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Towards Structured Performance Analysis of Industry 4.0 Workflow Automation Resources

Published:04 April 2019Publication History

ABSTRACT

Automation and the use of robotic components within business processes is in vogue across retail and manufacturing industries. However, a structured way of analyzing performance improvements provided by automation in complex workflows is still at a nascent stage. In this paper, we consider the common Industry 4.0 automation workflow resource patterns and model them within a hybrid queuing network. The queuing stations are replaced by scale up, scale out and hybrid scale automation patterns, to examine improvements in end-to-end process performance. We exhaustively simulate the throughput, response time, utilization and operating costs at higher concurrencies using Mean Value Analysis (MVA) algorithms. The queues are analyzed for cases with multiple classes, batch/transactional processing and load dependent service demands. These solutions are demonstrated over an exemplar use case of automation in Industry 4.0 warehouse automation workflows. A structured process of automation workflow performance analysis will prove valuable across industrial deployments.

References

  1. P. Leita, A. W. Colombo & S. Karnouskos, Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges", Computers in Industry, vol. 81, pp. 11--25, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. S. Greengard, "The Internet of Things", MIT, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Hermann, T. Pentek & B. Otto, "Design Principles for Industrie 4.0 Scenarios", 49th Hawaii Intl. Conf. on System Sciences, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Russell & P. Norvig, "Artificial Intelligence: A Modern Approach", Pearson, 3rd Ed., 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Bartholdi & S. Hackman, "Warehouse and Distribution Science", The Supply Chain and Logistics Institute, 2016.Google ScholarGoogle Scholar
  6. P. Wurman, R. D'Andrea & M. Mountz, "Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses", AAAI Artificial Intelligence Mag., vol. 29, no. 1, pp. 9--19, 2008.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M, Weske, "Business Process Management: Concepts, Languages, Architectures", Springer-Verlag Berlin Heidelberg, 2nd ed., 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Hao Zhang et al., "DoraPicker: An autonomous picking system for general objects", IEEE Intl. Conf. on Automation Science and Engineering (CASE), pp. 721--726, 2016.Google ScholarGoogle Scholar
  9. N. Russell, A.H.M. ter Hofstede, D. Edmond & W.M.P. van der Aalst, "Workflow Resource Patterns", BETA Working Paper Series -- Eindhoven University of Technology, WP 127, 2004.Google ScholarGoogle Scholar
  10. E. Lazowska, J. Zahorjan, S. Graham & K. Sevcik, "Quantitative System Performance: Computer System Analysis Using Queuing Network Models", Prentice-Hall, Inc., 1984. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. R. Ganeshan, "Managing supply chain inventories: A multiple retailer, one warehouse, multiple supplier model", Int. J. Production Economics, vol. 59, pp. 341--354, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  12. M. K. Govil & M. C. Fu, "Queuing theory in manufacturing: A survey", J. of Manufacturing Sys., vol. 18, no. 3, pp. 214--240, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  13. B. Schroeder, A. Wierman & M. Harchol-Balter, "Open Versus Closed: A Cautionary Tale", USENIX NSDI Tech. Paper, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. T. Lorido-Botran, J. Miguel-Alonso & J. Lozano, "A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments", vol. 12, no. 4, pp. 559--592, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. Kattepur, S. Dey & P. Balamuralidhar, "Knowledge Based Hierarchical Decomposition of Industry 4.0 Robotic Automation Tasks", IEEE Intl. Conf. on Industrial Electronics, 2018.Google ScholarGoogle Scholar
  16. A. Kattepur & M. Nambiar, "Performance Modeling of Multi-tiered Web Applications with Varying Service Demands", IPDPS Workshops, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. F. Basile, P. Chiacchio & J. Coppola, "A hybrid model of complex automated warehouse systems -- Part I: Modeling and simulation", IEEE Trans. on Automation Science and Engineering, vol. 9, no. 4, 2012.Google ScholarGoogle Scholar
  18. J. C. Hernandez-Matias, A. Vizan, J. Perez-Garcia & J. Rios, "An integrated modelling framework to support manufacturing system diagnosis for continuous improvement", Robotics and Computer-Integrated Manufacturing, vol. 24, pp. 187--199, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. W.M.P. van der Aalst, "Verification of Workflow Nets", Intl. Conf. on Application and Theory of Petri Nets, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. Kovacs and L. Gonczy, "Simulation and Formal Analysis of Workflow Models", Electronic Notes in Theoretical Computer Science, vol. 211, pp. 221--230, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. A. Kattepur, A. Mukherjee & P. Balamuralidhar, "Verification and Timing Analysis in Industry 4.0 Warehouse Automation Workflows", IEEE Intl. Conf. on Emerging Technologies and Factory Automation, 2018.Google ScholarGoogle Scholar

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            cover image ACM Conferences
            ICPE '19: Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering
            April 2019
            348 pages
            ISBN:9781450362399
            DOI:10.1145/3297663

            Copyright © 2019 ACM

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            Publication History

            • Published: 4 April 2019

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            ICPE '19 Paper Acceptance Rate13of71submissions,18%Overall Acceptance Rate252of851submissions,30%

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