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Sequencing and planning of packaging lines with reliability and digital twin concept considerations - a case study of a sugar production plant

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Background: The study focuses on simplified make-and-pack production in the sugar industry as a case study. The analyzed system is characterized by parallel packing lines, which share one resource with a sequence-independent setup time. Additionally, the special characteristics that occur in many enterprises make scheduling difficult. The special characteristics of the system are the simultaneous occurrence of a variable input stream, scheduling of processes, and including the reliability of machines. Due to the variability of the input parameters, it is appropriate to consider the use of Digital Twin, which is a virtual representation of the real processes' performance. Therefore, this purpose of the paper is two-fold. First, an analysis of sequence determination of the stream-splitting machine was performed with taking into account the impact of logistics system reliability on system performance. Second, the concept of implementing Digital Twin in the analyzed production process is presented. Methods: The mathematical model for line efficiency was developed on the presented make-and-pack production presented in the selected sugar industry. Different sequences of stream-splitting machines were studied to examine the system's efficiency, availability, and utilization of packaging lines. Two scenarios were investigated with the use of computer simulation. Results: Computer simulation experiments were performed to investigate the sequencing and planning of packaging line problems. The results obtained for the case company indicated a significant dependence between the preferred packing sequence and the operational parameters. Conclusions: The simulations confirm the influence of internal and external factors on sugar line packaging processes. The main advantage of the developed simulation model is identifying the relationship between the size of the input stream and the system's availability level, as well as identifying the main constraints on the possibility of implementing the DT concept in the analyzed company.
Czasopismo
Rocznik
Strony
321--334
Opis fizyczny
Bibliogr. 43 poz., rys., tab., wykr.
Twórcy
  • Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Department of Technical Systems Operation and Maintenance, Wroclaw, Poland
autor
  • Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Department of Technical Systems Operation and Maintenance, Wroclaw, Poland
  • Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Department of Technical Systems Operation and Maintenance, Wroclaw, Poland
Bibliografia
  • 1. Acebes L.F., Merino A., Rodriguez A., Mazaeda R., de Prada C., 2019, model based online scheduling of concurrent and equal batch process units: Sugar End industrial case study, Journal of Process Control, 80, 1–14, https://doi.org/10.1016/j.jprocont.2019.05.005
  • 2. Azizi H., Hakimzadeh V., Golestani H.A., 2016, Purification of Raw Sugar Beet Juice by Electrocoagulation, Ukrainian Food Journal, 5(4), 667-677. http://dx.doi.org/10.24263/2304-974X-2016-5-4-6
  • 3. Baumann P., Trautmann N., 2013, A continuous-time MILP model for short-term scheduling of make-and-pack production processes, International Journal of Production Research 51(6), 1707–1727, https://doi.org/10.1080/00207543.2012.694489.
  • 4. Bestjak L., Lindqvist C., 2020, Assessment of how Digital Twin can be utilized in manufacturing companies to create business value, MSc thesis, School of Innovation, Design and Engineering, Eskilstuna, Sweden.
  • 5. Branke J., Nguyen S., Pickardt C.W., Zhang M., 2016, Automated Design of Production Scheduling Heuristics: A Review, IEEE Transactions on Evolutionary Computation, 20(1), 110–124, https://doi.org/10.1109/TEVC.2015.2429314
  • 6. Caldwell D.G., Davis S., Masey R.J.M., Gray J.O., 2009, Automation in food processing, In: Nof, S. (ed.) Springer Handbook of Automation, 1041-1059. Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-78831-7_60
  • 7. Chauhan M.K., Varun, Chaudhary S., Kumar S., Samar, 2011, Life cycle assessment of sugar industry: A review, Renewable and Sustainable Energy Reviews, 15(7), 3445-3453, https://doi.org/10.1016/j.rser.2011.04.033
  • 8. Chen J.C., Cheng CH., Huang P.B., Wang K-J., Huang Ch-J., Ting T-Ch., 2013, Warehouse management with lean and RFID application: a case study, The International Journal of Advanced Manufacturing Technology, 69, 531-542, https://doi.org/10.1007/s00170-013-5016-8
  • 9. Dotoli M., Fay A., Miśkowicz M., Seatzu C., 2019, An overview of current technologies and emerging trends in factory automation, International Journal of Production Research 57(15–16), 5047–5067, https://doi.org/10.1080/00207543.2018.1510558
  • 10. Eggleston G., Lima I., 2015, Sustainability issues and opportunities in the sugar and sugar-bioproduct industries, Sustainability, 7(9), 12209–12235, https://doi.org/10.3390/su70912209
  • 11. Entrup M.L., Günther H.O., Van Beek P., Grunow M., Seiler T., 2005, Mixed-integer linear programming approaches to shelf-life-integrated planning and scheduling in yoghurt production, International Journal of Production Research, 43(23), 5071-5100, https://doi.org/10.1080/00207540500161068
  • 12. Errandonea I., Beltrán S., Arrizabalaga S., 2020, Digital Twin for maintenance: A literature review, Computers in Industry, 123, 103316, DOI:https://doi.org/10.1016/j.compind.2020.103316
  • 13. Fuchigami H.Y., Rangel S.,2018, A survey of case studies in production scheduling: Analysis and perspectives, Journal of Computational Science, 25, 425-436, https://doi.org/10.1016/j.jocs.2017.06.004
  • 14. Graham R.L., Lawler E.L., Lenstra J.K., Rinnooy Kan A.H.G., 1979, Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey, Annals Of Discrete Mathematics, 5, 287-326, https://doi.org/10.1016/S0167-5060(08)70356-X
  • 15. Harjunkoski I., Maravelias C.T., Bongers P., Castro P.M., Engell S., Grossmann I.E., Hooker J., Méndez C., Sand G., Wassick J., 2014, Scope for industrial applications of production scheduling models and solution methods, Computers and Chemical Engineering, 62, 161–193, https://doi.org/10.1016/j.compchemeng.2013.12.001
  • 16. Hecker F.T., Stanke M., Becker T., Hitzmann B., 2014, Application of a modified GA, ACO and a random search procedure to solve the production scheduling of a case study bakery, Expert Systems with Applications, 41(13), 5882–5891, https://doi.org/10.1016/j.eswa.2014.03.047
  • 17. Iqbal J., Khan Z.H., Khalid A., 2017, Prospects of robotics in food industry, Food Science and Technology 37(2), 159–165, https://doi.org/10.1590/1678-457X.14616
  • 18. Kondakci T., Zhou W., 2017, Recent Applications of Advanced Control Techniques in Food Industry, Food and Bioprocess Technology, 10(3), 522–542, https://doi.org/10.1007/s11947-016-1831-x
  • 19. Konstantinov S., Ahmad M., Ananthanarayan K., Harrison R., 2017, The Cyber-physical E-machine Manufacturing System: Virtual Engineering for Complete Lifecycle Support, Procedia CIRP, 63, 119-124, https://doi.org/10.1016/j.procir.2017.02.035
  • 20. Kopacek P., 2019, Trends in Production Automation, IFAC-PapersOnLine, 52(25), 509–512, https://doi.org/10.1016/j.ifacol.2019.12.595
  • 21. Kosior K., Digital Twin Technology in The Food Industry Enterprises – Requirements, Potential Applications, Limitations (in Polish), Przemysł Spożywczy, T. 74, 5, 10-14, http://dx.doi.org/10.15199/65.2020.5.2
  • 22. Liu M., Fang S., Dong H., Xu C., 2021, Review of digital twin about concepts, technologies, and industrial applications, Journal of Manufacturing Systems, 58, Part B, 346-361, https://doi.org/10.1016/j.jmsy.2020.06.017
  • 23. Méndez C.A., Cerdá J., 2002, An MILP-based approach to the short-term scheduling of make-and-pack continuous production plants, OR Spectrum 24(4), 403-429, https://doi.org/10.1007/s00291-002-0103-5
  • 24. Merino A., Mazaeda R., Alves R., Rueda A., Acebes L.F., De Prada C., 2006, Sugar factory simulator for operators training, IFAC Proceedings Volumes, 7(PART 1), 259–264, https://doi.org/10.3182/20060621-3-ES-2905.00046
  • 25. Olivotti D., Dreyer S., Lebek B., Breitner M.H., 2019, Creating the foundation for digital twins in the manufacturing industry: an integrated installed base management system. Information Systems and E-Business Management, 17, 89–116, https://doi.org/10.1007/s10257-018-0376-0
  • 26. Parthanadee P., Buddhakulsomsiri J., 2010, Simulation modeling and analysis for production scheduling using real-time dispatching rules: A case study in canned fruit industry, Computers and Electronics in Agriculture, 70(1), 245–255, https://doi.org/10.1016/j.compag.2009.11.002
  • 27. Pinedo M., 2012, Scheduling. Theory, algorithms and systems. Springer-Verlag New York.
  • 28. Pytlak R., 2014, Optimization of sugar dispatching process, Research in Logistics & Production, 4(2), 105-118.
  • 29. Rodic B., 2017, Industry 4.0 and the New Simulation Modelling Paradigm, Organizacija, 50, 3, 193-207, https://doi.org/10.1515/orga-2017-0017
  • 30. Singh M., Fuenmayor E., Hinchy E.P., Qiao Y., Murray N., Devine D., 2021, Digital Twin: Origin to Future, Applied System Innovation, 4(2), 36, https://doi.org/10.3390/asi4020036
  • 31. Singh S., Shehab E., Higgins N., Fowler K., Tomiyama T., Fowler Ch., 2018, Challenges of Digital Twin in High Value Manufacturing, SAE Technical Paper, https://doi.org/10.4271/2018-01-1928
  • 32. Souza V., Cruz R., Silva W., Lins S., Lucena V., 2019, A Digital Twin Architecture Based on the Industrial Internet of Things Technologies, IEEE International Conference on Consumer Electronics (ICCE), 1-2, https://doi.org/10.1109/ICCE.2019.8662081
  • 33. Tabriz S., 2016, Juice extraction from sugar beet by pressing method, Journal of Eco-friendly Agriculture, October, 67–69.
  • 34. Taner T., Sivrioğlu M., Topal H., Dalkılıç A.S., Wongwises S., 2018, A model of energy management analysis, case study of a sugar factory in Turkey, Sadhana - Academy Proceedings in Engineering Sciences, 43(3), 1-20, https://doi.org/10.1007/s12046-018-0793-2
  • 35. Tao F., Cheng J., Qi Q.., Zhang M., Zhang H., Fanguyan S., 2017, Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology 94, 3563–3576 https://doi.org/10.1007/s00170-017-0233-1
  • 36. Tao F., Qi Q., Wang L., Nee A.Y.C., 2019, Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison, Engineering, 5, 4, 2019, 653-661, https://doi.org/10.1016/j.eng.2019.01.014
  • 37. Toivonen V., Lanz M., Nylund H., Nieminen H., 2018, The FMS Training Center - a versatile learning environment for engineering education, Procedia Manufacturing, 23, 135-140, https://doi.org/10.1016/j.promfg.2018.04.006
  • 38. Touil A., Echchatbi A., Charkaoui A., 2016, An MILP Model for Scheduling Multistage, Multiproducts Milk Processing, IFAC-PapersOnLine, 49(12), 869–874, https://doi.org/10.1016/j.ifacol.2016.07.884
  • 39. Um J., Popper J., Ruskowski M., 2018, Modular augmented reality platform for smart operator in production environment, IEEE Industrial Cyber-Physical Systems (ICPS), 720-725, https://doi.org/10.1109/ICPHYS.2018.8390796
  • 40. Urbaniec K., 2004, The evolution of evaporator stations in the beet-sugar industry, Journal of Food Engineering, 61(4), 505–508, https://doi.org/10.1016/S0260-8774(03)00218-8
  • 41. VanDerHorn E., Mahadevan S., 2021, Digital Twin: Generalization, characterization and implementation, Decision Support Systems, 145, 113524, https://doi.org/10.1016/j.dss.2021.113524
  • 42. Yao F., Keller A., Ahmad M., Ahmad B., Harrison R., Colombo A. W., 2018, Optimizing the Scheduling of Autonomous Guided Vehicle in a Manufacturing Process, IEEE 16th International Conference on Industrial Informatics (INDIN), 264-269, https://doi.org/10.1109/INDIN.2018.8471979
  • 43. Zhang H., Liu Q., Chen X., Zhang D., Leng J., 2017, A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line, IEEE Access, 5, 26901-26911, https://doi.org/10.1109/ACCESS.2017.2766453
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c62381c3-03db-4139-ad21-16b99ebe6cdc
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