Metaheuristic algorithms and their applications in performance optimization of cyber-physical systems having applications in logistics

Monika Saini (), Vijay Singh Maan, Ashish Kumar () and Dinesh Kumar Saini
Additional contact information
Monika Saini: Manipal University Jaipur
Vijay Singh Maan: Manipal University Jaipur
Ashish Kumar: Manipal University Jaipur
Dinesh Kumar Saini: Manipal University Jaipur

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 6, No 16, 2202-2217

Abstract: Abstract In the industrial revolution 4.0 the applicability of cyber physical systems (CPS) extensively increased in various sectors including logistics, health, and manufacturing. The integration of CPS in logistics has revolutionized the way goods are transported, managed, and monitored. CPS combines digital and physical components to enhance the efficiency and effectiveness of logistics operations. However, to ensure the reliable and continuous operation of CPS, optimizing the availability of its subsystems is paramount. Hence, in present study, an effort is made to explore the applicability of metaheuristic approaches in the performance evaluation of cyber physical systems and a comprehensive evaluation framework is also proposed to compare the performance of metaheuristic algorithms. For this purpose, a Markov model of cyber physical system developed by considering constant failure and repair rates for all components. The failure and repair rates are followed exponential distribution. The concept of cold standby redundancy is utilized for two components including analog components and sensors & actuators unit. All components of cyber physical system can face failure during the working process and are perfectly repairable. The mathematical expression of system is derived and optimized by using metaheuristic approaches namely grey wolf optimization (GWO), cuckoo search algorithm (CS), dragonfly optimization (DA), grasshopper optimization algorithm (GOA) and cat swarm optimization (CSO). The numerical expression of the system availability obtained at various population sizes and estimated parameters derived. It is revealed from the numerical investigation that GWO and CSO outperform all optimization techniques. GWO attains its maximum availability 0.9983290 at population size 140 over 500 iterations and CSO attains its maximum availability 0.9983297 at population size 120 over 1000 iterations. The results of present study provide valuable insights into the strengths and weaknesses of each metaheuristic approach in the context of CPS subsystem optimization. This comparative analysis can guide logistics professionals and researchers in selecting the most suitable optimization algorithm for their specific CPS applications.

Keywords: Cyber-physical system; Availability; Markov birth–death process; Metaheuristic approaches (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-023-02236-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:6:d:10.1007_s13198-023-02236-0

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-023-02236-0

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().