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

Author

Listed:
  • Wenjuan Fan

    (Hefei University of Technology
    University of Florida)

  • Jun Pei

    (Hefei University of Technology
    University of Florida)

  • Xinbao Liu

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education)

  • Panos M. Pardalos

    (University of Florida)

  • Min Kong

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education)

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.

Suggested Citation

  • Wenjuan Fan & Jun Pei & Xinbao Liu & Panos M. Pardalos & Min Kong, 2018. "Serial-batching group scheduling with release times and the combined effects of deterioration and truncated job-dependent learning," Journal of Global Optimization, Springer, vol. 71(1), pages 147-163, May.
    • Handle: RePEc:spr:jglopt:v:71:y:2018:i:1:d:10.1007_s10898-017-0536-7
    DOI: 10.1007/s10898-017-0536-7
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    References listed on IDEAS

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    1. Cheng, T.C.E. & Wu, Chin-Chia & Chen, Juei-Chao & Wu, Wen-Hsiang & Cheng, Shuenn-Ren, 2013. "Two-machine flowshop scheduling with a truncated learning function to minimize the makespan," International Journal of Production Economics, Elsevier, vol. 141(1), pages 79-86.
    2. Borges, Paulo & Eid, Tron & Bergseng, Even, 2014. "Applying simulated annealing using different methods for the neighborhood search in forest planning problems," European Journal of Operational Research, Elsevier, vol. 233(3), pages 700-710.
    3. C-C Wu & Y Yin & S-R Cheng, 2013. "Single-machine and two-machine flowshop scheduling problems with truncated position-based learning functions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(1), pages 147-156, January.
    4. Jun Pei & Xinbao Liu & Panos M. Pardalos & Athanasios Migdalas & Shanlin Yang, 2017. "Serial-batching scheduling with time-dependent setup time and effects of deterioration and learning on a single-machine," Journal of Global Optimization, Springer, vol. 67(1), pages 251-262, January.
    5. Pei, Jun & Pardalos, Panos M. & Liu, Xinbao & Fan, Wenjuan & Yang, Shanlin, 2015. "Serial batching scheduling of deteriorating jobs in a two-stage supply chain to minimize the makespan," European Journal of Operational Research, Elsevier, vol. 244(1), pages 13-25.
    6. Jun Pei & Xinbao Liu & Panos M. Pardalos & Wenjuan Fan & Shanlin Yang, 2017. "Scheduling deteriorating jobs on a single serial-batching machine with multiple job types and sequence-dependent setup times," Annals of Operations Research, Springer, vol. 249(1), pages 175-195, February.
    7. Wen-Hung Kuo, 2012. "Single-machine group scheduling with time-dependent learning effect and position-based setup time learning effect," Annals of Operations Research, Springer, vol. 196(1), pages 349-359, July.
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    Cited by:

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    2. Nodari Vakhania & Badri Mamporia, 2020. "Fast Algorithms for Basic Supply Chain Scheduling Problems," Mathematics, MDPI, vol. 8(11), pages 1-19, November.
    3. Zhang, Jun & Liu, Feng & Tang, Jiafu & Li, Yanhui, 2019. "The online integrated order picking and delivery considering Pickers’ learning effects for an O2O community supermarket," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 180-199.
    4. Cheng, Bayi & Zhu, Huijun & Li, Kai & Li, Yongjun, 2019. "Optimization of batch operations with a truncated batch-position-based learning effect," Omega, Elsevier, vol. 85(C), pages 134-143.
    5. Jun Pei & Qingru Song & Baoyu Liao & Xinbao Liu & Panos M. Pardalos, 2021. "Parallel-machine serial-batching scheduling with release times under the effects of position-dependent learning and time-dependent deterioration," Annals of Operations Research, Springer, vol. 298(1), pages 407-444, March.
    6. Shaojun Lu & Jun Pei & Xinbao Liu & Xiaofei Qian & Nenad Mladenovic & Panos M. Pardalos, 2020. "Less is more: variable neighborhood search for integrated production and assembly in smart manufacturing," Journal of Scheduling, Springer, vol. 23(6), pages 649-664, December.
    7. Baoyu Liao & Xingming Wang & Xing Zhu & Shanlin Yang & Panos M. Pardalos, 2020. "Less is more approach for competing groups scheduling with different learning effects," Journal of Combinatorial Optimization, Springer, vol. 39(1), pages 33-54, January.

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