Skip to main content
SpringerLink
Log in

A Systematic Literature Review About Multi-objective Optimization for Distributed Manufacturing Scheduling in the Industry 4.0

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2022 Workshops (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13378))

Included in the following conference series:

Abstract

Multi-objective optimization problems are frequent in many engineering problems, namely in distributed manufacturing scheduling. In the current Industry 4.0 this kind of problems are becoming even more complex, due to the increase in data sets arising from the industry, thus requiring appropriate methods to solve them, in real time. In this paper, the results of a Systematic Literature Review are presented to reveal the state of the art in this scientific domain and identify the main research gaps in the current digitalization era. The results obtained allow to realize the importance of the multi-objective optimization approaches. Typically, when addressing large scale real problems, the existence of many objectives usually benefits with the establishment of some level of trade-off between objectives. In this paper, a summarized description and analysis is presented, related to several main issues arising currently in companies requiring the application of multi-objective optimization based distributed scheduling, for enabling them to fulfill requisites imposed by the Industry 4.0. In this context, issues related to energy consumption, among other customer-oriented objectives are focused to enable properly support decision-making through the analysis of a set of 33 main publications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.scopus.com.

  2. 2.

    https://www.webofscience.com.

  3. 3.

    https://www.b-on.pt.

References

  1. Okoli, C.: A guide to conducting a standalone systematic literature review. Commun. Assoc. Inf. Syst. 37, 879–910 (2015)

    Google Scholar 

  2. Thomé, A.M.T., Scavarda, L.F., Scavarda, A.J.: Conducting systematic literature review in operations management. Prod. Plan. Control 27, 408–420 (2016)

    Article  Google Scholar 

  3. Deb, K., Saxena, D.: Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. In: 2006 IEEE Congress on Evolutionary Computation (CEC 2006), pp. 3353–3360 (2006)

    Google Scholar 

  4. Patil, M.V., Kulkarni, A.J.: Pareto dominance based multiobjective cohort Intelligence algorithm. Inf. Sci. (Ny) 538, 69–118 (2020)

    Article  MathSciNet  Google Scholar 

  5. Santos, F., Costa, L.: Multivariate analysis to assist decision-making in many-objective engineering optimization problems. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12251, pp. 274–288. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58808-3_21

    Chapter  Google Scholar 

  6. Yang, M., Nazir, S., Xu, Q., Ali, S. Uddin, M.I.: Deep learning algorithms and multicriteria decision-making used in big data: a systematic literature review. Complexity 2020, 18 (2020). Article ID 2836064. https://doi.org/10.1155/2020/2836064

  7. Rymaszewski, S., Wątróbski, J., Karczmarczyk, A.: Identification of reference multi criteria domain model - production line optimization case study. Procedia Comput. Sci. 176, 3794–3801 (2020)

    Article  Google Scholar 

  8. Rocha, L.C.S., de Paiva, A.P., Rotela Junior, P., Balestrassi, P.P., da Silva Campos, P.H.: Robust multiple criteria decision making applied to optimization of AISI H13 hardened steel turning with PCBN wiper tool. Int. J. Adv. Manuf. Technol. 89, 2251–2268 (2017)

    Google Scholar 

  9. Varela, M.L.R., Silva, S.D.C.: An ontology for a model of manufacturing scheduling problems to be solved on the web. In: Azevedo, A. (ed.) Innovation in Manufacturing Networks. BASYS 2008. IFIP – The International Federation for Information Processing, vol. 266, pp. 197–204. Springer, Boston (2008). https://doi.org/10.1007/978-0-387-09492-2_21

  10. Varela, M.L.R., Trojanowska, J., Carmo-Silva, S., Costa, N.M.L., Machado, J.: Comparative simulation study of production scheduling in the hybrid and the parallel flow. Manag. Prod. Eng. Rev. 8, 69–80 (2017)

    Google Scholar 

  11. Zhang, J., Ding, G., Zou, Y., Qin, S., Fu, J.: Review of job shop scheduling research and its new perspectives under Industry 4.0. J. Intell. Manuf. 30(4), 1809–1830 (2017). https://doi.org/10.1007/s10845-017-1350-2

    Article  Google Scholar 

  12. Donato, H., Donato, M.: Stages for undertaking a systematic review. Acta Med. Port. 32, 227–235 (2019)

    Article  Google Scholar 

  13. Levy, Y., Ellis, T.J.: A systems approach to conduct an effective literature review in support of information systems research. Inf. Sci. J. 9, 181–212 (2006)

    Google Scholar 

  14. Bittencourt, V.L., Alves, A.C. Leão, C.P.: Industry 4.0 triggered by Lean thinking: insights from a systematic literature review. Int. J. Prod. Res. 59, 1496–1510 (2021)

    Google Scholar 

  15. Liu, T.K., Chen, Y.P., Chou, J.H.: Developing a multiobjective optimization scheduling system for a screw manufacturer: a refined genetic algorithm approach. IEEE Access 2, 356–364 (2014)

    Article  Google Scholar 

  16. Eftekharian, S.E., Shojafar, M., Shamshirband, S.: 2-Phase NSGA II: an optimized reward and risk measurements algorithm in portfolio optimization. Algorithms 10, 1–15 (2017)

    Article  MathSciNet  Google Scholar 

  17. Fu, Y., Ding, J., Wang, H., Wang, J.: Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system. Appl. Soft Comput. J. 68, 847–855 (2018)

    Google Scholar 

  18. Vahedi-Nouri, B., Tavakkoli-Moghaddam, R., Rohaninejad, M.: A multi-objective scheduling model for a cloud manufacturing system with pricing, equity, and order rejection. IFAC-PapersOnLine 52, 2177–2182 (2019)

    Article  Google Scholar 

  19. AbdelAziz, A.M., Soliman, T.H.A., Ghany, K.K.A., Sewisy, A.A.E.-M.: A Pareto-based hybrid whale optimization algorithm with Tabu search for multi-objective optimization. Algorithms 12, 261 (2019)

    Article  MathSciNet  Google Scholar 

  20. Guo, X., Gong, R., Bao, H., Lu, Z.: A multiobjective optimization dispatch method of wind-thermal power system. IEICE Trans. Inf. Syst. E103D, 2549–2558 (2020)

    Article  Google Scholar 

  21. Wen, X., Li, X., Gao, L., Wang, K., Li, H.: Modified honey bees mating optimization algorithm for multi-objective uncertain integrated process planning and scheduling problem. Int. J. Adv. Robot. Syst. 17, 1–17 (2020)

    Google Scholar 

  22. He, W., Li, W., Xu, S.: A Lyapunov drift-plus-penalty-based multi-objective optimization of energy consumption, construction period and benefit. KSCE J. Civ. Eng. 24(10), 2876–2889 (2020). https://doi.org/10.1007/s12205-020-2072-0

    Article  Google Scholar 

  23. Rubio, F., Llopis-Albert, C., Valero, F.: Multi-objective optimization of costs and energy efficiency associated with autonomous industrial processes for sustainable growth. Technol. Forecast. Soc. Change 173, 121115 (2021)

    Article  Google Scholar 

  24. Küster, T., Rayling, P., Wiersig, R., Pozo Pardo, F. D.: Multi-objective optimization of energy-efficient production schedules using genetic algorithms. Optim. Eng. (2021)

    Google Scholar 

  25. Yang, S., et al.: A novel maximin-based multi-objective evolutionary algorithm using one-by-one update scheme for multi-robot scheduling optimization. IEEE Access 9, 121316–121328 (2021)

    Article  Google Scholar 

  26. Joshi, M., Ghadai, R.K., Madhu, S., Kalita, K., Gao, X.Z.: Comparison of NSGA-II, MOALO and MODA for multi-objective optimization of micro-machining processes. Materials (Basel). 14, 1–16 (2021)

    Article  Google Scholar 

  27. He, L., Chiong, R., Li, W., Dhakal, S., Cao, Y., Zhang, Y.: Multiobjective optimization of energy-efficient job-shop scheduling with dynamic reference point-based fuzzy relative entropy. IEEE Trans. Ind. Inform. 18, 600–610 (2022)

    Article  Google Scholar 

  28. Qian, W., et al.: An improved MOEA/D algorithm for complex data analysis. Wirel. Commun. Mob. Comput. 2021, 20 (2021). Article ID 6393638. https://doi.org/10.1155/2021/6393638

  29. Fu, Y., Zhou, M., Guo, X., Qi, L.: Scheduling dual-objective stochastic hybrid flow shop with deteriorating jobs via bi-population evolutionary algorithm. IEEE Trans. Syst. Man Cybern. Syst. 50, 5037–5048 (2020)

    Google Scholar 

  30. Fang, Y., Ming, H., Li, M., Liu, Q., Pham, D.T.: Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time. Int. J. Prod. Res. 58, 846–862 (2020)

    Article  Google Scholar 

  31. Zhang, W., Yang, Y., Zhang, S., Yu, D., Li, Y.: Correlation-aware manufacturing service composition model using an extended flower pollination algorithm. Int. J. Prod. Res. 56, 4676–4691 (2018)

    Article  Google Scholar 

  32. Vaisi, B., Farughi, H., Raissi, S.: Schedule-allocate and robust sequencing in three-machine robotic cell under breakdowns. Math. Probl. Eng. 2020, 24 (2020). Article ID 4597827. https://doi.org/10.1155/2020/4597827

  33. Ji, W., Yin, S., Wang, L.: A big data analytics based machining optimisation approach. J. Intell. Manuf. 30(3), 1483–1495 (2018). https://doi.org/10.1007/s10845-018-1440-9

    Article  Google Scholar 

  34. Meng, K., Qian, X., Lou, P., Zhang, J.: Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study. J. Intell. Manuf. 31(1), 183–197 (2018). https://doi.org/10.1007/s10845-018-1439-2

    Article  Google Scholar 

  35. Ramezani, F., Lu, J., Taheri, J., Hussain, F.K.: Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments. World Wide Web 18(6), 1737–1757 (2015). https://doi.org/10.1007/s11280-015-0335-3

    Article  Google Scholar 

  36. Liu, J., Qiao, F., Kong, W.: Scenario-based multi-objective robust scheduling for a semiconductor production line. Int. J. Prod. Res. 57, 6807–6826 (2019)

    Article  Google Scholar 

  37. Fu, Y., Wang, H., Huang, M.: Integrated scheduling for a distributed manufacturing system: a stochastic multi-objective model. Enterp. Inf. Syst. 13, 557–573 (2019)

    Article  Google Scholar 

  38. Ghomi, E.J., Rahmani, A.M., Qader, N.N.: Service load balancing, task scheduling and transportation optimisation in cloud manufacturing by applying queuing system. Enterp. Inf. Syst. 13, 865–894 (2019)

    Article  Google Scholar 

  39. Coelho, P., Silva, C.: Parallel metaheuristics for shop scheduling: enabling Industry 4.0. Procedia Comput. Sci. 180, 778–786 (2021)

    Google Scholar 

  40. Fu, Y., Tian, G., Fathollahi-Fard, A.M., Ahmadi, A., Zhang, C.: Stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint. J. Clean. Prod. 226, 515–525 (2019)

    Article  Google Scholar 

  41. Afrin, M., Jin, J., Rahman, A., Tian, Y.C., Kulkarni, A.: Multi-objective resource allocation for Edge Cloud based robotic workflow in smart factory. Future Gener. Comput. Syst. 97, 119–130 (2019)

    Article  Google Scholar 

  42. Dziurzanski, P., Zhao, S., Przewozniczek, M., Komarnicki, M., Indrusiak, L.S.: Scalable distributed evolutionary algorithm orchestration using Docker containers. J. Comput. Sci. 40, 101069 (2020)

    Article  Google Scholar 

  43. Choi, T.M.: Guest editorial to the special issue on logistics and supply chain systems engineering. IEEE Trans. Syst. Man, Cybern. Syst. 50, 4852–4855 (2020)

    Google Scholar 

  44. Yetkin, E.B.: A multi-objective optimisation study for the design of an AVS/RS warehouse. Int. J. Prod. Res. 59, 1107–1126 (2021)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by national funds through the FCT-Fundação para a Ciência e Tecnologia through the R&D Units Project Scopes: UIDB/00319/2020, and EXPL/EME-SIS/1224/2021.

Author information

Authors and Affiliations

  1. Centro ALGORITMI, Universidade do Minho, 4710-057, Braga, Portugal

    Francisco dos Santos, Lino A. Costa & Leonilde Varela

  2. IP-UNIKIVI, Uíge, Angola

    Francisco dos Santos

Authors
  1. Francisco dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lino A. Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leonilde Varela
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francisco dos Santos .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Østfold University College, Halden, Norway

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  5. University of Cagliari, Cagliari, Italy

    Chiara Garau

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

dos Santos, F., Costa, L.A., Varela, L. (2022). A Systematic Literature Review About Multi-objective Optimization for Distributed Manufacturing Scheduling in the Industry 4.0. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13378. Springer, Cham. https://doi.org/10.1007/978-3-031-10562-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10562-3_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10561-6

  • Online ISBN: 978-3-031-10562-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation