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2024 | OriginalPaper | Chapter

Characteristics of Production Scheduling Problems in the Era of Industry 4.0 – A Review of Machine Learning Algorithms for Production Scheduling

Authors : Michael Groth, Matthias Schumann, Robert C. Nickerson

Published in: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems

Publisher: Springer Nature Switzerland

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Abstract

This chapter explores the characteristics of production scheduling problems in the Industry 4.0 era, focusing on the application of machine learning algorithms. It reviews existing literature and identifies research gaps, particularly in handling complex scheduling problems and the integration of practical constraints such as changeovers, buffers, prioritization, dynamics, and transportation. The chapter develops a taxonomy of machine learning algorithms for production scheduling and highlights the need for further research in open shop scheduling, reinforcement learning, multivariate optimization, and the creation of comprehensive scheduling algorithms and benchmark problems.

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Pinedo, M.L.: Scheduling. Springer International Publishing, Cham (2016).

Yang, W., Takakuwa, S.: Simulation-based dynamic shop floor scheduling for a flexible manufacturing system in the industry 4.0 environment. In: Chan, W.K.V. (ed.) Winter Simulation Conference. IEEE, pp. 3908–3916 (2017)

Schuh, G., Potente, T., Thomas, C., et al.: Web-based value stream oriented simulation of production control. In: Laroque, C. (ed.) 2012 Winter Simulation Conference. IEEE, pp. 1–10 (2012)

Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)CrossRef

Kück, M., Ehm, J., Hildebrandt, T., et al.: Potential of data-driven simulation-based optimization for adaptive scheduling and control of dynamic manufacturing systems. In: Roeder, T.M.K., Frazier, P.I., Szechtman, R., et al. (eds.) Winter Simulation Conference. IEEE, pp. 2820–2831 (2016)

Freier, P., Schumann, M.: Design and implementation of a decision support system for production scheduling in the context of cyber-physical systems. In: Gronau, N., Heine, M., Poustcchi, K., et al. (eds.) WI2020 Zentrale Tracks. GITO Verlag, pp. 757–773 (2020)

Lang, S., Behrendt, F., Lanzerath, N., et al.: Integration of deep reinforcement learning and discrete-event simulation for real-time scheduling of a flexible job shop production. In: 2020 Winter Simulation Conference (WSC). IEEE, pp. 3057–3068 (2020)

Branke, J., Nguyen, S., Pickardt, C.W., et al.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Computat. 20, 110–124 (2016)CrossRef

Lin, L., Gen, M.: Hybrid evolutionary optimisation with learning for production scheduling: state-of-the-art survey on algorithms and applications. Int. J. Prod. Res. 56, 193–223 (2018)CrossRef

10.

Cunha, B., Madureira, A.M., Fonseca, B., Coelho, D.: Deep reinforcement learning as a job shop scheduling solver: a literature review. In: Madureira, A.M., Abraham, A., Gandhi, N., Varela, M.L. (eds.) HIS 2018. AISC, vol. 923, pp. 350–359. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-14347-3_34CrossRef

11.

Fazel Zarandi, M.H., Sadat Asl, A.A., Sotudian, S., et al.: A state of the art review of intelligent scheduling. Artif. Intell. Rev. 53, 501–593 (2020)CrossRef

12.

Usuga Cadavid, J.P., Lamouri, S., Grabot, B., et al.: Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. J. Intell. Manuf. 31, 1531–1558 (2020)CrossRef

13.

Luo, D., Thevenin, S., Dolgui, A.: A state-of-the-art on production planning in Industry 4.0. International Journal of Production Research, pp. 1–31 (2022)

14.

Watson, R.T., Webster, J.: Analysing the past to prepare for the future: writing a literature review a roadmap for release 2.0. J. Decis. Syst. 29, 129–147 (2020)CrossRef

15.

vom Brocke, J., Simons, A., Niehaves, B., et al.: Reconstructing the giant: On the importance of rigour in documenting the literature search process. In: ECIS 2009 Proceedings (2009)

16.

Vela, C.R., Varela, R., González, M.A.: Local search and genetic algorithm for the job shop scheduling problem with sequence dependent setup times. J. Heuristics 16, 139–165 (2010)CrossRef

17.

Wong, W.K., Kwong, C.K., Mok, P.Y., et al.: Genetic optimization of JIT operation schedules for fabric-cutting process in apparel manufacture. J. Intell. Manuf. 17, 341–354 (2006)CrossRef

18.

Zhao, F., Zhang, Q., Yang, Y.: An improved particle swarm optimization (PSO) algorithm and fuzzy inference systems based approach to process planning and production scheduling integration in holonic manufacturing system (HMS). In: 2006 International Conference on Machine Learning and Cybernetics. IEEE, pp. 396–401 (2006)

19.

Tavakkoli-Moghaddam, R., Rahimi-Vahed, A.R., Mirzaei, A.H.: Solving a multi-objective no-wait flow shop scheduling problem with an immune algorithm. Int. J. Adv. Manuf. Technol. 36, 969–981 (2008)CrossRef

20.

Qu, S., Wang, J., Govil, S., et al.: Optimized adaptive scheduling of a manufacturing process system with multi-skill workforce and multiple machine types: an ontology-based, multiagent reinforcement learning approach. Procedia CIRP 57, 55–60 (2016)CrossRef

21.

Wadhwa, S., Madaan, J., Raina, R.: A genetic algorithm based scheduling for a flexible system. Global J. Flexible Syst. Manage. 8, 15–24 (2007)CrossRef

22.

Jun, S., Lee, S., Chun, H.: Learning dispatching rules using random forest in flexible job shop scheduling problems. Int. J. Prod. Res. 57, 3290–3310 (2019)CrossRef

23.

Niemeyer, G., Shiroma, P.: Production scheduling with genetic algorithms and simulation. In: Goos, G., Hartmanis, J., van Leeuwen, J., et al. (eds.) Parallel Problem Solving from Nature — PPSN IV, vol. 1141, pp. 930–939. Springer (1996)CrossRef

24.

Zhou, Y., Yang, J.-J.: Automatic design of scheduling policies for dynamic flexible job shop scheduling by multi-objective genetic programming based hyper-heuristic. Procedia CIRP 79, 439–444 (2019)CrossRef

25.

Zhang, R., Song, S., Wu, C.: Robust scheduling of hot rolling production by local search enhanced ant colony optimization algorithm. IEEE Trans. Ind. Inf. 16, 2809–2819 (2020)CrossRef

26.

Aytug, H., Bhattacharyya, S., Koehler, G.J.: Genetic learning through simulation: an investigation in shop floor scheduling. Ann. Oper. Res. 78, 1–29 (1998)CrossRef

27.

Li, W., Han, D., Gao, L., et al.: Integrated production and transportation scheduling method in hybrid flow shop. Chin J. Mech. Eng. 35 (2022)

28.

Zhao, Y., Wang, Y., Tan, Y., et al.: Dynamic jobshop scheduling algorithm based on deep Q network. IEEE Access 9, 122995–123011 (2021)CrossRef

29.

Marsland, S.: Machine Learning. Chapman and Hall/CRC (2011)

30.

Ning, G., Cao, D.: Multistep genetic algorithm for solving dynamic flexible job shop scheduling problem. In: International Conference on Advances in Electrical Engineering and Computer Applications. IEEE, pp. 23–28 (2021)

31.

Guo, K., Yang, M., Zhu, H.: Application research of improved genetic algorithm based on machine learning in production scheduling. Neural Comput. Appl. 32(7), 1857–1868 (2019)CrossRef

32.

Ma, Y., Qiao, F., Lu, J.: Learning-based dynamic scheduling of semiconductor manufacturing system. In: International Conference on Automation Science and Engineering. IEEE, pp. 1394–1399 (2016)

33.

Eguchi, T., Oba, F., Toyooka, S.: A robust scheduling rule using a neural network in dynamically changing job-shop environments. IJMTM 14, 266 (2008)CrossRef

34.

Scholz-Reiter, B., Heger, J., Hildebrandt, T.: Gaussian processes for dispatching rule selection in production scheduling: comparison of learning techniques. In: 2010 IEEE International Conference on Data Mining Workshops. IEEE, pp. 631–638 (2010)

35.

Ma, Y., Li, S., Lu, X., et al.: GAN based data analysis and mining for smart shop floor scheduling. In: 17th International Conference on Automation Science and Engineering. IEEE, pp. 651–656 (2021)

36.

Han, B.-A., Yang, J.-J.: Research on adaptive job shop scheduling problems based on dueling double DQN. IEEE Access 8, 186474–186495 (2020)CrossRef

37.

Liu, C.-L., Chang, C.-C., Tseng, C.-J.: Actor-critic deep reinforcement learning for solving job shop scheduling problems. IEEE Access 8, 71752–71762 (2020)CrossRef

38.

Yan, J., Liu, Z., Zhang, T., et al.: Autonomous decision-making method of transportation process for flexible job shop scheduling problem based on reinforcement learning. In: International Conference on Machine Learning and Intelligent Systems Engineering. IEEE, pp. 234–238 (2021)

39.

Nickerson, R.C., Varshney, U., Muntermann, J.: A method for taxonomy development and its application in information systems. Eur. J. Inf. Syst. 22, 336–359 (2013)CrossRef

40.

Kundisch, D., Muntermann, J., Oberländer, A.M., et al.: An update for taxonomy designers. Bus. Inf. Syst. Eng. 64, 421–439 (2022)CrossRef

Title: Characteristics of Production Scheduling Problems in the Era of Industry 4.0 – A Review of Machine Learning Algorithms for Production Scheduling
Authors: Michael Groth
Matthias Schumann
Robert C. Nickerson
Publisher: Springer Nature Switzerland
Book: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems
Print ISBN: 978-3-031-38164-5

Electronic ISBN: 978-3-031-38165-2

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-38165-2_15