prod. Res. 51(3), 772–779 (2013)
Boussaid, I., Lepagnot, J., Siarry, P.: A survey on optimization metaheuristics. Inf. Sci. 237, 82–117 (2013)
Lee, Y.H., Bhaskaran, K., Pinedo, M.: A heuristic to minimize the total weighted tardiness with sequence-dependent setups. IEEE Trans. 29(1), 45–52 (1997)
Eren, T., Güner, E.: Minimizing total tardiness in a scheduling problem with a learning effect. Appl. Math. Model. 31(7), 1351–1361 (2007)
Suescun, O., Yesid, B., Agudelo, R.A., Delgadillo, G., et al.: Minimización de la tardanza ponderada total en talleres de manufactura aplicando colonia de hormigas. Ingeniería 14(1), 25–30 (2009)
Zhang, R., Wu, C.: A Simulated annealing algorithm based on block properties for the job shop scheduling problem with total weighted tardiness objective. Comput. Oper. Res. 38(5), 854–867 (2011)
Li, D., Hsu, P., Chang, C., A genetic algorithm-based approach for single-machine scheduling with learning effect and release time. Math. Problems Eng. 2014 (2014). https://doi.org/10.1155/2014/249493
Nazif, H., Lee, L.S.: A genetic algorithm on single machine scheduling problem to minimize total weighted completion time. Eur. J. Sci. Res. 35(3), 44–452 (2009)
Bauer, A., Bullnheimer, B., Hartl, R.F., Strauss, C.: Minimizing total tardiness on a single machine using ant colony optimization. Cent. Eur. J. Oper. Res. 8(2), 125–141 (2000)
Bryant, R., Lakner, P., Pinedo, M.: On the optimality of the earliest due date rule in stochastic scheduling and in queueing. Eur. J. Oper. Res. 298(1), 202–212 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mexicano, A., Carmona-Frausto, J.C., Montes-Dorantes, P.N., Cervantes, S., Cervantes, JA., Rodríguez, R. (2023). Simulated Annealing and Tabu Search for Solving the Single Machine Scheduling Problem. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2022. Lecture Notes in Networks and Systems, vol 571. Springer, Cham. https://doi.org/10.1007/978-3-031-19945-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-19945-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19944-8
Online ISBN: 978-3-031-19945-5
eBook Packages: EngineeringEngineering (R0)