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Smart scheduling for next generation manufacturing systems: a systematic literature review

Author

Listed:
  • Shriprasad Chorghe

    (Indian Institute of Technology Indore)

  • Rishi Kumar

    (Indian Institute of Technology Indore)

  • Makarand S. Kulkarni

    (Indian Institute of Technology Bombay)

  • Vibhor Pandhare

    (Indian Institute of Technology Indore)

  • Bhupesh Kumar Lad

    (Indian Institute of Technology Indore)

Abstract

In the current scenario, smart scheduling has become an essential requirement to generate dynamic schedules, prescribe, and adjust scheduling plans in response to dynamic events such as machine failures, unpredictable demand, customer order cancellations, worker unavailability, and mass customization. Such scheduling techniques must also take advantage of intelligence continuously being built for next-generation manufacturing systems. This study presents a systematic literature review on smart scheduling, analysing 123 identified literature from 2010 to May 2024 using the PRISMA technique. The analysis includes scientometric and content analysis to identify paradigm shifts in development (concepts, methodologies, practices) along with their maturity levels, and provides recommendations for the next generation of smart scheduling. This study is significant for advancing knowledge and addressing current and future needs/requirements in smart scheduling. This would serve as a reference in understanding the maturity status of various developments, assist researchers and practitioners in identifying research gaps, and direct future advancements in the smart scheduling domain.

Suggested Citation

  • Shriprasad Chorghe & Rishi Kumar & Makarand S. Kulkarni & Vibhor Pandhare & Bhupesh Kumar Lad, 2025. "Smart scheduling for next generation manufacturing systems: a systematic literature review," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 4447-4476, October.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02484-2
    DOI: 10.1007/s10845-024-02484-2
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    1. Maroua Nouiri & Damien Trentesaux & Abdelghani Bekrar, 2019. "Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems," Energies, MDPI, vol. 12(23), pages 1-30, November.
    2. Khalil Tliba & Thierno M. L. Diallo & Olivia Penas & Romdhane Ben Khalifa & Noureddine Ben Yahia & Jean-Yves Choley, 2023. "Digital twin-driven dynamic scheduling of a hybrid flow shop," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2281-2306, June.
    3. Romero-Silva, Rodrigo & Hernández-López, Gabriel, 2020. "Shop-floor scheduling as a competitive advantage: A study on the relevance of cyber-physical systems in different manufacturing contexts," International Journal of Production Economics, Elsevier, vol. 224(C).
    4. Maroua Nouiri & Abdelghani Bekrar & Damien Trentesaux, 2020. "An energy-efficient scheduling and rescheduling method for production and logistics systems†," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3263-3283, June.
    5. Lei Shi & Gang Guo & Xiaohui Song, 2021. "Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment," International Journal of Production Research, Taylor & Francis Journals, vol. 59(2), pages 576-597, January.
    6. Abderraouf Maoudj & Brahim Bouzouia & Abdelfetah Hentout & Ahmed Kouider & Redouane Toumi, 2019. "Distributed multi-agent scheduling and control system for robotic flexible assembly cells," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1629-1644, April.
    7. He, N. & Zhang, D.Z. & Li, Q., 2014. "Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system," International Journal of Production Economics, Elsevier, vol. 149(C), pages 117-130.
    8. Mohammad Rohaninejad & Reza Tavakkoli-Moghaddam & Behdin Vahedi-Nouri & Zdeněk Hanzálek & Shadi Shirazian, 2022. "A hybrid learning-based meta-heuristic algorithm for scheduling of an additive manufacturing system consisting of parallel SLM machines," International Journal of Production Research, Taylor & Francis Journals, vol. 60(20), pages 6205-6225, October.
    9. Cheng Qian & Yingfeng Zhang & Chen Jiang & Shenle Pan & Yiming Rong, 2020. "A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing," Post-Print hal-02190419, HAL.
    10. Weibo Ren & Yan Yan & Yaoguang Hu & Yu Guan, 2022. "Joint optimisation for dynamic flexible job-shop scheduling problem with transportation time and resource constraints," International Journal of Production Research, Taylor & Francis Journals, vol. 60(18), pages 5675-5696, September.
    11. Fei Qiao & Juan Liu & Yumin Ma, 2021. "Industrial big-data-driven and CPS-based adaptive production scheduling for smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 59(23), pages 7139-7159, December.
    12. Liping Zhou & Zhibin Jiang & Na Geng & Yimeng Niu & Feng Cui & Kefei Liu & Nanshan Qi, 2022. "Production and operations management for intelligent manufacturing: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 60(2), pages 808-846, January.
    13. Manuel Parente & Gonçalo Figueira & Pedro Amorim & Alexandra Marques, 2020. "Production scheduling in the context of Industry 4.0: review and trends," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5401-5431, September.
    14. Hui Yang & Soundar Kumara & Satish T.S. Bukkapatnam & Fugee Tsung, 2019. "The internet of things for smart manufacturing: A review," IISE Transactions, Taylor & Francis Journals, vol. 51(11), pages 1190-1216, November.
    15. Jacob Lohmer & Rainer Lasch, 2021. "Production planning and scheduling in multi-factory production networks: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 2028-2054, April.
    16. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).
    17. Yaqiong Liu & Shudong Sun & Xi Vincent Wang & Lihui Wang, 2022. "An iterative combinatorial auction mechanism for multi-agent parallel machine scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 60(1), pages 361-380, January.
    18. Chengfeng Jian & Jing Ping & Meiyu Zhang, 2021. "A cloud edge-based two-level hybrid scheduling learning model in cloud manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 59(16), pages 4836-4850, August.
    19. Shengluo Yang & Zhigang Xu, 2022. "Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 60(16), pages 4936-4953, August.
    20. Zengqiang Jiang & Shuai Yuan & Jing Ma & Qiang Wang, 2022. "The evolution of production scheduling from Industry 3.0 through Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 60(11), pages 3534-3554, June.
    21. Andrea Grassi & Guido Guizzi & Liberatina Carmela Santillo & Silvestro Vespoli, 2021. "Assessing the performances of a novel decentralised scheduling approach in Industry 4.0 and cloud manufacturing contexts," International Journal of Production Research, Taylor & Francis Journals, vol. 59(20), pages 6034-6053, October.
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