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

  • Stefan KernEmail author
  • Johannes Scholz
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


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.


Indoor geography Smart manufacturing Industry 4.0 Agent-based modeling 


  1. Albach H, Meffert H, Pinkwart A, Ralf R (2015) Management of permanent change. Springer, Berlin, pp 1–240Google Scholar
  2. 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 3–21Google Scholar
  3. Almada-Lobo F (2016) The industry 4.0 revolution and the future of manufacturing execution systems (mes). J Innov Manag 3(4):16–21CrossRefGoogle Scholar
  4. Balasubramanian V (2018) Geospatial infrastructure, applications and technologies. India case studies. Ingist: a queryable and configurable indoorgis toolkit. Springer, Singapore, pp 93–105CrossRefGoogle Scholar
  5. 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 1–15Google Scholar
  6. Bi Z, Da Xu L, Wang C (2014) Internet of things for enterprise systems of modern manufacturing. IEEE Trans Ind Inform 10(2):1537–1546CrossRefGoogle Scholar
  7. 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):475–512CrossRefGoogle Scholar
  8. Cantamessa M (1997) Agent-balsed modeling and management of manufacturing systems. Comput Ind 34(97):173–186CrossRefGoogle Scholar
  9. Cheema MA (2018) Indoor location-based services: challenges and opportunities. SIGSPATIAL Spec 10(2):10–17CrossRefGoogle Scholar
  10. Crooks A, Heppenstall A (2012) Introduction to agent-based modelling. Agent-based models of geographical systems. Springer, Berlin, pp 85–96CrossRefGoogle Scholar
  11. Diakité AA, Zlatanova S (2018) Spatial subdivision of complex indoor environments for 3d indoor navigation. Int J Geogr Inf Sci 32(2):213–235CrossRefGoogle Scholar
  12. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1:269–271CrossRefGoogle Scholar
  13. Drath R, Horch A (2014) Industrie 4.0: hit or hype? [industry forum]. IEEE Ind Electron Mag 8:56–58CrossRefGoogle Scholar
  14. Geng H (2005) Semiconductor manufacturing handbook. McGraw-Hill, Inc, New YorkGoogle Scholar
  15. 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. Scholar
  16. Grimm V, Railsback SS (2012) Designing, formulating, and communicating agent-based models. Agent-based models of geographical systems. Springer, Berlin, pp 361–377CrossRefGoogle Scholar
  17. Henning K, Wolfgang W, Johannes H (2013) Recommendations for implementing the strategic initiative industrie 4.0. Final report of the Industrie 4.0 WG, 82Google Scholar
  18. 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 3928–3937Google Scholar
  19. 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):2141–2148CrossRefGoogle Scholar
  20. Kagermann H (2014) Change through digitization—value creation in the age of industry 4.0. Management of permanent change. Springer Fachmedien Wiesbaden, Wiesbaden, pp 23–45Google Scholar
  21. 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):231–252CrossRefGoogle Scholar
  22. 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):363CrossRefGoogle Scholar
  23. Leitão P (2009) Agent-based distributed manufacturing control: a state-of-the-art survey. Eng Appl Artif Intell 22(7):979–991CrossRefGoogle Scholar
  24. Macal CM, North MJ (2010) Tutorial on agent-based modelling and simulation. J Simul 4(3):151–162CrossRefGoogle Scholar
  25. Mandl P (2003) Multi-agenten-simulation und raum - spielwiese oder tragfähiger modellierungsansatz in der geographie. Klagenfurter Geographische Schriften 23:5–34Google Scholar
  26. 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 242–259CrossRefGoogle Scholar
  27. 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 853–860Google Scholar
  28. 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 258–267Google Scholar
  29. Monostori L, Kumara S, Váncza J (2006) Agent-based systems for manufacturing. CIRP Ann Manuf Technol 55(2):697–720 Scholar
  30. Negahban A, Smith JS (2014) Simulation for manufacturing system design and operation: literature review and analysis. J Manuf Syst 33(2):241–261CrossRefGoogle Scholar
  31. 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 1810–1814Google Scholar
  32. 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):3CrossRefGoogle Scholar
  33. 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 517–521Google Scholar
  34. Paolucci M, Sacile R (2016) Agent-based manufacturing and control systems: new agile manufacturing solutions for achieving peak performance. CRC Press, Boca RatonGoogle Scholar
  35. Pinedo M (1995) Scheduling: theory, algorithms and applications. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  36. Raubal M (2001) Ontology and epistemology for agent-based wayfinding simulation. Int J Geogr Inf Sci 15(7):653–665CrossRefGoogle Scholar
  37. 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 381–399Google Scholar
  38. Schabus S, Scholz J, Lampoltshammer TJ (2017) Mapping parallels between outdoor urban environments and indoor manufacturing environments. ISPRS Int J Geo-Inf 6(9):281CrossRefGoogle Scholar
  39. 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 204–220Google Scholar
  40. 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 463–470Google Scholar
  41. 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):280CrossRefGoogle Scholar
  42. 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 697–701Google Scholar
  43. 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):641–658CrossRefGoogle Scholar
  44. 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 101:158–168CrossRefGoogle Scholar
  45. Yang L, Worboys M (2015) Generation of navigation graphs for indoor space. Int J Geogr Inf Sci 29(10):1737–1756CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of GeodesyGraz University of TechnologyGrazAustria

Personalised recommendations