IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v40y2020i4d10.1007_s10878-020-00628-7.html
   My bibliography  Save this article

Note on the time complexity of resource constrained scheduling with general truncated job-dependent learning effect

Author

Listed:
  • Dexin Zou

    (Nanjing Sport Institute
    Jiangsu Sports and Health Engineering Collaborative Innovation Center)

  • Chong Jiang

    (Nanjing Sport Institute)

  • Weiwei Liu

    (Shenyang Sport University)

Abstract

In a recent paper (He et al. in J Comb Optim 33(2):626–644, 2017), the authors considered single machine resource allocation scheduling with general truncated job-dependent learning effect. For the convex resource consumption function and limited resource cost, the problem is to minimize the weighted sum of makespan, total completion time, the total absolute deviation in completion time and resource consumption cost. They conjectured that this problem is NP-hard. In this note we show that this problem can be solved in $$O(n^3)$$ O ( n 3 ) time. It is also shown that Lemma 4 in He et al. (2017) is incorrect by a counter-example.

Suggested Citation

  • Dexin Zou & Chong Jiang & Weiwei Liu, 2020. "Note on the time complexity of resource constrained scheduling with general truncated job-dependent learning effect," Journal of Combinatorial Optimization, Springer, vol. 40(4), pages 861-868, November.
    • Handle: RePEc:spr:jcomop:v:40:y:2020:i:4:d:10.1007_s10878-020-00628-7
    DOI: 10.1007/s10878-020-00628-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-020-00628-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-020-00628-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hongyu He & Mengqi Liu & Ji-Bo Wang, 2017. "Resource constrained scheduling with general truncated job-dependent learning effect," Journal of Combinatorial Optimization, Springer, vol. 33(2), pages 626-644, February.
    2. Biskup, Dirk, 2008. "A state-of-the-art review on scheduling with learning effects," European Journal of Operational Research, Elsevier, vol. 188(2), pages 315-329, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhongyi Jiang & Fangfang Chen & Xiandong Zhang, 2022. "Single-machine scheduling problems with general truncated sum-of-actual-processing-time-based learning effect," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 116-139, January.
    2. 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.
    3. Xingong Zhang & Guangle Yan & Wanzhen Huang & Guochun Tang, 2011. "Single-machine scheduling problems with time and position dependent processing times," Annals of Operations Research, Springer, vol. 186(1), pages 345-356, June.
    4. Wen-Hung Wu & Yunqiang Yin & T C E Cheng & Win-Chin Lin & Juei-Chao Chen & Shin-Yi Luo & Chin-Chia Wu, 2017. "A combined approach for two-agent scheduling with sum-of-processing-times-based learning effect," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(2), pages 111-120, February.
    5. Lodree Jr., Emmett J. & Geiger, Christopher D., 2010. "A note on the optimal sequence position for a rate-modifying activity under simple linear deterioration," European Journal of Operational Research, Elsevier, vol. 201(2), pages 644-648, March.
    6. Hongyu He & Yanzhi Zhao & Xiaojun Ma & Yuan-Yuan Lu & Na Ren & Ji-Bo Wang, 2023. "Study on Scheduling Problems with Learning Effects and Past Sequence Delivery Times," Mathematics, MDPI, vol. 11(19), pages 1-19, September.
    7. Lin-Hui Sun & Kai Cui & Ju-Hong Chen & Jun Wang & Xian-Chen He, 2013. "Research on permutation flow shop scheduling problems with general position-dependent learning effects," Annals of Operations Research, Springer, vol. 211(1), pages 473-480, December.
    8. Bakker, Steffen J. & Wang, Akang & Gounaris, Chrysanthos E., 2021. "Vehicle routing with endogenous learning: Application to offshore plug and abandonment campaign planning," European Journal of Operational Research, Elsevier, vol. 289(1), pages 93-106.
    9. Cheng, T.C.E. & Shafransky, Y. & Ng, C.T., 2016. "An alternative approach for proving the NP-hardness of optimization problems," European Journal of Operational Research, Elsevier, vol. 248(1), pages 52-58.
    10. Ji-Bo Wang & Ming-Zheng Wang, 2011. "Worst-case behavior of simple sequencing rules in flow shop scheduling with general position-dependent learning effects," Annals of Operations Research, Springer, vol. 191(1), pages 155-169, November.
    11. Li, Gang & Wang, Xiao-Yuan & Wang, Ji-Bo & Sun, Lin-Yan, 2013. "Worst case analysis of flow shop scheduling problems with a time-dependent learning effect," International Journal of Production Economics, Elsevier, vol. 142(1), pages 98-104.
    12. Li, Haitao & Womer, Keith, 2012. "Optimizing the supply chain configuration for make-to-order manufacturing," European Journal of Operational Research, Elsevier, vol. 221(1), pages 118-128.
    13. Guo, Z.X. & Ngai, E.W.T. & Yang, Can & Liang, Xuedong, 2015. "An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment," International Journal of Production Economics, Elsevier, vol. 159(C), pages 16-28.
    14. S. M. Mousavi & I. Mahdavi & J. Rezaeian & M. Zandieh, 2018. "An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times," Operational Research, Springer, vol. 18(1), pages 123-158, April.
    15. Corominas, Albert & Olivella, Jordi & Pastor, Rafael, 2010. "A model for the assignment of a set of tasks when work performance depends on experience of all tasks involved," International Journal of Production Economics, Elsevier, vol. 126(2), pages 335-340, August.
    16. Bahram Alidaee & Haibo Wang & R. Bryan Kethley & Frank Landram, 2019. "A unified view of parallel machine scheduling with interdependent processing rates," Journal of Scheduling, Springer, vol. 22(5), pages 499-515, October.
    17. Zong-Jun Wei & Li-Yan Wang & Lei Zhang & Ji-Bo Wang & Ershen Wang, 2023. "Single-Machine Maintenance Activity Scheduling with Convex Resource Constraints and Learning Effects," Mathematics, MDPI, vol. 11(16), pages 1-21, August.
    18. Yang, Suh-Jenq & Yang, Dar-Li, 2010. "Minimizing the makespan on single-machine scheduling with aging effect and variable maintenance activities," Omega, Elsevier, vol. 38(6), pages 528-533, December.
    19. Kiassat, Corey & Safaei, Nima & Banjevic, Dragan, 2014. "Choosing the optimal intervention method to reduce human-related machine failures," European Journal of Operational Research, Elsevier, vol. 233(3), pages 604-612.
    20. Koulamas, Christos & Gupta, Sushil & Kyparisis, George J., 2010. "A unified analysis for the single-machine scheduling problem with controllable and non-controllable variable job processing times," European Journal of Operational Research, Elsevier, vol. 205(2), pages 479-482, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:40:y:2020:i:4:d:10.1007_s10878-020-00628-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.