Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

Job-shop scheduling with machine breakdown prediction under completion time constraint

, ,

DOI: http://dx.doi.org/10.15439/2018F83

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 437440 ()

Full text

Abstract. This paper discusses the problem of time-constrained job-shop scheduling with technological machine breakdown prediction. The first section gives short charac-teristics of the field of research, and describes the effects of ma-chine failure on job completion times. Secondly, the discussed problem is represented by means of mathematical equations and solved with original algorithms for machine failure prediction and implementation of redundant service times, and finally, the proposed solutions are verified by means of simulation. In the computational experiment stage a typical production case with taking into account 3 machines failure was considered. The last part of this paper draws conclusions from the study and presents directions of future research work.


  1. M. T. Jensen, “Robust and Flexible Scheduling with Evolutionary Computation,” Aarhus, 2001.
  2. Ł. Sobaszek, A. Gola, A. Świć, “Predictive scheduling as a part of intelligent job scheduling system,” in Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017: proceedings of the First International Conference on Intelligent Systems in Production Engineering and Maintenance ISPEM 2017, D. Mazurkiewicz, A. Burduk, Ed. Switzerland, 2018, pp. 358–367.
  3. J. Louis, Zhijie Xu, “Genetic Algorithms for Open Shop Scheduling and Re-Scheduling,” Departament of Computer Science, University of Nevada, 1999.
  4. In-Chan Choi, Dae-Sik Choi, “A Local Search Algorithm for Jobshop Scheduling Problems with Alternative Operations and Sequence-Dependent Setups,” Computers & Industrial Engineering, 42 (2002), pp. 43–58.
  5. P. Sharma, A. Jain, “Performance analysis of dispatching rules in a stochastic dynamic job shop manufacturing system with sequence-dependent setup times: Simulation approach,” CIRP Journal of Manufacturing Science and Technology, Vol. 10, 2015, pp. 110–119.
  6. M. Pawlak, “Algorytmy ewolucyjne jako narzędzie harmonogramowania produkcji,” Wydawnictwo Naukowe PWN, Warszawa, 1999.
  7. Yu. N. Sotskov, N. Yu. Sotskova, Lai T.-C., F. Werner, “Scheduling under Uncertainty – Theory and Algorithms,” Belorusskaya nauka, Minsk, 2010.
  8. J. S. Norwood, “An evaluation of the time constrained and resource constrained scheduling features of commercially available project management software,” Naval Postgraduate School, Monterey, California, 1996.
  9. M. Klimek, “Priority algorithms for the problem of financial optimisation of a multi stage project,” Applied Computer Science, vol. 13, no. 4, 2017, pp. 20–34.
  10. P. Pekczynski, “Scheduling Constraints,” Department of Computer Science, Saarland University, Germany, 2005.
  11. S. Van de Vonder, E. Demeulemeester, W. Herroelen, “Proactive Heuristic Procedures for Robust Project Scheduling: An Experimental Analysis,” European Journal of Operational Research, 189 (2008), pp. 723–733.
  12. Hung-Kai Wang, Chen-Fu Chien, Che-Wei Chou, “An empirical study of bio manufacturing for the scheduling of hepatitis in vitro diagnostic device with constrained process time window,” Computers & Industrial Engineering, Volume 114, 2017, pp. 31–44.
  13. A. M. Aguirre, L. G. Papageorgiou, “Resource-constrained formulation for production scheduling and maintenance,” in Computer Aided Chemical Engineering, A. Espuña, M. Graells, L. Puigjaner, Ed. Elsevier, vol. 40, 2017, pp. 1375–1380.
  14. E. Gebennini, L. Zeppetella, A. Grassi, B. Rimini, “Production scheduling to optimize the product assortment in case of constrained capacity and customer-driven substitution,” IFAC – PapersOnLine, vol. 48(3), 2015, pp. 1954–1959.
  15. Ł. Sobaszek, A. Gola, E. Kozłowski, “Application of survival function in robust scheduling of production jobs,” 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, 2017, pp. 575–578.
  16. Jian Xiong, Li-ning Xing, Ying-wu Chen, “Robust Scheduling for Multi-Objective Flexible Job-Shop Problems with Random Machine Breakdowns,” International Journal of Production Economics, vol. 141(1), 2013, pp. 112–126.
  17. M. T. Jensen, T. K. Hansen, “Robust solutions to Job Shop problems,” The 1999 Congress on Evolutionary Computation, July 1999, pp. 1138–1144.