Real-time discrete event simulation: a framework for an intelligent expert system approach utilising decision trees

Abstract

This paper explores the use of discrete event simulation (DES) for decision making in real time based on the potential for data streamed from production line sensors. Technological innovations for data collection and an increasingly competitive global market have led to an increase in the application of discrete event simulation by manufacturing companies in recent years. Scenario analysis and optimisation methods are often applied to these simulation models to improve objectives such as cost, profit and throughput. The literature review has identified key research gaps as the lack of example cases where multi-objective optimisation methods have been applied to simulation models and the need for a framework to visualise the relationship between inputs and outputs of simulation models. A framework is presented to enable the optimisation DES simulation models and optimise multiple objectives simultaneously using design of experiments and meta-models to create a Pareto front of solutions. The results show that the resource allocation meta-model provides acceptable prediction accuracy whilst the lead time meta-model was not able to provide accurate prediction. Regression trees have been proposed to assist stakeholders with understanding the relationships between input and output variables. The framework uses regression and classification trees with overlaid values for multiple objectives and random forests to improve prediction accuracy for new points. A real-life test case involving a turbine assembly process is presented to illustrate the use and validity of the framework. The generated regression tree expressed a general trend by demonstrating relationships between input variables and two conflicting objectives. Random forests were implemented for creating higher accuracy predictions and they produced a mean square error of ~ 0.066 on the training data and ~ 0.081 on test data.

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Abbreviations

AI:

Artificial intelligence

CART:

Classification and regression tree

DES:

Discrete event simulation

DT:

Decision tree

FMS:

Flexible manufacturing system

IDEF:

Icam DEFinition for Function Modelling

IGRIP:

Interactive Graphical Robot Instruction Program

KPI:

Key performance indicator

NCR:

Non-conformance report

OLH:

Optimal Latin hypercube

References

  1. 1.

    Negahban A, Smith JS (2014) Simulation for manufacturing system design and operation: literature review and analysis. J Manuf Syst 33(2):241–261

    Article  Google Scholar 

  2. 2.

    Lu RF, Sundaram S (2002) Manufacturing process modeling of Boeing 747 moving line concepts. Winter Simulation Conference (WSC '02), 8–11 December, San Diego, California:1041–1045

  3. 3.

    Watson JD, Cross IJ ,Albiston JN (2010) Exploring and interpreting the solution space of a simulation. In: Operational Research Society Simulation Workshop, (SW10): 23–24 March 2010, Worcestershire, England, p 263–265

  4. 4.

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

    Article  Google Scholar 

  5. 5.

    Posada J, Toro C, Barandiaran I, Oyarzun D et al (2015) Visual computing as a key enabling technology for Industrie 4.0 and Industrial Internet. IEEE CG&A J:0272–1716/15

  6. 6.

    Turner C, Hutabarat W, Oyekan J, Tiwari A (2016) Discrete event simulation and virtual reality use in industry: new opportunities and future trends. IEEE Trans Hum Mach Syst 46(6):882–894

    Article  Google Scholar 

  7. 7.

    Korytkowski P, Wisniewski T, Rymaszewski S (2013) An evolutionary simulation-based optimization approach for dispatching scheduling. Simul Model Pract Theory 35:69–85

    Article  Google Scholar 

  8. 8.

    Kellner MI, Madachy RJ, Raffo DM (1999) Software process simulation modeling: why? What? How? J Syst Softw 46(2):91–105

    Article  Google Scholar 

  9. 9.

    Law AM, Kelton WD (2014) Simulation modelling and analysis, 5th edn. McGraw-Hill, New York

  10. 10.

    Lee K, Shin JG, Ryu C (2009) Development of simulation-based production execution system in a shipyard: a case study for a panel block assembly shop. Prod Plan Control 20(8):750–768

    Article  Google Scholar 

  11. 11.

    Ghani U, Monfared RP, Harrison R (2012) Energy optimisation in manufacturing systems using virtual engineering-driven discrete event simulation. Proc Inst Mech Eng B J Eng Manuf 226(11):1914–1929

    Article  Google Scholar 

  12. 12.

    Charnley F, Tiwari D, Hutabarat W, Moreno M, Okorie O, Tiwari A (2019) Simulation to enable a data-driven circular economy. Sustainability 11(12):3379–3395

    Article  Google Scholar 

  13. 13.

    Ferreira LP, Ares E, Peláez G, Resano A, Luis CJ, Tjahjono B (2012) Evaluation of the changes in working limits in an automobile assembly line using simulation. Manufacturing Engineering Society International Conference (MESIC 2011), 21–23 September, Cadiz:617–624

  14. 14.

    Nikoukaran J, Paul RJ (1999) Software selection for simulation in manufacturing: a review. Simul Pract Theory 7(1):1–14

    Article  Google Scholar 

  15. 15.

    Smith JS (2003) Survey on the use of simulation for manufacturing system design and operation. J Manuf Syst 22(2):157–171

    Article  Google Scholar 

  16. 16.

    Jahangirian M, Eldabi T, Naseer A, Stergioulas LK, Young T (2010) Simulation in manufacturing and business: a review. Eur J Oper Res 203(1):1–13

    Article  Google Scholar 

  17. 17.

    Alrabghi A, Tiwari A (2015) State of the art in simulation-based optimisation for maintenance systems. Comput Ind Eng 82:167–182

    Article  Google Scholar 

  18. 18.

    Sahu A, Pradhan SK (2016) Quantitative analysis and optimization of production line based on multiple evaluation criteria using discrete event simulation: A review. In: Proceedings of 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), 9–10 September, Pune, India, p 612–617

  19. 19.

    Kibira D, McLean C (2002) Virtual reality simulation of a mechanical assembly production line. Winter Simulation Conference (WSC '02), 8–11 December, San Diego, California:1130–1137

  20. 20.

    Lior R (2014) Data mining with decision trees: theory and applications. World scientific, Singapore

    Google Scholar 

  21. 21.

    Kearns M, Mansour Y (1999) On the boosting ability of top-down decision tree learning algorithms. J Comput Syst Sci 58:109–128

    MathSciNet  MATH  Article  Google Scholar 

  22. 22.

    Jiang W, Prasanna VK (2012) Scalable packet classification on FPGA. IEEE Trans Very Large Scale Integr (VLSI) Syst 20:1668–1680

    Article  Google Scholar 

  23. 23.

    Kumar R, Singh B, Shahani DT, Chandra A, Al-Haddad K (2015) Recognition of power-quality disturbances using S-transform-based ANN classifier and rule-based decision tree. IEEE Trans Ind Appl 51:1249–1258

    Article  Google Scholar 

  24. 24.

    Deradjat D, Minshall T (2018) Decision trees for implementing rapid manufacturing for mass customisation. CIRP J Manuf Sci Technol 23:156–171. https://doi.org/10.1016/j.cirpj.2017.12.003

    Article  Google Scholar 

  25. 25.

    Priore P, Ponte B, Puente J, Gómez A (2018) Learning-based scheduling of flexible manufacturing systems using ensemble methods. Comput Ind Eng 126:282–291. https://doi.org/10.1016/j.cie.2018.09.034

    Article  Google Scholar 

  26. 26.

    Kim A, Oh K, Jung JY, Kim B (2018) Imbalanced classification of manufacturing quality conditions using cost-sensitive decision tree ensembles. Int J Comput Integr Manuf 31(8):701–717. https://doi.org/10.1080/0951192X.2017.1407447

    Article  Google Scholar 

  27. 27.

    Khemiri A, El Amine Hamri M, Frydman C, Pinaton J (2018) Improving business process in semiconductor manufacturing by discovering business rules, In Rabe M, et al. eds. Proceedings of the 2018 Winter Simulation Conference

  28. 28.

    Cupek R, Ziebinski A, Drewniak M (2018) Application of decision trees for quality management support. In: Nguyen N, Pimenidis E, Khan Z, Trawiński B (eds) Computational collective intelligence. ICCCI 2018, Lecture Notes in Computer Science, vol 11056. Springer, Cham

    Google Scholar 

  29. 29.

    Zeng X, Wong W-K, Leung SY-S (2012) An operator allocation optimization model for balancing control of the hybrid assembly lines using Pareto utility discrete differential evolution algorithm. Comput Oper Res 39(5):1145–1159

    Article  Google Scholar 

  30. 30.

    Sadeghian O, Oshnoei A, Nikkhah S, Mohammadi-Ivatloo B (2019) Multi-objective optimisation of generation maintenance scheduling in restructured power systems based on global criterion method. IET Smart Grid 2(2):203–213

    Article  Google Scholar 

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Funding

Ashutosh Tiwari received support by Airbus and the Royal Academy of Engineering under the Research Chairs and Senior Research Fellowships scheme (RCSRF1718\5\41).

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Correspondence to C. Turner.

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Prajapat, N., Turner, C., Tiwari, A. et al. Real-time discrete event simulation: a framework for an intelligent expert system approach utilising decision trees. Int J Adv Manuf Technol (2020). https://doi.org/10.1007/s00170-020-06048-5

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Keywords

  • Simulation
  • Discrete event simulation (DES)
  • Expert system
  • Decision tree
  • Random forest
  • Flexible manufacturing system