Optimization of Lubricant Oil Filling and Packaging Lines: A Simulation-Based Automation Approach
How to cite (IJASEIT) :
Z. Li and M. N. Janardhanan, “Modelling and solving profit-oriented U-shaped partial disassembly line balancing problem,” Expert Syst. Appl., vol. 183, p. 115431, 2021, doi: 10.1016/j.eswa.2021.115431.
L. Hughes, Y. K. Dwivedi, N. P. Rana, M. D. Williams, and V. Raghavan, “Perspectives on the future of manufacturing within the Industry 4.0 era,” Prod. Plan. Control, vol. 33, no. 2–3, pp. 138–158, Feb. 2022, doi: 10.1080/09537287.2020.1810762.
J. Ribeiro, R. Lima, T. Eckhardt, and S. Paiva, “Robotic process automation and artificial intelligence in Industry 4.0—A literature review,” Procedia Comput. Sci., vol. 181, pp. 51–58, 2021, doi:10.1016/j.procs.2021.01.104.
M. S. Mira, “Applications of new technology in operations and supply chain management: Chatbots as recruiters and customer service,” in Handbook of Research on AI and ML for Intelligent Machines and Systems, IGI Global, 2024, p. 85, doi: 10.4018/979-8-3693-1578-1.ch005.
S. Kumar, M. Suhaib, M. Asjad, and B. Salah, “Industry 4.0 and its suitability in post COVID-19,” J. Ind. Integr. Manag., p. 2250012, May 2022, doi: 10.1142/S2424862222500129.
S. Matsuoka and T. Sawaragi, “Recovery planning of industrial robots based on semantic information of failures and time-dependent utility,” Adv. Eng. Inform., vol. 51, p. 101507, 2022, doi:10.1016/j.aei.2021.101507.
R. Remesan et al., “Modeling and management option analysis for saline groundwater drainage in a deltaic island,” Sustainability, vol. 13, no. 12, p. 6784, Jun. 2021, doi: 10.3390/su13126784.
M. Bohušík, V. Bulej, J. Stanček, D. Wiecek, J. Uríček, and M. Bartoš, “Concept of flexible transport system for components distribution within the production hall based on self-navigated mobile robot,” Transp. Res. Procedia, vol. 55, pp. 845–852, 2021, doi:10.1016/j.trpro.2021.07.053.
H. Yu, N. Can, Y. Wang, S. Wang, A. Ogbeyemi, and W. Zhang, “An integrated approach to line balancing for a robotic production system with the unlimited availability of human resources,” IFAC-PapersOnLine, vol. 55, no. 10, pp. 1098–1103, 2022, doi:10.1016/j.ifacol.2022.09.536.
J. Leng et al., “Industry 5.0: Prospect and retrospect,” J. Manuf. Syst., vol. 65, pp. 279–295, 2022, doi: 10.1016/j.jmsy.2022.09.017.
M. Tatasciore, V. Bowden, and S. Loft, “Do concurrent task demands impact the benefit of automation transparency?,” Appl. Ergon., vol. 110, p. 104022, 2023, doi: 10.1016/j.apergo.2023.104022.
F. Settanni et al., “Total value of ownership and overall equipment effectiveness analysis to evaluate the impact of automation on time and costs of therapeutic drug monitoring,” Anal. Chim. Acta, vol. 1160, p. 338455, 2021, doi: 10.1016/j.aca.2021.338455.
M. Wurster, B. Häfner, D. Gauder, N. Stricker, and G. Lanza, “Fluid automation—a definition and an application in remanufacturing production systems,” Procedia CIRP, vol. 97, pp. 508–513, 2021, doi: 10.1016/j.procir.2020.05.267.
N. Sahlab, N. Jazdi, and M. Weyrich, “An overview on designs and applications of context-aware automation systems,” Procedia Comput. Sci., vol. 207, pp. 2414–2423, 2022, doi: 10.1016/j.procs.2022.09.300.
H. G. Nguyen, M. Kuhn, and J. Franke, “Manufacturing automation for automotive wiring harnesses,” Procedia CIRP, vol. 97, pp. 379–384, 2021, doi: 10.1016/j.procir.2020.05.254.
O. E. Oluyisola, S. Bhalla, F. Sgarbossa, and J. O. Strandhagen, “Designing and developing smart production planning and control systems in the industry 4.0 era: A methodology and case study,” J. Intell. Manuf., vol. 33, no. 1, pp. 311–332, 2022, doi: 10.1007/s10845-021-01808-w.
B. K. Dey, S. Bhuniya, and B. Sarkar, “Involvement of controllable lead time and variable demand for a smart manufacturing system under a supply chain management,” Expert Syst. Appl., vol. 184, p. 115464, 2021, doi: 10.1016/j.eswa.2021.115464.
D. Braun, F. Biesinger, N. Jazdi, and M. Weyrich, “A concept for the automated layout generation of an existing production line within the digital twin,” Procedia CIRP, vol. 97, pp. 302–307, 2021, doi: 10.1016/j.procir.2020.05.242.
X. Yang, Z. Zhou, J. H. Sørensen, C. B. Christensen, M. Ünalan, and X. Zhang, “Automation of SME production with a Cobot system powered by learning-based vision,” Robot. Comput. Integr. Manuf., vol. 83, p. 102564, 2023, doi: 10.1016/j.rcim.2023.102564.
B. Wang, F. Tao, X. Fang, C. Liu, Y. Liu, and T. Freiheit, “Smart manufacturing and intelligent manufacturing: A comparative review,” Engineering, vol. 7, no. 6, pp. 738–757, Jun. 2021, doi:10.1016/j.eng.2020.07.017.
A. Napoleone, A.-L. Andersen, T. D. Brunoe, and K. Nielsen, “Towards human-centric reconfigurable manufacturing systems: Literature review of reconfigurability enablers for reduced reconfiguration effort and classification frameworks,” J. Manuf. Syst., vol. 67, pp. 23–34, 2023, doi: 10.1016/j.jmsy.2022.12.014.
M. Albonico, M. Đorđević, E. Hamer, and I. Malavolta, “Software engineering research on the Robot Operating System: A systematic mapping study,” J. Syst. Softw., vol. 197, p. 111574, 2023, doi:10.1016/j.jss.2022.111574.
K. Bhatta, C. Li, and Q. Chang, “Production loss analysis in mobile multi-skilled robot operated flexible serial production systems,” Manuf. Lett., vol. 33, pp. 835–842, 2022, doi:10.1016/j.mfglet.2022.07.103.
J. G. Enríquez, A. Jiménez-Ramírez, F. J. Domínguez-Mayo, and J. A. García-García, “Robotic process automation: A scientific and industrial systematic mapping study,” IEEE Access, vol. 8, pp. 39113–39129, 2020, doi: 10.1109/ACCESS.2020.2974934.
A. Grau, M. Indri, L. Lo Bello, and T. Sauter, “Robots in industry: The past, present, and future of a growing collaboration with humans,” IEEE Ind. Electron. Mag., vol. 15, no. 1, pp. 50–61, Mar. 2020, doi: 10.1109/MIE.2020.3008136.
J. Hernandez et al., “Current designs of robotic arm grippers: A comprehensive systematic review,” Robotics, vol. 12, no. 1, 2023, doi:10.3390/robotics12010005.
A. Dzedzickis, J. Subačiūtė-Žemaitienė, E. Šutinys, U. Samukaitė-Bubnienė, and V. Bučinskas, “Advanced applications of industrial robotics: New trends and possibilities,” Appl. Sci., vol. 12, no. 1, 2022, doi: 10.3390/app12010135.
J. Arents and M. Greitans, “Smart industrial robot control trends, challenges and opportunities within manufacturing,” Appl. Sci., vol. 12, no. 2, 2022, doi: 10.3390/app12020937.
R. Szczepanski, K. Erwinski, M. Tejer, A. Bereit, and T. Tarczewski, “Optimal scheduling for palletizing task using robotic arm and artificial bee colony algorithm,” Eng. Appl. Artif. Intell., vol. 113, p. 104976, 2022, doi: 10.1016/j.engappai.2022.104976.
N. M. Nawi, N. A. Hamid, N. A. Samsudin, Z. Harun, M. F. Ab Aziz, and A. A. Ramli, “Cause and effect prediction in manufacturing process using an improved neural networks,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 6, p. 2027, Jun. 2017, doi:10.18517/ijaseit.7.6.2384.
J. V. S. do Amaral, R. de Carvalho Miranda, J. A. B. Montevechi, C. H. dos Santos, and G. T. Gabriel, “Metamodeling-based simulation optimization in manufacturing problems: A comparative study,” Int. J. Adv. Manuf. Technol., vol. 120, no. 7, pp. 5205–5224, 2022, doi:10.1007/s00170-022-09072-9.
L. Wang, Z. Pan, and J. Wang, “A review of reinforcement learning based intelligent optimization for manufacturing scheduling,” Complex Syst. Model. Simul., vol. 1, no. 4, pp. 257–270, 2021, doi: 10.23919/CSMS.2021.0027.
X. Zhang, X. Ming, and Y. Bao, “A flexible smart manufacturing system in mass personalization manufacturing model based on multi-module-platform, multi-virtual-unit, and multi-production-line,” Comput. Ind. Eng., vol. 171, p. 108379, 2022, doi:10.1016/j.cie.2022.108379.
M. Al-kassab, “The use of one sample t-test in the real data,” J. Adv. Math., vol. 21, pp. 134–138, Jun. 2022, doi: 10.24297/jam.v21i.9279.
Q. Liu and L. Wang, “t-Test and ANOVA for data with ceiling and/or floor effects,” Behav. Res. Methods, vol. 53, no. 1, pp. 264–277, 2021, doi: 10.3758/s13428-020-01407-2.
J. Zhang and D. T. Robinson, “Replication of an agent-based model using the Replication Standard,” Environ. Model. Softw., vol. 139, p. 105016, 2021, doi: 10.1016/j.envsoft.2021.105016.

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).