Serial-batching group scheduling with release times and the combined effects of deterioration and truncated job-dependent learning

Abstract

This paper investigates a single machine serial-batching scheduling problem considering release times, setup time, and group scheduling, with the combined effects of deterioration and truncated job-dependent learning. The objective of the studied problem is to minimize the makespan. Firstly, we analyze the special case where all groups have the same arrival time, and propose the optimal structural properties on jobs sequencing, jobs batching, batches sequencing, and groups sequencing. Next, the corresponding batching rule and algorithm are developed. Based on these properties and the scheduling algorithm, we develop a hybrid VNS–ASHLO algorithm incorporating variable neighborhood search (VNS) and adaptive simplified human learning optimization (ASHLO) algorithms to solve the general case of the studied problem. Computational experiments on randomly generated instances are conducted to compare the proposed VNS–ASHLO with the algorithms of VNS, ASHLO, Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The results based on instances of different scales show the effectiveness and efficiency of the proposed algorithm.

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References

  1. 1.

    Wang, J.B., Jiang, Y., Wang, G.: Single-machine scheduling with past-sequence-dependent setup times and effects of deterioration and learning. Int. J. Adv. Manuf. Technol. 41(11–12), 1221–1226 (2009)

    Article  Google Scholar 

  2. 2.

    Wang, J.B., Gao, W.J., Wang, L.Y., Wang, D.: Single machine group scheduling with general linear deterioration to minimize the makespan. Int. J. Adv. Manuf. Technol. 43, 146–150 (2009)

    Article  Google Scholar 

  3. 3.

    Cheng, T.C.E., Lee, W.-C., Wu, C.-C.: Scheduling problems with deteriorating jobs and learning effects including proportional setup times. Comput. Ind. Eng. 58(2), 326–331 (2010)

    Article  Google Scholar 

  4. 4.

    Kuo, W.-H.: Single-machine group scheduling with time-dependent learning effect and position-based setup time learning effect. Ann. Oper. Res. 196, 349–359 (2012)

    MathSciNet  Article  MATH  Google Scholar 

  5. 5.

    Wang, J.B., Huang, X., Wu, Y.B., Ji, P.: Group scheduling with independent setup times, ready times, and deteriorating job processing times. Int. J. Adv. Manuf. Technol. 60, 643–649 (2012)

    Article  Google Scholar 

  6. 6.

    Yang, S.-J.: Unrelated parallel-machine scheduling with deterioration effects and deteriorating multi-maintenance activities for minimizing the total completion time. Appl. Math. Model. 37, 2995–3005 (2013)

    MathSciNet  Article  MATH  Google Scholar 

  7. 7.

    Yang, S.-W., Wan, L., Yin, N.: Research on single machine SLK/DIF due window assignment problem with learning effect and deteriorating jobs. Appl. Math. Model. 39, 4593–4598 (2015)

    MathSciNet  Article  Google Scholar 

  8. 8.

    Wu, C.-C., Wu, W.-H., Hsu, P.-H., Lai, K.: A two-machine flowshop scheduling problem with a truncated sum of processing-times-based learning function. Appl. Math. Model. 36, 5001–5014 (2012)

    MathSciNet  Article  MATH  Google Scholar 

  9. 9.

    He, H., Liu, M., Wang, J.B.: Resource constrained scheduling with general truncated job-dependent learning effect. J. Comb. Optim. (2015). doi:10.1007/s10878-015-9984-5

    MATH  Google Scholar 

  10. 10.

    Wu, Y.-B., Wang, J.-J.: Single-machine scheduling with truncated sum-of-processing-times-based learning effect including proportional delivery times. Neural. Comput. Appl. 27, 937–943 (2016)

    Article  Google Scholar 

  11. 11.

    Niu, Y.-P., Wang, J., Yin, N.: Scheduling problems with effects of deterioration and truncated job-dependent learning. J. Appl. Math. Comput. 47, 315–325 (2015)

    MathSciNet  Article  MATH  Google Scholar 

  12. 12.

    Wang, J.B., Wang, X.-Y., Sun, L.-H., Sun, L.-Y.: Scheduling jobs with truncated exponential learning functions. Optim. Lett. 7, 1857–1873 (2013)

    MathSciNet  Article  MATH  Google Scholar 

  13. 13.

    Wu, C.-C., Yin, Y., Cheng, S.-R.: Single-machine and two-machine flowshop scheduling problems with truncated position-based learning functions. J. Oper. Res. Soc. 64, 147–156 (2013)

    Article  Google Scholar 

  14. 14.

    Pei, J., Liu, X., Pardalos, P.M., Migdalas, A., Yang, S.: Serial-batching scheduling with time-dependent setup time and effects of deterioration and learning on a single-machine. J. Glob. Optim. 67(1), 251–262 (2017)

    MathSciNet  Article  MATH  Google Scholar 

  15. 15.

    Pei, J., Pardalos, P.M., Liu, X., Fan, W., Yang, S.: Serial batching scheduling of deteriorating jobs in a two-stage supply chain to minimize the makespan. Eur. J. Oper. Res. 244(1), 13–25 (2015)

    MathSciNet  Article  MATH  Google Scholar 

  16. 16.

    Pei, J., Liu, X., Pardalos, P.M., Fan, W., Yang, S.: Scheduling deteriorating jobs on a single serial-batching machine with multiple job types and sequence-dependent setup times. Ann. Oper. Res. 249, 175–195 (2017)

    MathSciNet  Article  MATH  Google Scholar 

  17. 17.

    Pei, J., Liu, X., Pardalos, P.M., Li, K., Fan, W., Migdalas, A.: Single-machine serial-batching scheduling with a machine availability constraint, position-dependent processing time, and time-dependent set-up time. Optim. Lett. (2016). doi:10.1007/s11590-016-1074-9

    MATH  Google Scholar 

  18. 18.

    Xuan, H., Tang, L.X.: Scheduling a hybrid flowshop with batch production at the last stage. Comput. Oper. Res. 34(9), 2718–2733 (2007)

    Article  MATH  Google Scholar 

  19. 19.

    Graham, R.L., Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G.: Optimization and approximation indeterministic sequencing and scheduling: a survey. Ann. Discret. Math. 5, 287–326 (1979)

    Article  MATH  Google Scholar 

  20. 20.

    Hansen, P., Mladenović, N.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)

    MathSciNet  Article  MATH  Google Scholar 

  21. 21.

    Hansen, P., Mladenović, N., Pérez, J.A.M.: Variable neighbourhood search: methods and applications. 4OR 175(4), 367–407 (2008)

  22. 22.

    Wang, L., Ni, H., Yang, R., Pardalos, P.M., Du, X., Fei, M.: An adaptive simplified human learning optimization algorithm. Inf. Sci. 320, 126–139 (2015)

    MathSciNet  Article  Google Scholar 

  23. 23.

    Xia, H., Li, X., Gao, L.: A hybrid genetic algorithm with variable neighborhood search for dynamic integrated process planning and scheduling. Comput. Ind. Eng. 102, 99–112 (2016)

    Article  Google Scholar 

  24. 24.

    Borges, P., Eid, T., Bergseng, E.: Applying simulated annealing using different methods for the neighborhood search in forest planning problems. Eur. J. Oper. Res. 233(3), 700–710 (2014)

    MathSciNet  Article  MATH  Google Scholar 

  25. 25.

    Liang, X., Li, W., Zhang, Y., Zhou, M.: An adaptive particle swarm optimization method based on clustering. Soft Comput. 19(2), 431–448 (2015)

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 71501058, 71601065, 71231004, 71690235, 71690230, 71671055), and Innovative Research Groups of the National Natural Science Foundation of China (71521001), the Humanities and Social Sciences Foundation of the Chinese Ministry of Education (No. 15YJC630097), and Anhui Province Natural Science Foundation (No. 1608085QG167). Panos M. Pardalos is partially supported by the project of “Distinguished International Professor by the Chinese Ministry of Education” (MS2014HFGY026).

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Correspondence to Jun Pei or Xinbao Liu.

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Fan, W., Pei, J., Liu, X. et al. Serial-batching group scheduling with release times and the combined effects of deterioration and truncated job-dependent learning. J Glob Optim 71, 147–163 (2018). https://doi.org/10.1007/s10898-017-0536-7

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Keywords

  • Scheduling
  • Serial-batching
  • Group scheduling
  • Release time
  • Deterioration
  • Truncated job-dependent learning