Skip to main content
SpringerLink
Log in

Distributed permutation flowshop scheduling problem with total completion time objective

  • Application Article
  • Published:
OPSEARCH Aims and scope Submit manuscript

Abstract

This paper considers the distributed permutation flowshop scheduling problem (DPFSP) which is an extension of permutation flowshop scheduling problem (PFSP). In DPFSP, there are multiple parallel factories instead of one factory as in PFSP. Each factory consists of same number of machines, and jobs can be processed in either of the factories to perform all necessary operations. This paper considers DPFSP for minimizing the total completion time objective. An MILP formulation is developed to find the optimal solution. To solve the problem, a metaheuristic, tabu search (TS) is proposed. Numerical experiments are performed on benchmark problem instances from the literature, and results of the proposed method are compared with current metaheuristics in the literature for this problem. The tabu search outperforms all existing metaheuristics in terms of solution quality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Bargaoui, H., Belkahla Driss, O., Ghédira, K.: A novel chemical reaction optimization for the distributed permutation flowshop scheduling problem with makespan criterion. Comput. Ind. Eng. 111, 239–250 (2017)

    Article  Google Scholar 

  2. Chan, F.T.S., Chung, S.H., Chan, P.L.Y.: An adaptive genetic algorithm with dominated genes for distributed scheduling problems. Expert. Syst. Appl. 29(2), 364–371 (2005)

    Article  Google Scholar 

  3. Chan, H.K., Chung, S.H.: Optimisation approaches for distributed scheduling problems. Int. J. Prod. Res. 51(9), 2571–2577 (2013)

    Article  Google Scholar 

  4. De Giovanni, L., Pezzella, F.: An Improved Genetic algorithm for the distributed and flexible Job-shop scheduling problem. Eur. J. Oper. Res. 200(2), 395–408 (2010)

    Article  Google Scholar 

  5. Fan, S.K.S.: Quality improvement of chemical-mechanical wafer planarization process in semiconductor manufacturing using a combined generalized linear modelling - non-linear programming approach. Int. J. Prod. Res. 38(13), 3011–3029 (2000)

    Article  Google Scholar 

  6. Fernandez-Viagas, V., Framinan, J.M.: A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem. Int. J. Prod. Res. 53(4), 1111–1123 (2015)

    Article  Google Scholar 

  7. Fernandez-Viagas, V., Perez-Gonzalez, P., Framinan, J.M.: The distributed permutation flow shop to minimise the total flowtime. Comput. Ind. Eng. 118(March), 464–477 (2018)

    Article  Google Scholar 

  8. Gajpal, Y., Rajendran, C.: An ant-colony optimization algorithm for minimizing the completion-time variance of jobs in flowshops. Int. J. Prod. Econ. 101(2), 259–272 (2006)

    Article  Google Scholar 

  9. Gao, J., Chen, R., Deng, W., Liu, Y.: Solving multi-factory flowshop problems with a novel variable neighbourhood descent algorithm. J. Comput. Inf. Syst. 8(5), 2025–2032 (2012)

    Google Scholar 

  10. Gao, J., Chen, R., Deng, W.: An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem. Int. J. Prod. Res. 51(3), 641–651 (2013)

    Article  Google Scholar 

  11. Gao, J., Chen, R.: A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem. Int. J. Comput. Intell. Syst. 4(4), 497–508 (2011)

    Article  Google Scholar 

  12. Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1993)

    Article  Google Scholar 

  13. Gnoni, M.G., Iavagnilio, R., Mossa, G., Mummolo, G., Di Leva, A.: Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry. Int. J. Prod. Econ. 85(2), 251–262 (2003)

    Article  Google Scholar 

  14. Gupta, D., Sharma, S., Aggarwal, S.: Flow shop scheduling on 2-machines with setup time and single transport facility under fuzzy environment. Opsearch. 50(1), 14–24 (2013)

    Article  Google Scholar 

  15. Jia, H.Z., Fuh, J.Y.H., A.Y.C. Nee YFZ. : Web-based multi-functional scheduling system for a distributed manufacturing environment. Concur. Eng. 11(4), 249–265 (2003)

    Article  Google Scholar 

  16. Jia, H.Z., Nee, A.Y.C., Fuh, J.Y.H., Zhang, Y.F.: A modified genetic algorithm for distributed scheduling problems. J. Intell. Manuf. 14(3–4), 351–362 (2003)

    Article  Google Scholar 

  17. Johnson, S.M.: Optimal two-and three-stage production schedules with setup times included. NAV. RES. LOG. 1(1), 61–68 (1954)

    Article  Google Scholar 

  18. Kahn, K.B.: The PDMA handbook of new product development. Wiley, NewYork (2012)

    Book  Google Scholar 

  19. Komaki, G.M., Mobin, S., Teymourian, E., Sheikh, S.: A General Variable Neighborhood Search Algorithm to Minimize Makespan of the Distributed Permutation Flowshop Scheduling Problem. World. Acad. Sci. Eng. Technol. Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng. 9(8), 2582–2589 (2015)

    Google Scholar 

  20. Leung, S.C.H., Wu, Y., Lai, K.K.: Multi-site aggregate production planning with multiple objectives: A goal programming approach. Prod. Plan. Control. 14(5), 425–436 (2003)

    Article  Google Scholar 

  21. Li Y, Chen Z. 2015. The distributed permutation flowshop scheduling problem: A genetic algorithm approach. 2015 3rd International Conference on Mechatronics and Industrial Informatics (ICMII):381–384.

  22. Lin, S.W., Ying, K.C., Huang, C.Y.: Minimising makespan in distributed permutation flowshops using a modified iterated greedy algorithm. Int. J. Prod. Res. 51(16), 5029–5038 (2013)

    Article  Google Scholar 

  23. Lin, S.W., Ying, K.C.: Applying a hybrid simulated annealing and tabu search approach to non-permutation flowshop scheduling problems. Int. J. Prod. Res. 47(5), 1411–1424 (2009)

    Article  Google Scholar 

  24. Liu H, Gao L. 2010. A discrete electromagnetism-like mechanism algorithm for solving distributed permutation flowshop scheduling problem. Proc - 2010 Int Conf Manuf Autom ICMA 2010.:156–163.

  25. Naderi, B., Ruiz, R.: The distributed permutation flowshop scheduling problem. Comput. Oper. Res. 37(4), 754–768 (2010)

    Article  Google Scholar 

  26. Naderi, B., Ruiz, R.: A scatter search algorithm for the distributed permutation flowshop scheduling problem. Eur. J. Oper. Res. 239(2), 323–334 (2014)

    Article  Google Scholar 

  27. Osman, I., Potts, C.: Simulated annealing for permutation flow-shop scheduling. Omega. 17(6), 551–557 (1989)

    Article  Google Scholar 

  28. Pan, Q.K., Gao, L., Wang, L., Liang, J., Li, X.Y.: Effective heuristics and metaheuristics to minimize total flowtime for the distributed permutation flowshop problem. Expert. Syst. Appl. 124, 309–324 (2019)

    Article  Google Scholar 

  29. Pindo, M.L.: Scheduling. Springer, New York (2012)

    Book  Google Scholar 

  30. Prasad, S.D.: A genetic algorithm for flowshop scheduling with multiple objectives. Opsearch. 44(1), 1–16 (2007)

    Article  Google Scholar 

  31. Ropke, S., Pisinger, D.: A unified heuristic for a large class of Vehicle Routing Problems with Backhauls. Eur. J. Oper. Res. 171(3), 750–775 (2006)

    Article  Google Scholar 

  32. Ruiz, R., Pan, Q.K., Naderi, B.: Iterated Greedy methods for the distributed permutation flowshop scheduling problem. Omega (United Kingdom). 83, 213–222 (2019)

    Google Scholar 

  33. Ruiz, R., Stützle, T.: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur. J. Oper. Res. 177(3), 2033–2049 (2007)

    Article  Google Scholar 

  34. Sambasivan, M., Yahya, S.: A Lagrangean-based heuristic for multi-plant, multi-item, multi-period capacitated lot-sizing problems with inter-plant transfers. Comput. Oper. Res. 32(3), 537–555 (2005)

    Article  Google Scholar 

  35. Shabtay, D., Bensoussan, Y., Kaspi, M.: A bicriteria approach to maximize the weighted number of just-in-time jobs and to minimize the total resource consumption cost in a two-machine flow-shop scheduling system. Int. J. Prod. Econ. 136(1), 67–74 (2012)

    Article  Google Scholar 

  36. Wang FY, Chua TJ, Cai TX, Chai LS. 2007. Common capacity modelling for multi-site planning: Case studies. IEEE Int Conf Emerg Technol Fact Autom ETFA.:336–343.

  37. Wang J, Wang L, Shen J. 2016. A hybrid discrete cuckoo search for distributed permutation flowshop scheduling problem. 2016 IEEE Congr Evol Comput CEC. (2013):2240–2246.

  38. Wang, S., Wang, X., Chu, F., Yu, J.: An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production. Int. J. Prod. Res. 58(8), 2283–2314 (2020)

    Article  Google Scholar 

  39. Wang, S.Y., Wang, L., Liu, M., Xu, Y.: An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem. Int. J. Prod. Econ. 145(1), 387–396 (2013)

    Article  Google Scholar 

  40. Wilkinson, S.J., Cortier, A., Shah, N., Pantelides, C.C.: Integrated production and distribution scheduling on a Europe-wide basis. Comput. Chem. Eng. 20(96), S1275–S1280 (1996)

    Article  Google Scholar 

  41. Xu, Y., Wang, L., Wang, S., Liu, M.: An effective hybrid immune algorithm for solving the distributed permutation flow-shop scheduling problem. Eng. Optim. 46(9), 1269–1283 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada

    Arshad Ali

  2. Department of Supply Chain Management, Asper School of Business, University of Manitoba, Winnipeg, MB, R3T 5V4, Canada

    Yuvraj Gajpal

  3. Department of Mechanical and Industrial Engineering, Qatar University, 2713, Doha, Qatar

    Tarek Y. Elmekkawy

Authors
  1. Arshad Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuvraj Gajpal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tarek Y. Elmekkawy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arshad Ali.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table 3.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ali, A., Gajpal, Y. & Elmekkawy, T.Y. Distributed permutation flowshop scheduling problem with total completion time objective. OPSEARCH 58, 425–447 (2021). https://doi.org/10.1007/s12597-020-00484-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12597-020-00484-3

Keywords

Search

Navigation