IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v241y2021ics0925527321002486.html
   My bibliography  Save this article

Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system

Author

Listed:
  • Li, Mingxing
  • Huang, George Q.

Abstract

Assembly line balancing problems (ALBP) have plagued scholars and practitioners for decades. This paper investigates a new assembly system called flexible assembly line (FAL) derived from empirical observations in an air-conditioner assembly workshop. FAL can avoid the ALBP itself thanks to its structural flexibility and reconfigurability. However, field investigation highlights new challenges in the FAL - the mismatch between production (assembly) and intralogistics (material supply) leads to long waiting/idle time and workflow chaos, consequently lowers productivity and increases backorders. The production-intralogistics (PiL) processes are spatiotemporally coupled and interactional. Its complexity is much higher than considering the production or intralogistics optimization solely. And the PiL processes are further complicated by uncertain events such as new job arrivals, stochastic operational time, and equipment failures. The advent of Industry 4.0 technologies shows the tremendous potentials to revolutionize the contemporary notions of production management. Massive production data can be collected and analyzed in real-time. Nevertheless, there is little methodological research regarding utilizing real-time data to support production decisions under uncertainties. Thus, how to leverage real-time data collected in Industry 4.0 environments to support the decision-making of PiL processes for achieving a matched, coordinated, and synchronous operations management under various uncertainties, is a novel research problem. This paper develops a five-phase Graduation intelligent Manufacturing System (GiMS) to achieve PiL synchronization with flexibility and resilience. The underlying principles and rationale of GiMS are formulated as a synchronization mechanism, which includes a graph-theory based clustering for planning/scheduling and real-time decentralized ticketing for execution/control. Comprehensive numerical results validate the superiority of GiMS and the benefits of visibility and traceability in various scenarios. Moreover, the effects of uncertainties and trolley capacity are investigated in the sensitivity analysis.

Suggested Citation

  • Li, Mingxing & Huang, George Q., 2021. "Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system," International Journal of Production Economics, Elsevier, vol. 241(C).
  • Handle: RePEc:eee:proeco:v:241:y:2021:i:c:s0925527321002486
    DOI: 10.1016/j.ijpe.2021.108272
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527321002486
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2021.108272?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. Yong-Hong Kuo & Andrew Kusiak, 2019. "From data to big data in production research: the past and future trends," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4828-4853, August.
    2. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    3. Sternatz, Johannes, 2015. "The joint line balancing and material supply problem," International Journal of Production Economics, Elsevier, vol. 159(C), pages 304-318.
    4. Baller, Reinhard & Hage, Steffen & Fontaine, Pirmin & Spinler, Stefan, 2020. "The assembly line feeding problem: An extended formulation with multiple line feeding policies and a case study," International Journal of Production Economics, Elsevier, vol. 222(C).
    5. Boysen, Nils & Fliedner, Malte & Scholl, Armin, 2007. "A classification of assembly line balancing problems," European Journal of Operational Research, Elsevier, vol. 183(2), pages 674-693, December.
    6. Devapriya, Priyantha & Ferrell, William & Geismar, Neil, 2017. "Integrated production and distribution scheduling with a perishable product," European Journal of Operational Research, Elsevier, vol. 259(3), pages 906-916.
    7. Daria Battini & Martina Calzavara & Alena Otto & Fabio Sgarbossa, 2017. "Preventing ergonomic risks with integrated planning on assembly line balancing and parts feeding," International Journal of Production Research, Taylor & Francis Journals, vol. 55(24), pages 7452-7472, December.
    8. Nico André Schmid & Veronique Limère, 2019. "A classification of tactical assembly line feeding problems," International Journal of Production Research, Taylor & Francis Journals, vol. 57(24), pages 7586-7609, December.
    9. Becker, Christian & Scholl, Armin, 2006. "A survey on problems and methods in generalized assembly line balancing," European Journal of Operational Research, Elsevier, vol. 168(3), pages 694-715, February.
    10. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
    11. Romero-Silva, Rodrigo & Shaaban, Sabry & Marsillac, Erika & Hurtado, Margarita, 2018. "Exploiting the characteristics of serial queues to reduce the mean and variance of flow time using combined priority rules," International Journal of Production Economics, Elsevier, vol. 196(C), pages 211-225.
    12. Pereira, Jordi & Álvarez-Miranda, Eduardo, 2018. "An exact approach for the robust assembly line balancing problem," Omega, Elsevier, vol. 78(C), pages 85-98.
    13. Zhong, Ray Y. & Huang, George Q. & Lan, Shulin & Dai, Q.Y. & Chen, Xu & Zhang, T., 2015. "A big data approach for logistics trajectory discovery from RFID-enabled production data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 260-272.
    14. Battaïa, Olga & Dolgui, Alexandre, 2013. "A taxonomy of line balancing problems and their solutionapproaches," International Journal of Production Economics, Elsevier, vol. 142(2), pages 259-277.
    15. 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.
    16. Tava Lennon Olsen & Brian Tomlin, 2020. "Industry 4.0: Opportunities and Challenges for Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 113-122, January.
    17. Robert L. Carraway, 1989. "A Dynamic Programming Approach to Stochastic Assembly Line Balancing," Management Science, INFORMS, vol. 35(4), pages 459-471, April.
    18. Hamta, Nima & Fatemi Ghomi, S.M.T. & Jolai, F. & Akbarpour Shirazi, M., 2013. "A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect," International Journal of Production Economics, Elsevier, vol. 141(1), pages 99-111.
    19. Guo, Daqiang & Li, Mingxing & Lyu, Zhongyuan & Kang, Kai & Wu, Wei & Zhong, Ray Y. & Huang, George Q., 2021. "Synchroperation in industry 4.0 manufacturing," International Journal of Production Economics, Elsevier, vol. 238(C).
    20. Daqiang Guo & Ray Y. Zhong & Shiquan Ling & Yiming Rong & George Q. Huang, 2020. "A roadmap for Assembly 4.0: self-configuration of fixed-position assembly islands under Graduation Intelligent Manufacturing System," International Journal of Production Research, Taylor & Francis Journals, vol. 58(15), pages 4631-4646, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Zhe & Gong, Xue & Song, Xiaoling & Yin, Yong & Lev, Benjamin & Zhou, Xiaoyang, 2024. "An effective two phase heuristic for synchronized seru production scheduling and 3PL transportation problems," International Journal of Production Economics, Elsevier, vol. 268(C).
    2. Leung, Eric K.H. & Lee, Carmen Kar Hang & Ouyang, Zhiyuan, 2022. "From traditional warehouses to Physical Internet hubs: A digital twin-based inbound synchronization framework for PI-order management," International Journal of Production Economics, Elsevier, vol. 244(C).
    3. Patanjal Kumar & Sachin Kumar Mangla & Yigit Kazancoglu & Ali Emrouznejad, 2023. "A decision framework for incorporating the coordination and behavioural issues in sustainable supply chains in digital economy," Annals of Operations Research, Springer, vol. 326(2), pages 721-749, July.
    4. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    5. Pabolu, Venkata Krishna Rao & Shrivastava, Divya & Kulkarni, Makarand S., 2022. "Modelling and prediction of worker task performance using a knowledge-based system application," International Journal of Production Economics, Elsevier, vol. 254(C).

    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. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    2. Boysen, Nils & Schulze, Philipp & Scholl, Armin, 2022. "Assembly line balancing: What happened in the last fifteen years?," European Journal of Operational Research, Elsevier, vol. 301(3), pages 797-814.
    3. Ibrahim Kucukkoc & Kadir Buyukozkan & Sule Itir Satoglu & David Z. Zhang, 2019. "A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2913-2925, December.
    4. Jietao Dong & Linxuan Zhang & Tianyuan Xiao, 2018. "A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 737-751, April.
    5. Adenipekun, Ebenezer Olatunde & Limère, Veronique & Schmid, Nico André, 2022. "The impact of transportation optimisation on assembly line feeding," Omega, Elsevier, vol. 107(C).
    6. Walter, Rico & Schulze, Philipp & Scholl, Armin, 2021. "SALSA: Combining branch-and-bound with dynamic programming to smoothen workloads in simple assembly line balancing," European Journal of Operational Research, Elsevier, vol. 295(3), pages 857-873.
    7. García-Villoria, Alberto & Corominas, Albert & Nadal, Adrià & Pastor, Rafael, 2018. "Solving the accessibility windows assembly line problem level 1 and variant 1 (AWALBP-L1-1) with precedence constraints," European Journal of Operational Research, Elsevier, vol. 271(3), pages 882-895.
    8. Schmid, Nico André & Limère, Veronique & Raa, Birger, 2021. "Mixed model assembly line feeding with discrete location assignments and variable station space," Omega, Elsevier, vol. 102(C).
    9. Borba, Leonardo & Ritt, Marcus & Miralles, Cristóbal, 2018. "Exact and heuristic methods for solving the Robotic Assembly Line Balancing Problem," European Journal of Operational Research, Elsevier, vol. 270(1), pages 146-156.
    10. Marcus Ritt & Alysson M. Costa & Cristóbal Miralles, 2016. "The assembly line worker assignment and balancing problem with stochastic worker availability," International Journal of Production Research, Taylor & Francis Journals, vol. 54(3), pages 907-922, February.
    11. Özcan, Ugur, 2010. "Balancing stochastic two-sided assembly lines: A chance-constrained, piecewise-linear, mixed integer program and a simulated annealing algorithm," European Journal of Operational Research, Elsevier, vol. 205(1), pages 81-97, August.
    12. Urban, Timothy L. & Chiang, Wen-Chyuan, 2016. "Designing energy-efficient serial production lines: The unpaced synchronous line-balancing problem," European Journal of Operational Research, Elsevier, vol. 248(3), pages 789-801.
    13. Sikora, Celso Gustavo Stall, 2024. "Balancing mixed-model assembly lines for random sequences," European Journal of Operational Research, Elsevier, vol. 314(2), pages 597-611.
    14. Wen-Chyuan Chiang & Timothy L. Urban & Chunyong Luo, 2016. "Balancing stochastic two-sided assembly lines," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 6232-6250, October.
    15. Arnd Huchzermeier & Tobias Mönch, 2023. "Mixed‐model assembly lines with variable takt and open stations," Production and Operations Management, Production and Operations Management Society, vol. 32(3), pages 704-722, March.
    16. Emilio Moretti & Elena Tappia & Veronique Limère & Marco Melacini, 2021. "Exploring the application of machine learning to the assembly line feeding problem," Operations Management Research, Springer, vol. 14(3), pages 403-419, December.
    17. Jonathan Oesterle & Lionel Amodeo & Farouk Yalaoui, 2019. "A comparative study of Multi-Objective Algorithms for the Assembly Line Balancing and Equipment Selection Problem under consideration of Product Design Alternatives," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1021-1046, March.
    18. M. H. Alavidoost & M. H. Fazel Zarandi & Mosahar Tarimoradi & Yaser Nemati, 2017. "Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 313-336, February.
    19. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).
    20. Guo, Daqiang & Li, Mingxing & Lyu, Zhongyuan & Kang, Kai & Wu, Wei & Zhong, Ray Y. & Huang, George Q., 2021. "Synchroperation in industry 4.0 manufacturing," International Journal of Production Economics, Elsevier, vol. 238(C).

    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:eee:proeco:v:241:y:2021:i:c:s0925527321002486. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.