An Efficient Jaya Algorithm for Multi-objective Permutation Flow Shop Scheduling Problem

  • Aseem Kumar MishraEmail author
  • Divya Shrivastava
  • Bhasker Bundela
  • Surabhi Sircar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)


The Jaya algorithm is a novel, simple, and efficient meta-heuristic optimization technique and has received a successful application in the various fields of engineering and sciences. In the present paper, we apply the Jaya algorithm to permutation flow shop scheduling problem (PFSP) with the multi-objective of minimization of maximum completion time (makespan) and tardiness cost under due date constraints. PFSP is a well-known NP-hard and discrete combinatorial optimization problem. Firstly, to retrieve a job sequence, a random preference is allocated to each job in a permutation schedule. Secondly, a job preference vector is transformed into a job permutation vector by means of largest order value (LOV) rule. To deal with the multi-objective criteria, we apply a multi-attribute model (MAM) based on Apriori approach. The correctness of the Jaya algorithm is verified by comparing the results with the total enumeration method and simulated annealing (SA) algorithm. Computational results reveal that the proposed optimization technique is well efficient in solving multi-objective discrete combinatorial optimization problems such as the flow shop scheduling problem in the present study.


Jaya algorithm Permutation flow shop Multi-objective Multi-attribute model 


  1. 1.
    Rinnooy Kan, A.H.G.: Machine scheduling problems : classification, complexity and computations. Nijhoff (1976)Google Scholar
  2. 2.
    Arora, D., Agarwal, G.: Meta-heuristic approaches for flowshop scheduling problems: a review. Int. J. Adv. Oper. Manag. 8, 1 (2016). Scholar
  3. 3.
    Yenisey, M.M., Yagmahan, B.: Multi-objective permutation flow shop scheduling problem: literature review, classification and current trends. Omega (United Kingdom) 45, 119–135 (2014). Scholar
  4. 4.
    Yagmahan, B., Yenisey, M.M.: A multi-objective ant colony system algorithm for flow shop scheduling problem. Expert Syst. Appl. 37, 1361–1368 (2010). Scholar
  5. 5.
    Minella, G., Ruiz, R., Ciavotta, M.: Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems. Comput. Oper. Res. 38, 1521–1533 (2011). Scholar
  6. 6.
    Sun, Y., Zhang, C., Gao, L., Wang, X.: Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospects. Int. J. Adv. Manuf. Technol. 55, 723–739 (2011). Scholar
  7. 7.
    Venkata, Rao R.: Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7, 19–34 (2016). Scholar
  8. 8.
    Rao, R.V., Saroj, A.: Economic optimization of shell-and-tube heat exchanger using Jaya algorithm with maintenance consideration. Appl. Therm. Eng. 116, 473–487 (2017). Scholar
  9. 9.
    Rao, R.V., More, K.C., Taler, J., Ocłoń, P.: Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl. Therm. Eng. 103, 572–582 (2016). Scholar
  10. 10.
    Rao, R.V., Rai, D.P., Balic, J.: A multi-objective algorithm for optimization of modern machining processes. Eng. Appl. Artif. Intell. 61, 103–125 (2017). Scholar
  11. 11.
    Radhika, S., Ch, S.R., Krishna, N., Karteeka Pavan, K.: Multi-objective optimization of master production scheduling problems using Jaya algorithm 1729–32 (2016)Google Scholar
  12. 12.
    Rao, R.V., Saroj, A.: Multi-objective design optimization of heat exchangers using elitist-Jaya algorithm. Energy Syst (2016). Scholar
  13. 13.
    Buddala, R., Mahapatra, S.S.: Improved teaching–learning-based and Jaya optimization algorithms for solving flexible flow shop scheduling problems. J. Ind. Eng. Int. 1–16 (2017). Scholar
  14. 14.
    Gao, K., Sadollah, A., Zhang, Y., Su, R.: Discrete Jaya algorithm for flexible job shop scheduling problem with new job insertion. In: 14th International Conference on Control Automation Robot Vis 13–5 (2016)Google Scholar
  15. 15.
    Xia, T., Xi, L., Zhou, X., Lee, J.: Dynamic maintenance decision-making for series-parallel manufacturing system based on MAM-MTW methodology. Eur. J. Oper. Res. 221, 231–240 (2012). Scholar
  16. 16.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science (New York,) 220(4598), 671–680 (1983)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Aseem Kumar Mishra
    • 1
    Email author
  • Divya Shrivastava
    • 1
  • Bhasker Bundela
    • 1
  • Surabhi Sircar
    • 1
  1. 1.Department of Mechanical EngineeringShiv Nadar UniversityDadriIndia

Personalised recommendations