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Scheduling problems under learning effects: classification and cartography

Author

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  • Ameni Azzouz
  • Meriem Ennigrou
  • Lamjed Ben Said

Abstract

Traditionally, the processing times of jobs are assumed to be fixed and known throughout the entire process. However, recent empirical research in several industries has demonstrated that processing times decline as workers improve their skills and gain experience after doing the same task for a long time. This phenomenon is known as learning effects. Recently, several researchers have devoted a lot of effort on scheduling problems under learning effects. Although there is increase in the number of research in this topic, there are few review papers. The most recent one considers solely studies on scheduling problems with learning effects models prior to early 2007. For that, this paper focuses on reviewing the most recent advances in this field. First, we attempt to present a concise overview of some important learning models. Second, a new classification scheme for the different model of scheduling under learning effects is proposed and discussed. Next, a cartography showing the relation between some well-known models is proposed. Finally, our viewpoints and several areas for future research are provided.

Suggested Citation

  • Ameni Azzouz & Meriem Ennigrou & Lamjed Ben Said, 2018. "Scheduling problems under learning effects: classification and cartography," International Journal of Production Research, Taylor & Francis Journals, vol. 56(4), pages 1642-1661, February.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:4:p:1642-1661
    DOI: 10.1080/00207543.2017.1355576
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    Citations

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    Cited by:

    1. Zheng Liu & Ji-Bo Wang, 2024. "Single-Machine Scheduling with Simultaneous Learning Effects and Delivery Times," Mathematics, MDPI, vol. 12(16), pages 1-20, August.
    2. Zhang, Zhe & Song, Xiaoling & Gong, Xue & Yin, Yong & Lev, Benjamin & Zhou, Xiaoyang, 2024. "Coordinated seru scheduling and distribution operation problems with DeJong’s learning effects," European Journal of Operational Research, Elsevier, vol. 313(2), pages 452-464.
    3. Marco Trost & Thorsten Claus & Frank Herrmann, 2022. "Social Sustainability in Production Planning: A Systematic Literature Review," Sustainability, MDPI, vol. 14(13), pages 1-31, July.
    4. Baruch Mor & Gur Mosheiov & Dana Shapira, 2020. "Flowshop scheduling with learning effect and job rejection," Journal of Scheduling, Springer, vol. 23(6), pages 631-641, December.
    5. Mina Roohnavazfar & Daniele Manerba & Lohic Fotio Tiotsop & Seyed Hamid Reza Pasandideh & Roberto Tadei, 2021. "Stochastic single machine scheduling problem as a multi-stage dynamic random decision process," Computational Management Science, Springer, vol. 18(3), pages 267-297, July.
    6. Chen, Ke & Cheng, T.C.E. & Huang, Hailiang & Ji, Min & Yao, Danli, 2023. "Single-machine scheduling with autonomous and induced learning to minimize total weighted number of tardy jobs," European Journal of Operational Research, Elsevier, vol. 309(1), pages 24-34.
    7. Jian-You Xu & Win-Chin Lin & Yu-Wei Chang & Yu-Hsiang Chung & Juin-Han Chen & Chin-Chia Wu, 2023. "A Two-Machine Learning Date Flow-Shop Scheduling Problem with Heuristics and Population-Based GA to Minimize the Makespan," Mathematics, MDPI, vol. 11(19), pages 1-21, September.
    8. Ming-Hui Li & Dan-Yang Lv & Yuan-Yuan Lu & Ji-Bo Wang, 2024. "Scheduling with Group Technology, Resource Allocation, and Learning Effect Simultaneously," Mathematics, MDPI, vol. 12(7), pages 1-21, March.
    9. Yi-Chun Wang & Ji-Bo Wang, 2023. "Study on Convex Resource Allocation Scheduling with a Time-Dependent Learning Effect," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
    10. 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.
    11. Jesús Isaac Vázquez-Serrano & Leopoldo Eduardo Cárdenas-Barrón & Rodrigo E. Peimbert-García, 2021. "Agent Scheduling in Unrelated Parallel Machines with Sequence- and Agent–Machine–Dependent Setup Time Problem," Mathematics, MDPI, vol. 9(22), pages 1-34, November.
    12. 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.
    13. Wang, Xiong & Ferreira, Fernando A.F. & Chang, Ching-Ter, 2022. "Multi-objective competency-based approach to project scheduling and staff assignment: Case study of an internal audit project," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    14. 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.
    15. 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.
    16. 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.

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