Job Scheduling Problem in a Flow Shop System with Simulated Hardening Algorithm

Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

A solution to the problem of job scheduling in a flow shop system is proposed in this paper. A three-machine flow shop system is presented with 10 sets of 10 random jobs to be processed. A new approach, called Simulated Hardening (SH), is used to schedule the jobs by taking into consideration two criteria: minimal makespan and maximal profit. The results obtained are compared with First In First Out (FIFO), Earliest Due Date (EDD), Shortest Processing Time (SPT), Longest Processing Time (LPT), Time Reserve (TR), and Descending Delay Penalty (DDP) sorting methods and confronted with the results of exhaustive search. The usefulness of the new SH algorithm is estimated in terms of the quality of the results obtained.

Keywords

Simulated hardening Scheduling Flow shop Makespan Profit 

References

  1. 1.
    Emmons, H., Vairaktarakis, G.: Flow Shop Scheduling. Springer, New York, US (2013)Google Scholar
  2. 2.
    Knosala, R, Wal, T.: A production scheduling problem using genetic algorithm. J. Mat. Process. Technol. 109(1–2), 90-95 (2001) (Elsevier Science SA)Google Scholar
  3. 3.
    Pugazhenthi, R., Xavior, M.A.: A Heuristic Toward Minimizing Waiting Time of Critical Jobs in a Flow Shop. In: Lecture Notes on Mechanical Engineering. Emerging Trends in Science, Engineering and Technology, pp. 343–350. Springer, India (2012)Google Scholar
  4. 4.
    Laha, D, Chakraborty, U.K.: An efficient hybrid heuristic for make span minimization in permutation flow shop scheduling. Int. J. Adv. Manuf. Technol. 44, 555–569 (2009) (Springer-Verlag)Google Scholar
  5. 5.
    Xiaoping, L., Long, C., Haiyan, X., Gupta, J.N.D.: Trajectory scheduling methods for minimizing total tardiness in a flowshop. Oper. Res. Perspect. 2, 13–23 (2015) (Elsevier B.V)Google Scholar
  6. 6.
    Chapadosa, N., Joliveaub, M., L’Ecuyera, P., Rousseau, L.-M. Retail store scheduling for profit. Eur. J. Oper. Res. 239(3), 609–624 (2014) (Elsevier B.V)Google Scholar
  7. 7.
    Qi, X.: Production scheduling with subcontracting: the subcontractor’s pricing game. J. Sched. 15(6), 773–781 (2012) (Springer, USA)Google Scholar
  8. 8.
    Sereshti, N., Bijari, M.: Profit maximization in simultaneous lot-sizing and scheduling problem. Appl. Math. Model. 37(23), 9516–9523 (2013) (Elsevier B.V)Google Scholar
  9. 9.
    Dylewski, R., Jardzioch, A., Krebs, I.: the optimal sequence of production orders, taking into account the cost of delays. Manage. Prod. Eng. Rev. 7(2), 21–28 (2016) (Production Engineering Committee of the Polish Academy of Sciences)Google Scholar
  10. 10.
    Wang, X., Xie, X., Cheng, T.C.E.: A modified artificial bee colony algorithm for order acceptance in two-machine flow shops. Int. J. Prod. Econ. 141(1), 14–23 (2013) (Elsevier B.V)Google Scholar
  11. 11.
    Jardzioch, A., Skobiej, B.: Analysis of variable changeover times impact on the revenue in manufacturing process. Acad. J. Manuf. Eng. 11(4), 114–117 (2013) (Editura Politehnica)Google Scholar
  12. 12.
    Jardzioch, A., Skobiej, B.: Simulated hardening algorithm application in advanced planning and scheduling. Entrepreneurship Manage. XVII(12), part II, 105–117 (2016) (Wydawnictwo Spolecznej Akademii Nauk)Google Scholar
  13. 13.
    Dylewski, R., Jardzioch, A.: Scheduling production orders, taking into account delays and waste. Manage. Prod. Eng. Rev. 5(3), 3–8 (2014) (Production Engineering Committee of the Polish Academy of Sciences)Google Scholar
  14. 14.
    Mallak, S.K., Ishak, M.B., Kasim, M.R.M., Abu Samah, M.A.: Assessing the effectiveness of waste minimization methods in solid waste reduction at the source by manufacturing firms in Malaysia. Polish J. Environ. Stud. 24(5), 2063–2071 (2015) (Hard Publishing)Google Scholar
  15. 15.
    Cantore, N.: Factors affecting the adoption of energy efficiency in the manufacturing sector of developing countries. Energy Effi. 10(3), 743–752 (2017) (Springer)Google Scholar
  16. 16.
    Terelak-Tymczyna, A., Miadlicki, K., Nowak, M.: The energy efficiency of the machining process on the example of rolling, (in Polish). Mechanik, No. 10/2016, pp 1307–1308, Redakcja Mechanik (2016)Google Scholar
  17. 17.
    Starzynska, B., Hamrol, A.: Excellence toolbox: decision support system for quality tools and techniques selection and application. Total Qual. Manage. Bus. Excellence 24(5–6), 577–595 (2013) (Taylor & Francis Ltd)Google Scholar
  18. 18.
    Meng, K., Lou, P.H., Pengm, X.H., Prybutok, V.: Multi-objective optimization decision-making of quality dependent product recovery for sustainability. Int. J. Prod. Econ. 188, 72–85 (2017) (Elsevier Science BV)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.West Pomeranian University of TechnologySzczecinPoland

Personalised recommendations