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Robustness of Schedules Obtained Using the Tabu Search Algorithm Based on the Average Slack Method

  • Iwona Paprocka
  • Aleksander GwiazdaEmail author
  • Magdalena Bączkowicz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)

Abstract

One of the most important problems consider with the scheduling process is to ensure the needed level of robustness of obtained schedules. One of possible tools that could be used to realize this objective is the Taboo Search Algorithm (TSA). The Average Slack Method (ASM) enables to obtain the best performance of the job shop system. In the paper is presented analysis of two objectives: to achieve the best compromise basic schedule for four efficiency measures as well as to achieve the best compromise reactive schedule. It was investigated of 15 processes executed on 10 machines. It was shown that ASM enables the obtainment of the best performance of the job shop system.

Keywords

Scheduling Tabu Search Algorithm (TSA) Average Slack Method (ASM) 

References

  1. 1.
    Abumaizar, R.J., Svestka, J.A.: Rescheduling job shops under random disruptions. Int. J. Prod. Res. 35, 2065–2082 (1997)CrossRefGoogle Scholar
  2. 2.
    Al-Hinai, N., ElMekkawy, T.Y.: Robust and flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm. Int. J. Prod. Econ. 132, 279–291 (2011)CrossRefGoogle Scholar
  3. 3.
    Hamzadayi, A., Yildiz, G.: Event driven strategy based complete rescheduling approaches for dynamic m identical parallel machines scheduling problem with a common server. Comput. Ind. Eng. 91, 66–84 (2016)CrossRefGoogle Scholar
  4. 4.
    Banaś, W., Sękala, A., Foit, K., Gwiazda, A., Hryniewicz, P., Kost, G.: The modular design of robotic workcells in a flexible production line. In: IOP Conference Series: Materials Science and Engineering, vol. 95, p. 012099 (2015)CrossRefGoogle Scholar
  5. 5.
    Banaś, W., Sękala, A., Gwiazda, A., Foit, K., Hryniewicz, P., Kost, G.: Determination of the robot location in a workcell of a flexible production line. In: IOP Conference Series: Materials Science and Engineering, vol. 95, p. 012105 (2015)CrossRefGoogle Scholar
  6. 6.
    Chen, J., Chung, C.-H.: An examination of flexibility measurements and performance of flexible manufacturing systems. Int. J. Prod. Res. 34, 379–394 (1996)CrossRefGoogle Scholar
  7. 7.
    Cheng, R., Gen, M., Tsujimura, Y.: A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: hybrid genetic search strategies. Comput. Ind. Eng. 36, 343–346 (1999)CrossRefGoogle Scholar
  8. 8.
    Chong, C.S., Sivakumar, A.I., Gay, R.: Simulation-based scheduling for dynamic discrete manufacturing. In: Proceedings of the 2003 Winter Simulation Conference, pp. 1465–1473 (2003)Google Scholar
  9. 9.
    Church, L.K., Uzsoy, R.: Analysis of periodic and event-driven rescheduling policies in dynamic shops. Int. J. Comput. Integr. Manuf. 5, 153–163 (1992)CrossRefGoogle Scholar
  10. 10.
    Duenas, A., Petrovic, D.: An approach to predictive-reactive scheduling of parallel machines subject to disruptions. Ann. Oper. Res. 159, 65–82 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Pan, E., Liao, W., Xi, L.: A joint model of production scheduling and predictive maintenance for minimizing job tardiness. Int. J. Adv. Manuf. Technol. 60, 1049–1061 (2012)CrossRefGoogle Scholar
  12. 12.
    Goren, S., Sabuncuoglu, I.: Robustness and stability measures for scheduling: single-machine environment. IIE Trans. 40, 66–83 (2008)CrossRefGoogle Scholar
  13. 13.
    Vieira, G.V., Herrmann, J.W., Lin, E.: Rescheduling manufacturing systems: a framework of strategies, policies, and methods. J. Sched. 6(1), 35–58 (2003)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Heng, L., Zhicheng, L., Ling, X.L., Bin, H.: A production rescheduling expert simulation system. Eur. J. Oper. Res. 124, 283–293 (2000)CrossRefGoogle Scholar
  15. 15.
    Jain, A.K., Elmaraghy, H.A.: Production scheduling/rescheduling in flexible manufacturing. Int. J. Prod. Res. 35, 28–309 (1997)CrossRefGoogle Scholar
  16. 16.
    Hasan, S.M.K., Sarker, R., Essam, D.: Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns. Int. J. Prod. Res. 49(16), 4999–5015 (2011)CrossRefGoogle Scholar
  17. 17.
    Zhang, L., Gao, L., Li, X.: A hybrid genetic algorithm and tabu search for multi-objective dynamic job shop scheduling problem. Int. J. Prod. Res. 51(12), 3516–3531 (2013)CrossRefGoogle Scholar
  18. 18.
    Liu, L., Han-yu, G., Yu-geng, X.: Robust and stable scheduling of a single machine with random machine breakdowns. Int. J. Adv. Manuf. Technol. 31, 645–656 (2007)CrossRefGoogle Scholar
  19. 19.
    Mattfeld, D.C., Bierwirth, C.: An efficient genetic algorithm for job shop scheduling with tardiness objectives. Eur. J. Oper. Res. 155, 616–630 (2004)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Matsuura, H., Tsubone, H., Kanezashi, M.: Sequencing, dispatching and switching in a dynamic manufacturing environment. Int. J. Prod. Res. 37(7), 1671–1688 (1993)CrossRefGoogle Scholar
  21. 21.
    Monica, Z.: Optimization of the production process using virtual model of a workspace. In: IOP Conference Series: Materials Science and Engineering, vol. 95, p. 012102 (2015)CrossRefGoogle Scholar
  22. 22.
    Paprocka, I., Kempa, W.M., Kalinowski, K., Grabowik, C.: A production scheduling model with maintenance. Adv. Mater. Res. 1036, 885–890 (2014)CrossRefGoogle Scholar
  23. 23.
    Paprocka, I., Kempa, W., Kalinowski, K., et al.: Estimation of overall equipment effectiveness using simulation programme. Mater. Sci. Eng. 95 (2015). Article Number: 012155Google Scholar
  24. 24.
    Paprocka, I., Kempa, W.M., Grabowik, C., Kalinowski, K.: Sensitivity analysis of predictive scheduling algorithms. Adv. Mater. Res. 2014, 921–926 (1036)Google Scholar
  25. 25.
    Fahmy, S.A., Balakrishnan, S., ElMekkawy, T.Y.: A generic deadlock-free reactive scheduling approach. Int. J. Prod. Res. 47(20), 5657–5676 (2009)CrossRefGoogle Scholar
  26. 26.
    Suresh, V., Chudhari, D.: Dynamic scheduling - a survey of research. Int. J. Prod. Econ. 32(1), 53–63 (1993)CrossRefGoogle Scholar
  27. 27.
    Turkcan, A., Akturk, M.S., Storer, R.H.: Predictive/reactive scheduling with controllable processing times and earliness-tardiness penalties. IIE Trans. 41, 1080–1095 (2009)CrossRefGoogle Scholar
  28. 28.
    Vieira, G.E., Herrmann, J.W., Lin, E.: Predicting the performance of rescheduling strategies for parallel machine systems. J. Manuf. Syst. 19(4), 256–266 (2000)CrossRefGoogle Scholar

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Authors and Affiliations

  • Iwona Paprocka
    • 1
  • Aleksander Gwiazda
    • 1
    Email author
  • Magdalena Bączkowicz
    • 1
  1. 1.Faculty of Mechanical EngineeringSilesian University of TechnologyGliwicePoland

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