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A Novel Analysis Framework of 4.0 Production Planning Approaches – Part II

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Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future (SOHOMA 2021)

Abstract

The emergence of the Fourth Industrial Revolution has brought enterprises to review their production planning processes. Characterized by many technologies this revolution provides managers and planners with multiple means to increase productivity, get an added value from data mining processes and become more agile. This paper, divided into two parts, proposes an analysis framework to conduct a literature review of the production planning approaches developed during the 4th Industrial Revolution. This second part of the paper presents a summary of the contributions, a discussion of results, the gaps in literature and opportunities for further research. The results show that current production planning approaches do not exploit all the 4.0 tools and technologies; researchers usually employ CPS and Simulation. The results demonstrate that all the approaches followed some form of agility even though not all its dimensions have been pursued equally. Our results also indicate that production planning approaches are mainly focused on balancing resource utilization at operational planning level. Finally, the literature review showed that there are not real-case validations.

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References

  1. Dallasega, P., Rojas, R.A., Rauch, E., Matt, D.T.: Simulation based validation of supply chain effects through ICT enabled real-time-capability in ETO production planning. In: FAIM, pp. 846–853 (2017). https://doi.org/10.1016/j.promfg.2017.07.187

  2. Erol, S., Sihn, W.: Intelligent production planning and control in the cloud – towards a scalable software architecture. In: 10th CIRP Conf. on Intell. Comput. in Manufacturing Engineering, pp. 571–576. Ischia, Italy (2016). https://doi.org/10.1016/j.procir.2017.01.003

  3. Georgiadis, P., Michaloudis, C.: Real-time production planning and control system for job-shop manufacturing: a system dynamics analysis. Eur. J. Oper. Res. 216, 94–104 (2012). https://doi.org/10.1016/j.ejor.2011.07.022

    Article  MathSciNet  MATH  Google Scholar 

  4. Kumar, S., Purohit, B.S., Manjrekar, V., Singh, V., Kumar Lad, B.: Investigating the value of integrated operations planning: a case-based approach from automotive industry. Int. J. Prod. Res. 7543, 1–22 (2018). https://doi.org/10.1080/00207543.2018.1424367

    Article  Google Scholar 

  5. Lanza, G., Stricker, N., Moser, R.: Concept of an intelligent production control for global manufacturing in dynamic environments based on rescheduling. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 315–319. Malaysia (2014). https://doi.org/10.1109/IEEM.2013.6962425

  6. Jiang, Z., Jin, Y., Mingcheng, E., Li, Q.: Distributed dynamic scheduling for cyber-physical production systems based on a multi-agent system. IEEE Access 6, 1855–1869 (2017). https://doi.org/10.1109/ACCESS.2017.2780321

    Article  Google Scholar 

  7. Rajabinasab, A., Mansour, S.: Dynamic flexible job shop scheduling with alternative process plans: an agent-based approach. Int. J. Adv. Manuf. Technol. 54, 1091–1107 (2011). https://doi.org/10.1007/s00170-010-2986-7

    Article  Google Scholar 

  8. Pellerin, R.: The contribution of Industry 4.0 in creating agility within SMEs. In: Proceedings of the 2018 IRMBAM Conference, pp. 1–10. Nice, France (2018)

    Google Scholar 

  9. Berger, C., Zipfel, A., Braunreuther, S., Reinhart, G.: Approach for an event-driven production control for cyber-physical production systems. In: 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, pp. 349–354. Elsevier B.V., Naples, Italy (2019). https://doi.org/10.1016/j.procir.2019.02.085

  10. Tang, L., et al.: Online and offline based load balance algorithm in cloud computing. Knowl.-Based Syst. 138, 91–104 (2017). https://doi.org/10.1016/j.knosys.2017.09.040

    Article  Google Scholar 

  11. Wandt, R., Friedewald, A., Lödding, H.: Simulation aided disturbance management in one-of-a-kind production on the assembly site. In: IEEE Int. Conf. on Industrial Engineering and Eng. Management, pp. 503–507 (2012). https://doi.org/10.1109/IEEM.2012.6837790

  12. Shpilevoy, V., Shishov, A., Skobelev, P., Kolbova, E., Kazanskaia, D., Shepilov, Y., Tsarev, A.: Multi-agent system “Smart Factory” for real-time workshop management in aircraft jet engines production. In: 11th IFAC Workshop on Intelligent Manufacturing Systems, pp. 204–209. IFAC, São Paulo, Brazil (2013). https://doi.org/10.3182/20130522-3-BR-4036.00025

  13. Xu, Y., Chen, M.: Improving just-in-time manufacturing operations by using Internet of Things based solutions. In: 9th Int. Conference on Digital Enterprise Technology, pp. 326–331. Elsevier B.V. (2016). https://doi.org/10.1016/j.procir.2016.10.030

  14. Xu, Y., Chen, M.: An Internet of Things based framework to enhance just-in-time manufacturing. J. Eng. Manuf. 232, 2353–3263 (2017). https://doi.org/10.1177/0954405417731467

    Article  Google Scholar 

  15. Zhou, G., Zhang, C., Li, Z., Ding, K., Wang, C.: Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing. Int. J. Prod. Res. 7543(4), 1034–1051 (2019). https://doi.org/10.1080/00207543.2019.1607978

    Article  Google Scholar 

  16. Iannino, V., Vannocci, M., Vannucci, M., Colla, V., Neuer, M.: A multi-agent approach for the self-optimization of steel production. Int. J. Simul. Syst. Sci. Technol. 19, 1–7 (2018). https://doi.org/10.5013/IJSSST.a.19.05.20

    Article  Google Scholar 

  17. Lin, P., Li, M., Kong, X., Chen, J., Huang, G.Q., Wang, M.: Synchronization for smart factory – towards IoT-enabled mechanisms. Int. J. Comput. Integr. Manuf. 31, 624–635 (2018). https://doi.org/10.1080/0951192X.2017.1407445

    Article  Google Scholar 

  18. Mourtzis, D., Vlachou, E.: A cloud-based cyber-physical system for adaptive shop-floor scheduling and condition-based maintenance. J. Manuf. Syst. 47, 179–198 (2018). https://doi.org/10.1016/j.jmsy.2018.05.008

    Article  Google Scholar 

  19. Nonaka, Y., Suginishi, Y., Lengyel, A., Nagahara, S., Kamoda, K., Katsumura, Y.: The S-model: a digital manufacturing system combined with autonomous statistical analysis and autonomous discrete-event simulation for smart manufacturing. In: International Conference on Automation Science and Engineering, CASE 2015, pp. 1006–1011. Gothenburg, Sweden (2015). https://doi.org/10.1109/CoASE.2015.7294230

  20. Qu, S., Wang, J., Govil, S., Leckie, J.O.: Optimized adaptive scheduling of a manufacturing process system with multi-skill workforce and multiple machine types: an ontology-based, multi-agent reinforcement learning approach. In: 49th CIRP Conference on Manufacturing Systems, CIRP-CMS, pp. 55–60. Elsevier B.V., Stuttgart, Germany (2016). https://doi.org/10.1016/j.procir.2016.11.011

  21. Rossit, D.A., Tohmé, F., Frutos, M.: Industry 4.0: smart scheduling. Int. J. Prod. Res. 57, 3802–3813 (2019). https://doi.org/10.1080/00207543.2018.1504248

    Article  Google Scholar 

  22. Shamsuzzoha, A., Toscano, C., Carneiro, L.M., Kumar, V., Helo, P.: ICT-based solution approach for collaborative delivery of customized products. Prod. Plan. Control. 27, 280–298 (2016). https://doi.org/10.1080/09537287.2015.1123322

    Article  Google Scholar 

  23. Wang, X., Yew, A.W.W., Ong, S.K., Nee, A.Y.C.: Enhancing smart shop floor management with ubiquitous augmented reality. Int. J. Prod. Res. 7543(8), 2352–2367 (2019). https://doi.org/10.1080/00207543.2019.1629667

    Article  Google Scholar 

  24. Zhu, X., Qiao, F., Cao, Q.: Industrial big data-based scheduling modeling framework for complex manufacturing system. Adv. Mech. Eng. 9, 1–12 (2017). https://doi.org/10.1177/1687814017726289

    Article  Google Scholar 

  25. Asadzadeh, L.: A local search genetic algorithm for the job shop scheduling problem with intelligent agents. Comput. Ind. Eng. 85, 376–383 (2015). https://doi.org/10.1016/j.cie.2015.04.006

    Article  Google Scholar 

  26. Helo, P., Phuong, D., Hao, Y.: Cloud manufacturing – scheduling as a service for sheet metal manufacturing. Comput. Oper. Res. 110, 208–219 (2019). https://doi.org/10.1016/j.cor.2018.06.002

    Article  MathSciNet  MATH  Google Scholar 

  27. Waschneck, B., Reichstaller, A., Belzner, L., Altenmüller, T., Bauernhasl, T., Knapp, A., Kyek, A.: Optimization of global production scheduling deep reinforcement learning. In: 51st CIRP Conference on Manufacturing Systems, pp. 1264–1269. Stockholm, Sweden (2018). https://doi.org/10.1016/j.procir.2018.03.212

  28. Saif, U., Guan, Z., Wang, C., He, C., Yue, L., Mirza, J.: Drum buffer rope-based heuristic for multi-level rolling horizon planning in mixed model production. Int. J. Prod. Res. 57, 3864–3891 (2019). https://doi.org/10.1080/00207543.2019.1569272

    Article  Google Scholar 

  29. Shim, S., Park, K., Choi, S.: Sustainable production scheduling in open innovation perspective under the fourth industrial revolution. J. Open Innovation 4(4), 42 (2018). https://doi.org/10.3390/joitmc4040042

    Article  Google Scholar 

  30. Klein, M., et al.: A negotiation-based approach-based production International for scheduling. In: 28th Int. Conf. on Flexible Automation and Intelligent Manufacturing, FAIM 2018, pp. 334–341. Elsevier, Columbus, OH, USA (2018). https://doi.org/10.1016/j.promfg.2018.10.054

  31. Grundstein, S., Freitag, M., Scholz-Reiter, B.: A new method for autonomous control of complex job shops – integrating order release, sequencing and capacity control to meet due dates. J. Manuf. Syst. 42, 11–28 (2017). https://doi.org/10.1016/j.jmsy.2016.10.006

    Article  Google Scholar 

  32. Denno, P., Dickerson, C., Anne, J.: Dynamic production system identification for smart manufacturing systems. J. Manuf. Syst. 48, 1–11 (2018). https://doi.org/10.1016/j.jmsy.2018.04.006

    Article  Google Scholar 

  33. Engelhardt, P., Reinhart, G.: Approach for an RFID-based situational shop floor control. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 444–448 (2012). https://doi.org/10.1109/IEEM.2012.6837778

  34. Leusin, M.E., Kück, M., Frazzon, E.M., Maldonado, M.U., Freitag, M.: Potential of a multi-agent system approach for production control in smart factories. In: IFAC – Paper Online, 51, issue 11, pp. 1459–1464. Elsevier (2018). https://doi.org/10.1016/j.ifacol.2018.08.309

  35. Kuhnle, A., Röhrig, N., Lanza, G. Autonomous order dispatching in the semiconductor industry using reinforcement learning. In: 12th CIRP Conf. on Intell. Comput. in Manuf. Eng., pp. 391–396. Elsevier, Naples (2019). https://doi.org/10.1016/j.procir.2019.02.101

  36. Lachenmaier, J.F., Lasi, H., Kemper, H.G.: Simulation of production processes involving cyber-physical systems. In: 10th CIRP Conf. on Intelligent Computation in Manufacturing Engineering, pp. 577–582, Ischia (2017). https://doi.org/10.1016/j.procir.2016.06.074

  37. Gräler, I., Pöhler, A.: Intelligent devices in a decentralized production system concept. In: 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering, pp. 116–121. Naples (2018). https://doi.org/10.1016/j.procir.2017.12.186

  38. Kubo, R.H., Asato, O.L., Dos Santos, G.A., Nakamoto, F.Y.: Modeling of allocation control system of multifunctional resources for manufacturing systems. In: 12th IEEE Int. Conf. on Ind. App. INDUSCON (2016). https://doi.org/10.1109/INDUSCON.2016.7874596

  39. Lanza, G., Stricker, N., Peters, S.: Ad-hoc rescheduling and innovative business models for shock-robust production systems. In: 46th CIRP Conference on Manufacturing Systems, pp. 121–126. Elsevier (2013). https://doi.org/10.1016/j.procir.2013.05.021

  40. Li, Q., Wang, L., Shi, L., Wang, C.: A data-based production planning method for multi-variety and small-batch production. In: IEEE 2nd Int. Conf. on Big Data Analysis, ICBDA, pp. 420–425. Beijing, China (2017). https://doi.org/10.1109/ICBDA.2017.8078854

  41. Biesinger, F., Meike, D., Kraß, B., Weyrich, M.: A digital twin for production planning based on cyber-physical systems: a case study for a cyber-physical system-based creation of a digital twin. In: 12th CIRP Conf. on Intell. Comput. in Manufacturing Engineering, pp. 355–360. Elsevier, Naples (2019). https://doi.org/10.1016/j.procir.2019.02.087

  42. Graessler, I., Poehler, A.: Integration of a digital twin as human representation in a scheduling procedure of a cyber-physical production system. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 289–293. Singapore (2017). https://doi.org/10.1109/IEEM.2017.8289898

  43. Ruppert, T., Abonyi, J.: Industrial internet of things-based cycle time control of assembly lines. In: 2018 IEEE International Conference on Future IoT Technologies, Future IoT, pp. 1–4 (2018). https://doi.org/10.1109/FIOT.2018.8325590

  44. Fang, C., Liu, X., Pei, J., Fan, W., Pardalos, P.M.: Optimal production planning in a hybrid manufacturing and recovering system based on the internet of things with closed loop supply chains. Oper. Res. Int. J. 16(3), 543–577 (2015). https://doi.org/10.1007/s12351-015-0213-x

    Article  Google Scholar 

  45. Illmer, B., Vielhaber, M.: Virtual validation of decentrally controlled manufacturing systems with cyber-physical functionalities. In: 51st CIRP Conf. on Manufacturing Systems Virtual, pp. 509–514. Elsevier, Stockholm (2018). https://doi.org/10.1016/j.procir.2018.03.195

  46. Meyer, G.G., Hans Wortmann, J.C., Szirbik, N.B.: Production monitoring and control with intelligent products. Int. J. Prod. Res. 49, 1303–1317 (2011). https://doi.org/10.1080/00207543.2010.518742

    Article  Google Scholar 

  47. Um, J., Choi, Y.C., Stroud, I.: Factory planning system considering energy-efficient process under cloud manufacturing. In: 47th CIRP Conf. on Manufacturing Systems, pp. 553–558. Elsevier, Windsor, Canada (2014). https://doi.org/10.1016/j.procir.2014.01.084

  48. Autenrieth, P., Lörcher, C., Pfeiffer, C., Winkens, T., Martin, L., Overview, A.: Current significance of IT-infrastructure enabling industry 4.0 in large companies. In: IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC 2018) (2018). https://doi.org/10.1109/ICE.2018.8436244

  49. Sousa, R.A., Varela, M.L.R., Alves, C., Machado, J.: Job shop schedules analysis in the context of industry 4.0. In: 2017 International Conference on Engineering, Technology and Innovation ICE/ITMC, pp. 711–717 (2018). https://doi.org/10.1109/ICE.2017.8279955

  50. Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., Barbaray, R.: The industrial management of SMEs in the era of Industry 4.0. Int. J. Prod. Res. 56, 1118–1136 (2018). https://doi.org/10.1080/00207543.2017.1372647

    Article  Google Scholar 

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Correspondence to Estefania Tobon Valencia .

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Tobon Valencia, E., Lamouri, S., Pellerin, R., Moeuf, A. (2021). A Novel Analysis Framework of 4.0 Production Planning Approaches – Part II. In: Trentesaux, D., Borangiu, T., Leitão, P., Jimenez, JF., Montoya-Torres, J.R. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2021. Studies in Computational Intelligence, vol 987. Springer, Cham. https://doi.org/10.1007/978-3-030-80906-5_10

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