Skip to main content
SpringerLink
Log in

Agent-Based Simulation for Indoor Manufacturing Environments—Evaluating the Effects of Spatialization

  • Conference paper
  • First Online:
Geospatial Technologies for Local and Regional Development (AGILE 2019)

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

Included in the following conference series:

Abstract

The paper elaborates on an Agent-based Modeling approach for an indoor manufacturing environment—in particular, a semiconductor production plant. In order to maintain a flexible production “line”, there is no conveyor belt, and a mix of different products is present in the indoor environment. With the integration of Industry 4.0 or Smart Manufacturing principles, production assets may be transported by autonomous robots in the near future. The optimization of manufacturing processes is challenging and computationally hard. Thus, simulation methods are used to optimize manufacturing plants and the processes. In contemporary literature, the effects of the spatial dimension with respect to the simulation of manufacturing processes is neglected. In this paper, we evaluate on the effects the spatial dimension in an Agent-based Model for indoor manufacturing environments. The Agent-based Model developed in this paper is utilized to simulate a manufacturing environment with the help of an artificial indoor space and a set of test data. Four simulation scenarios—with varying levels of spatial data usable—have been tested using Repast Simphony framework. The results reveal that different levels of available spatial information have an influence on the simulation results of indoor manufacturing environments and processes. First, the distances moved by the worker agents can be significantly reduced and the unproductive movements of worker agents (without production assets) can be decreased.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Repast Simphony 2.5: https://repast.github.io/download.html.

References

  • Albach H, Meffert H, Pinkwart A, Ralf R (2015) Management of permanent change. Springer, Berlin, pp 1–240

    Google Scholar 

  • Albach H, Meffert H, Pinkwart A, Reichwald R (2014) Management of permanent change—new challenges and opportunities for change management. Management of permanent change. Springer Fachmedien Wiesbaden, Wiesbaden, pp 3–21

    Google Scholar 

  • Almada-Lobo F (2016) The industry 4.0 revolution and the future of manufacturing execution systems (mes). J Innov Manag 3(4):16–21

    Article  Google Scholar 

  • Balasubramanian V (2018) Geospatial infrastructure, applications and technologies. India case studies. Ingist: a queryable and configurable indoorgis toolkit. Springer, Singapore, pp 93–105

    Book  Google Scholar 

  • Batty M, Crooks AT, See LM, Heppenstall AJ (2012) Perspectives on agent-based models and geographical systems. Agent-based models of geographical systems. Springer, Berlin, pp 1–15

    Google Scholar 

  • Bi Z, Da Xu L, Wang C (2014) Internet of things for enterprise systems of modern manufacturing. IEEE Trans Ind Inform 10(2):1537–1546

    Article  Google Scholar 

  • Brändle JM, Langendijk G, Peter S, Brunner SH, Huber R (2015) Sensitivity analysis of a land-use change model with and without agents to assess land abandonment and long-term re-forestation in a swiss mountain region. Land 4(2):475–512

    Article  Google Scholar 

  • Cantamessa M (1997) Agent-balsed modeling and management of manufacturing systems. Comput Ind 34(97):173–186

    Article  Google Scholar 

  • Cheema MA (2018) Indoor location-based services: challenges and opportunities. SIGSPATIAL Spec 10(2):10–17

    Article  Google Scholar 

  • Crooks A, Heppenstall A (2012) Introduction to agent-based modelling. Agent-based models of geographical systems. Springer, Berlin, pp 85–96

    Chapter  Google Scholar 

  • Diakité AA, Zlatanova S (2018) Spatial subdivision of complex indoor environments for 3d indoor navigation. Int J Geogr Inf Sci 32(2):213–235

    Article  Google Scholar 

  • Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1:269–271

    Article  Google Scholar 

  • Drath R, Horch A (2014) Industrie 4.0: hit or hype? [industry forum]. IEEE Ind Electron Mag 8:56–58

    Article  Google Scholar 

  • Geng H (2005) Semiconductor manufacturing handbook. McGraw-Hill, Inc, New York

    Google Scholar 

  • Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SE (2010) The ODD protocol: a review and first update. Ecol Model 221(23):2760–2768. https://doi.org/10.1016/j.ecolmodel.2010.08.019

    Article  Google Scholar 

  • Grimm V, Railsback SS (2012) Designing, formulating, and communicating agent-based models. Agent-based models of geographical systems. Springer, Berlin, pp 361–377

    Chapter  Google Scholar 

  • Henning K, Wolfgang W, Johannes H (2013) Recommendations for implementing the strategic initiative industrie 4.0. Final report of the Industrie 4.0 WG, 82

    Google Scholar 

  • Hermann M, Pentek T, Otto B (2016) Design principles for industrie 4.0 scenarios. In: Proceedings of the annual Hawaii international conference on system sciences, pp 3928–3937

    Google Scholar 

  • Jenkins PL, Phillips TJ, Mulberg EJ, Hui SP (1992) Activity patterns of californians: use of and proximity to indoor pollutant sources. Atmos Environment Part A Gen Top 26(12):2141–2148

    Article  Google Scholar 

  • Kagermann H (2014) Change through digitization—value creation in the age of industry 4.0. Management of permanent change. Springer Fachmedien Wiesbaden, Wiesbaden, pp 23–45

    Google Scholar 

  • Klepeis NE, Nelson WC, Ott WR, Robinson JP, Tsang AM, Switzer P, Behar JV, Hern SC, Engelmann WH et al (2001) The national human activity pattern survey (nhaps): a resource for assessing exposure to environmental pollutants. J Expo Anal Environ Epidemiol 11(3):231–252

    Article  Google Scholar 

  • Knoth L, Scholz J, Strobl J, Mittlböck M, Vockner B, Atzl C, Rajabifard A, Atazadeh B (2018) Cross-domain building models—a step towards interoperability. ISPRS Int J Geo-Inf 7(9):363

    Article  Google Scholar 

  • Leitão P (2009) Agent-based distributed manufacturing control: a state-of-the-art survey. Eng Appl Artif Intell 22(7):979–991

    Article  Google Scholar 

  • Macal CM, North MJ (2010) Tutorial on agent-based modelling and simulation. J Simul 4(3):151–162

    Article  Google Scholar 

  • Mandl P (2003) Multi-agenten-simulation und raum - spielwiese oder tragfähiger modellierungsansatz in der geographie. Klagenfurter Geographische Schriften 23:5–34

    Google Scholar 

  • Mattern F, Floerkemeier C (2010) From the internet of computers to the internet of things. From active data management to event-based systems and more, vol 6462. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer, Berlin, pp 242–259

    Chapter  Google Scholar 

  • Mazziotti BW, Horne RE Jr (1997) Creating a flexible, simulation-based finite scheduling tool. In: Proceedings of the 29th conference on winter simulation. IEEE Computer Society, pp 853–860

    Google Scholar 

  • Mönch L. Stehli M, Zimmermann J (2003) Fabmas: an agent-based system for production control of semiconductor manufacturing processes. In: International conference on industrial applications of holonic and multi-agent systems. Springer, pp 258–267

    Google Scholar 

  • Monostori L, Kumara S, Váncza J (2006) Agent-based systems for manufacturing. CIRP Ann Manuf Technol 55(2):697–720 http://www.sciencedirect.com/science/article/pii/S1660277306000053

    Article  Google Scholar 

  • 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 

  • Negmeldin MA, Eltawil A (2015) Agent based modeling in factory planning and process control. In: 2015 IEEE international conference on industrial engineering and engineering management (IEEM), pp 1810–1814

    Google Scholar 

  • North MJ, Collier NT, Ozik J, Tatara ER, Macal CM, Bragen M, Sydelko P (2013) Complex adaptive systems modeling with repast simphony. Complex Adapt Syst Model 1(1):3

    Article  Google Scholar 

  • Osswald S, Weiss A, Tscheligi M (2013) Designing wearable devices for the factory: rapid contextual experience prototyping. In: 2013 international conference on collaboration technologies and systems (CTS). IEEE, pp 517–521

    Google Scholar 

  • Paolucci M, Sacile R (2016) Agent-based manufacturing and control systems: new agile manufacturing solutions for achieving peak performance. CRC Press, Boca Raton

    Google Scholar 

  • Pinedo M (1995) Scheduling: theory, algorithms and applications. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Plattform Industrie 4.0 (2018) http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html

  • Raubal M (2001) Ontology and epistemology for agent-based wayfinding simulation. Int J Geogr Inf Sci 15(7):653–665

    Article  Google Scholar 

  • Raubal M, Worboys M (1999) A formal model of the process of wayfinding in built environments. Spatial information theory. cognitive and computational foundations of geographic information science. Springer, Berlin, pp 381–399

    Chapter  Google Scholar 

  • Schabus S, Scholz J, Lampoltshammer TJ (2017) Mapping parallels between outdoor urban environments and indoor manufacturing environments. ISPRS Int J Geo-Inf 6(9):281

    Article  Google Scholar 

  • Scholz J, Schabus S (2014) An indoor navigation ontology for production assets. In: Proceedings of the 8th international conference, GIScience 2014, Vienna, Austria, 24–26 September 2014, pp 204–220

    Google Scholar 

  • Scholz J, Schabus S, (2015) Geographic information science and technology as key approach to unveil the potential of industry 4.0. In: 2015 Proceedings of 12th International Conference on Informatics in Control, Automation and Robotic (ICINCO), vol.2, pp 463–470

    Google Scholar 

  • Scholz J, Schabus S (2017) Towards an affordance-based ad-hoc suitability network for indoor manufacturing transportation processes. ISPRS Int J Geo-Inf 6(9):280

    Article  Google Scholar 

  • Shrouf F, Ordieres J, Miragliotta G (2014) Smart factories in industry 4.0: a review of the concept and of energy management approached in production based on the internet of things paradigm. In: 2015 IEEE international conference on industrial engineering and engineering management, pp 697–701

    Google Scholar 

  • Van Berkel DB, Verburg PH (2012) Combining exploratory scenarios and participatory backcasting: using an agent-based model in participatory policy design for a multi-functional landscape. Landsc Ecol 27(5):641–658

    Article  Google Scholar 

  • Wang S, Wan J, Zhang D, Li D, Zhang C (2016) Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 101:158–168

    Article  Google Scholar 

  • Yang L, Worboys M (2015) Generation of navigation graphs for indoor space. Int J Geogr Inf Sci 29(10):1737–1756

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Geodesy, Graz University of Technology, Steyrergasse 30, A-8010, Graz, Austria

    Stefan Kern & Johannes Scholz

Authors
  1. Stefan Kern
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Johannes Scholz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefan Kern .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus

    Phaedon Kyriakidis

  2. Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus

    Diofantos Hadjimitsis

  3. Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus

    Dimitrios Skarlatos

  4. Department of Physical Geography and Ecosystem Science, Lund University GIS Center, Lund, Skåne Län, Sweden

    Ali Mansourian

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kern, S., Scholz, J. (2020). Agent-Based Simulation for Indoor Manufacturing Environments—Evaluating the Effects of Spatialization. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D., Mansourian, A. (eds) Geospatial Technologies for Local and Regional Development. AGILE 2019. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-14745-7_17

Download citation

Publish with us

Policies and ethics

Search

Navigation