Skip to main content
Log in

Minmizing service span with batch-position-based learning effects

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

We consider the learning effects in the coordination of production and outbound distribution for manufacturers. The objective is to minimize service span, which lasts from the beginning of production to the completion of delivery of products. In production, a batch-processing facility is used to process jobs which have different sizes. Batch-position-based learning effects are considered since workers become skillful gradually after processing batches one by one. In distribution, a vehicle with a fixed capacity is used to deliver products the customer and the transportation time from the manufacturer to the customer is a constant. We show the coordinated scheduling problem is NP-hard in the strong sense. We propose properties of optimal solutions and we provide an approximation algorithm for the problem. The absolute performance guarantee of the algorithm is 1.667 and the asymptotic performance guarantee is 1.223. Then we consider the problem where there are infinite vehicles and the performance guarantees are respectively 1.5 and 1.223. Finally we analyze the performance of the algorithm by the change of the problem scale, the learning index and operational factors. We propose managerial suggestions for decision makers of manufacturers according to our results.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Wang, J.B.: Single-machine scheduling problems with the effects of learning and deterioration. OMEGA 35(4), 397–402 (2007)

    Article  Google Scholar 

  2. Lai, P.J., Lee, W.C.: Single-machine scheduling with general sum-of-processing -time-based and position-based learning effects. OMEGA 39(5), 467–471 (2011)

    Article  Google Scholar 

  3. Lee, W.C., Chung, Y.H.: Permutation flowshop scheduling to minimize the total tardiness with learning effects. Int. J. Prod. Econ. 141(1), 327–334 (2013)

    Article  Google Scholar 

  4. Nouri, B.V., Fattahi, P., Ramezanian, R.: Hybrid firefly-simulated annealing algorithm for the flow shop problem with learning effects and flexible maintenance activities. Int. J. Prod. Res. 51(12), 3501–3515 (2013)

    Article  Google Scholar 

  5. Tarakci, H.: Two types of learning effects on maintenance activities. Int. J. Prod. Res. 144, 1–14 (2015)

    Google Scholar 

  6. Shafer, S.M., Nembhard, D.A., Uzumeri, M.V.: The effects of worker learning, forgetting, and heterogeneity on assembly line productivity. Manag. Sci. 47(12), 1639–1653 (2001)

    Article  Google Scholar 

  7. Otto, C., Otto, A.: Extending assembly line balancing problem by incorporating learning effects. Int. J. Prod. Res. 52(24), 7193–7208 (2014)

    Article  Google Scholar 

  8. Lu, R.F., Petersen, T.D., Storch, R.L.: Asynchronous stochastic learning curve effects in engineering-to-order customisation processes. Int. J. Prod. Res. 47(5), 1309–1329 (2009)

    Article  Google Scholar 

  9. Mccreery, J.K., Krajewski, L.J.: Improving performance using workforce flexibility in an assembly environment with learning and forgetting effects. Int. J. Prod. Res. 37(9), 2031–2058 (2010)

    Article  Google Scholar 

  10. Biskup, D.: A state-of-the-art review on scheduling with learning effects. Eur. J. Oper. Res. 188(2), 315–329 (2008)

    Article  MathSciNet  Google Scholar 

  11. Chen, Z.L., Vairaktarakis, G.L.: Integrated scheduling of production and distribution operations. Manag. Sci. 51, 614–628 (2005)

    Article  Google Scholar 

  12. Cheng, B.Y., Leung, J.Y.-T., Li, K., Yang, S.L.: Single batch machine scheduling with deliveries. Nav. Res. Logist. 62, 470–482 (2015)

    Article  MathSciNet  Google Scholar 

  13. Sawik, T., Lev, B.: Integrated supply, production and distribution scheduling under disruption risks. OMEGA 62, 131–144 (2015)

    Article  Google Scholar 

  14. Shahvari, O., Logendran, R.: Hybrid flow shop batching and scheduling with a bi-criteria objective. Int. J. Prod. Econ. 179, 239–258 (2016)

    Article  Google Scholar 

  15. Agnetis, A., Aloulou, M.A., Fu, L.L.: Production and interplant batch delivery scheduling: dominance and cooperation. Int. J. Prod. Econ. 182, 38–49 (2016)

    Article  Google Scholar 

  16. Cheng, B.Y., Leung, J.Y.-T., Li, K.: Integrated scheduling on a batch machine to minimize production, inventory and distribution costs. Eur. J. Oper. Res. 258(1), 104–112 (2017)

    Article  MathSciNet  Google Scholar 

  17. Geismar, H.N., Murthy, N.M.: Balancing production and distribution in paper manufacturing. Prod. Oper. Manag. 24(7), 1164–1178 (2014)

    Article  Google Scholar 

  18. Ng, C.T., Lu, L.: On-line integrated production and outbound distribution scheduling to minimize the maximum delivery completion time. J. Sched. 15(3), 391–398 (2012)

    Article  MathSciNet  Google Scholar 

  19. Dosa, G., Li, R., Han, X., Tuza, Z.: Tight absolute bound for first fit decreasing bin packing: FFD(L) \(\le \) 11/9OPT(L)+6/9. Theor. Comput. Sci. 510, 13–61 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grants 71671055, 71531008, 91746210, 71690230, 71671059 and 71471052.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bayi Cheng.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, B., Zhu, H., Liu, B. et al. Minmizing service span with batch-position-based learning effects. Optim Lett 15, 553–567 (2021). https://doi.org/10.1007/s11590-019-01402-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-019-01402-3

Keywords

Navigation