ROBUST SIMULATION METHOD OF COMPLEX TECHNICAL TRANSPORT SYSTEMS

Publications

Share / Export Citation / Email / Print / Text size:

Transport Problems

Silesian University of Technology

Subject: Economics, Transportation, Transportation Science & Technology

GET ALERTS

eISSN: 2300-861X

DESCRIPTION

6
Reader(s)
6
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 16 , ISSUE 2 (June 2021) > List of articles

ROBUST SIMULATION METHOD OF COMPLEX TECHNICAL TRANSPORT SYSTEMS

Janusz SZPYTKO / Yorlandys SALGADO DUARTE *

Keywords : overhead cranes; Monte Carlo simulation; variance reduction; parallel computing

Citation Information : Transport Problems. Volume 16, Issue 2, Pages 101-112, DOI: https://doi.org/10.21307/tp-2021-026

License : (CC BY 4.0)

Received Date : 12-December-2019 / Accepted: 11-May-2021 / Published Online: 24-June-2021

ARTICLE

ABSTRACT

In the optimization of technical systems focused on a specific functional purpose (reliability, safety, and availability) with the use of simulation methods, an important parameter is the digital simulation time of the research subject. With the complexity of the issue, the digital simulation time increases. The aim of the article is to present a method (combination of parallel computing and variance reduction techniques) of reducing the computer simulation time of the research technical object. An example of the application of the developed method was presented as a result of an experiment conducted for decision making and control processes aimed at optimizing the process of operating overhead cranes in critical conditions. In this paper, selecting parallel batch jobs computation and stratified sampling, we exponentially decreased the simulation time, finding fast and practical solutions and eliminating the time constraint in the search of solutions.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

1. Alebrant Mendes, A. & Weber Lorenzoni, M. Analysis and optimization of periodic inspection intervals in cold standby systems using Monte Carlo simulation. Journal of Manufacturing Systems. 2018. Vol. 49. P. 121-130.

2. Botev, Z. & Ridder, A. Variance Reduction. Wiley StatsRef: Statistics Reference Online: 1-6. 2017.

3. Dieker, A.B. & Ghosh, S. & Squillante, M.S. Optimal resource capacity management for stochastic networks. Operations Research. 2017. Vol. 65(1). P. 221-241.

4. Fan, C. & Liao, Y. & Zhou, G. & Zhou, X. & Ding, Y. Improving cooling load prediction reliability for HVAC system using Monte-Carlo simulation to deal with uncertainties in input variables. Energy & Buildings. 2020. Vol. 226. No 110372.

5. Hubbard, D. & Samuelson, D.A. Modeling Without Measurements. 2009. OR/MS Today: 28-33.

6. Keyes, D. Parallel numerical algorithms: An introduction. In: Parallel Numerical Algorithms. Keyes D.E. & Sameh, A. & Venkatakrishnan, V. (Eds.). Kluwer Academic Publisher. Norwell, MA. 1997.

7. Kroese, D.P. & Brereton, T. & Taimre, T. & Botev, Z.I. Why the Monte Carlo method is so important today. WIREs Comput Stat. 2014. Vol. 6(6). P. 386-392.

8. Leahu, H. & Mandjes, M. & Oprescu, A.M. A numerical approach to stability of multiclass queueing networks. IEEE. Transactions on Automatic Control. 2017. Vol. 62(10). P. 5478-5484.

9. Mandjes, M. & Patch, B. & Walton, N.S. Detecting Markov chain instability: a Monte Carlo approach. Stochastic Systems. 2017. Vol. 7(2). P. 289-314.

10. MATLAB. version 9.7.9.1319299 (R2019b). Natick, Massachusetts: The MathWorks Inc. 2010.

11. Ozkan, O. & Kilic, S.A. Monte Carlo Simulation for Reliability Estimation of Logistics and Supply Chain Networks. IFAC PapersOnLine. 2019. Vol. 52(13). P. 2080-2085.

12. Rausch, C. & Nahangi, M. & Haas, C. & Liang, W. Monte Carlo simulation for tolerance analysis in prefabrication and offsite construction. Automation in Construction. 2019. Vol. 103. P. 300- 314.

13. Salgado Duarte, Y. & Szpytko, J. & del Castillo Serpa, A.M. Monte Carlo simulation model to coordinate the preventive maintenance scheduling of generating units in isolated distributed Power Systems. Electric Power Systems Research. 2020. Vol. 182. No. 106237.

14. Singh, R. & Singh Mangat, N. Elements of Survey Sampling. Springer Science + Business Media Dordrecht. 1996.

15. Spall, J.C. Estimation via Markov Chain Monte Carlo. IEEE Control Systems Magazine. 2003. Vol. 23(2). P. 34-45.

16. Szpytko, J. & Salgado Duarte, Y. Integrated maintenance platform for critical cranes under operation: Database for maintenance purposes. Proceeding of 4th IFAC Workshop on Advanced Maintenance Engineering. Services and Technologies Sept. 10-11, 2020. Cambridge, UK, IFAC PapersOnLine. 2020. Vol. 53(3). P. 167-172.

17. Szpytko, J. & Salgado Duarte, Y. Exploitation Efficiency System of Crane based on Risk Management. Proceeding of International Conference on Innovative Intelligent Industrial Production and Logistics. IN4PL 2020. 2-4 November 2020. ISBN: 978-989-758-476-3.

18. Trobec, R. & Vajtersic, M. & Zinterhof, P. (Eds.). Parallel Computing Numerics, Applications, and Trends. Springer. Dordrecht Heidelberg London New York. 2009.

 

EXTRA FILES

COMMENTS