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Simulation optimization in the era of Industrial 4.0 and the Industrial Internet

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Journal of Simulation

Simulation is an established tool for predicting and evaluating the performance of complex stochastic systems that are analytically intractable. Recent research in simulation optimization and explosive growth in computing power have made it feasible to use simulations to optimize the design and operations of systems directly. Concurrently, ubiquitous sensing, pervasive computing, and unprecedented systems interconnectivity have ushered in a new era of industrialization (the so-called Industrial 4.0/Industrial Internet). In this article, we argue that simulation optimization is a decision-making tool that can be applied to many scenarios to tremendous effect. By capitalizing on an unprecedented integration of sensing, computing, and control, simulation optimization provides the “smart brain” required to drastically improve the efficiency of industrial systems. We explore the potential of simulation optimization and discuss how simulation optimization can be applied, with an emphasis on the recent development of multi-fidelity/multi-scale simulation optimization.

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Acknowledgments

This research is supported in part by the National Science Foundation under Grant No. CMMI-1233376, CMMI-1462787, and ECCS-1462409.

Statement of contribution

In this feature article, we argue that simulation optimization is a decision-making tool that can be applied to many scenarios to tremendous effect in the era of Industrial 4.0/Industrial Internet. By capitalizing on an unprecedented integration of sensing, computing, and control, simulation optimization provides the “smart brain” required to drastically improve the efficiency of industrial systems. We explore the potential of simulation optimization and discuss how simulation optimization can be applied, with an emphasis on the recent development of multi-fidelity/multi-scale simulation optimization.

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Authors and Affiliations

  1. Department of Systems Engineering and Operations Research, George Mason University, 4400 University Dr., MS 4A6, Engr Bldg, Rm 2100, Fairfax, VA, 22030, USA

    Jie Xu, Edward Huang, Liam Hsieh & Chun-Hung Chen

  2. National University of Singapore, Singapore, Singapore

    Loo Hay Lee

  3. Tsinghua University, Beijing, China

    Qing-Shan Jia

Authors
  1. Jie Xu
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  2. Edward Huang
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  3. Liam Hsieh
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  4. Loo Hay Lee
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  5. Qing-Shan Jia
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  6. Chun-Hung Chen
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Corresponding author

Correspondence to Jie Xu.

Appendix

Appendix

The total number of feasible designs depends on the number of work stations and machines. Define N(w,m) as the number of feasible designs where w is the number of work stations and m is the number of machines.

If we have only one workstation, the number of designs depends on the number of machines. If this workstation has between 5 and 10 machines, N(1,m) = 1; otherwise, N(1,m) = 0.

If w > 1, the number of feasible designs can be counted from the number of feasible designs with only w−1 workstations. The last workstation must have between 5 and 10 machines. Denote the number of machines assigned to the last workstation by j. Then, N(w, m) = ∑  10 j=5  N(w−1, m−j). For example, if w = 2 and m = 11, N(2,11) = N(1,6) + N(1,5) + N(1,4) + N(1,3) + N(1,2) + N(1,1) = 2. The first term, i.e., N(1,6), means the number of feasible designs if we assign 6 and 5 machines to the first and second workstation, respectively. In the problem setting described in Section 5, we have 5 workstations and 38 machines. Then, N(5,38) = N(4,33) + N(4,32) + N(4,31) + N(4,30) + N(4,29) + N(4,28). By using the forward induction, we can get that N(5,38) = 780. We enumerate all 780 solutions and list the first five and last five of these solutions in Table A1. The columns report the numbers of machines in workstation 1 (WS1) to workstation 5 (WS5).

Table A1 Examples of feasible solutions in the machine allocation problem
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Xu, J., Huang, E., Hsieh, L. et al. Simulation optimization in the era of Industrial 4.0 and the Industrial Internet. J Simulation 10, 310–320 (2016). https://doi.org/10.1057/s41273-016-0037-6

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  • DOI: https://doi.org/10.1057/s41273-016-0037-6

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