Abstract
Decisions in factory layout planning can be considered multi-dimensional and complex since they need to cope with numerous partially conflicting boundary conditions and objectives. However, they do have a significant impact on long-term efficiency and flexibility. Due to rising needs in this area, an assisted solution for optimizing factory layout planning is required. Generative Design (GD) is a summarizing term for iterative, mostly nature-analogue approaches that support an efficient analysis of large design spaces, allowing to effortlessly achieve mathematically optimized solutions not usually achievable by traditional methods. Although there have been decades of research on the underlying principles, generative planning of spatial arrangements for manufacturing facilities still lacks behind its potential. Therefore, the proposed paper will begin with a structured overview of terminology and different factory planning requirements, followed by possible mathematical approaches for facility layout planning problems (FLP) in manufacturing. Special attention is drawn to sustainability aspects, defining the requirements to be considered in an automated design and including empirical knowledge in complex scenarios. The paper finishes with the derivation of identified future research areas.
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References
Michael Clauß: Methode zum Einsatz von Web 2.0-Werkzeugen in der Fabrikplanung (2013). https://monarch.qucosa.de/api/qucosa%3A19915/attachment/ATT-0/. Accessed 22 Feb 2022
VDI 5200 Blatt 1 Fabrikplanung—Planungsvorgehen, VDI-Gesellschaft Produktion und Logistik (Mar. 2011)
Augustin, H., Al-Shahmani, H., Dreßler, E., Krafzik, C., Liebler, C., Schmidt, P.: Gestaltung eines virtuellen Fabrikplanungsprozesses. ZWF 115(10), 659–662 (2020). https://doi.org/10.1515/zwf-2020-1151003. Accessed 3 Feb 2022
Reinema, C., Pompe, A., Nyhuis, P.: Agiles Projektmanagement. ZWF 108(3), 113–117 (2013). https://doi.org/10.3139/104.110903
Brunone, F., Cucuzza, M., Imperadori, M., Vanossi, A.: An innovative method for the management of the building process. In: Brunone, F., Cucuzza, M., Imperadori, M., Vanossi, A. (Eds.) Springer eBook collection, wood additive technologies: Application of active design optioneering, 1st ed., pp. 35–64. Springer, Cham (2021). Accessed 3 Mar 2022
Keshavarzi, M., Rahmani-Asl, M.: GenFloor: Interactive generative space layout system via encoded tree graphs. Front. Archit. Res. 10(4), 771–786 (2021). https://doi.org/10.1016/j.foar.2021.07.003. Accessed 1 Feb 2022
Süße, M., Putz, M.: Generative design in factory layout planning. Procedia CIRP 99, 9–14 (2021). https://doi.org/10.1016/j.procir.2021.03.002. Accessed 23 Mar 2022
Spacemaker A.S.: Spacemaker—AI architecture design | building information modelling. https://www.spacemakerai.com/. Accessed 4 Mar 2022
Elie, D., Kubicki, S.: Technologies in the planning of refugees’ camps: A parametric participative framework for spatial camp planning. In: 2017 IEEE Canada International Humanitarian Technology Conference (IHTC), Toronto, ON, Canada, Jul. 2017, pp. 207–212. Accessed 22 Feb 2022
Burggräf, P., Adlon, T., Hahn, V., Schulz-Isenbeck, T.: Fields of action towards automated facility layout design and optimization in factory planning—A systematic literature review. CIRP J. Manuf. Sci. Technol. 35, 864–871 (2021). https://doi.org/10.1016/j.cirpj.2021.09.013. Accessed 1 Mar 2022
Hosseini-Nasab, H., Fereidouni, S., Fatemi Ghomi, S.M.T., Fakhrzad, M.B.: Classification of facility layout problems: A review study. Int. J. Adv. Manuf. Technol. 94(1–4), 957–977 (2017). https://doi.org/10.1007/s00170-017-0895-8
Kusiak, A., Heragu, S. S.: The facility layout problem. Eur. J. Oper. Res. 29(3), 229–251 (1987). https://doi.org/10.1016/0377-2217(87)90238-4. Accessed 3 Dec 2021
Anjos, M.F., Vieira, M.V.: Mathematical optimization approaches for facility layout problems: The state-of-the-art and future research directions. Eur. J. Oper. Res. 261(1), 1–16 (2017). https://doi.org/10.1016/j.ejor.2017.01.049. Accessed 1 Mar 2022
Drira, A., Pierreval, H., Hajri-Gabouj, S.: Facility layout problems: A survey. Ann. Rev. Cont. 31(2), 255–267 (2007). https://doi.org/10.1016/j.arcontrol.2007.04.001. Accessed 11 Dec 2021
Pérez-Gosende, P., Mula, J., Díaz-Madroñero, M.: Facility layout planning. An extended literature review. Int. J. Prod. Res. 59(12), 3777–3816 (2021). https://doi.org/10.1080/00207543.2021.1897176. Accessed 15 Mar 2022
Burggräf, P., Wagner, J., Heinbach, B.: Bibliometric study on the use of machine learning as resolution technique for facility layout problems. IEEE Access 9, 22569–22586 (2021). https://doi.org/10.1109/ACCESS.2021.3054563
Burggräf, P., Dannapfel, M., Adlon, T., Kahmann, H., Schukat, E., Keens, J.: Capability-based assembly design: An approach for planning an agile assembly system in automotive industry. Procedia CIRP 93, 1206–1211 (2020). https://doi.org/10.1016/j.procir.2020.03.079. Accessed 21 Feb 2022
Kern, W., Lämmermann, H., Bauernhansl, T.: An integrated logistics concept for a modular assembly system. Procedia Manufac. 11, 957–964 (2017). https://doi.org/10.1016/j.promfg.2017.07.200. Accessed 22 Feb 2022
Neugebauer, R., Putz, M., Pfeifer, M., Todtermuschke, M.: Improving flexibility in car body assembly systems. CATS 2010: Responsive, costumer demand driven, adaptive assembly, pp. 115–120. Trondheim (2010). Accessed 11 Feb 2022
Elkington, J.: The triple bottom line. In: M. V. Russo (Ed.): Environmental management: Readings and cases, 2nd ed., pp. 49–66. SAGE, Los Angeles (2008). Accessed 15 Jan 2022
United Nations, THE 17 GOALS | Sustainable development. https://sdgs.un.org/goals. Accessed 4 Mar 2022
Müller, E., Engelmann, J., Löffler, T., Strauch, J.: Energieeffiziente Fabriken planen und betreiben. Springer Berlin Heidelberg, Berlin (2009)
Nielsen, L., et al.: Towards quantitative factory life cycle evaluation. Procedia CIRP 55, 266–271 (2016). https://doi.org/10.1016/j.procir.2016.08.009
Autodesk Inc.: About lighting analysis | Revit 2021 | Autodesk knowledge network. https://knowledge.autodesk.com/support/revit/learnexplore/caas/CloudHelp/cloudhelp/2021/ENU/Revit-Analyze/files/GUID-1F9669B3-338B-436D-B850-3FA4BC84A300-htm.html. Accessed 4 Mar 2022
Süße, M., Ihlenfeldt, S., Putz, M.: Framework for increasing sustainability of factory systems by generative layout design. Procedia CIRP 105, 345–350 (2022). https://doi.org/10.1016/j.procir.2022.02.057. Accessed 5 Apr 2022
Pelletier, N., Allacker, K., Manfredi, S., Chomkhamsri, K., Maia de Souza, D.: Organisation environmental footprint (OEF) guide. https://ec.europa.eu/environment/eussd/pdf/footprint/OEF%20Guide_final_July%202012_clean%20version.pdf. Accessed 19 Sep 2021
Stacey, R. D., Mowles, C.: Strategic management and organisational dynamics: The challenge of complexity to ways of thinking about organisations. Pearson Education, Harlow (2016). Accessed 28 Feb 2022
Zimmerman, B.: Ralph Staceyʼs agreement & certainty matrix. https://www.betterevaluation.org/en/resources/guide/ralph_staceys_agreement_and_certainty_matrix. Accessed 4 Mar 2022
Schwaber, K.: Agile project management with Scrum. Microsoft Press; Safari Books Online, Redmond (2004). https://learning.oreilly.com/library/view/-/9780735619937/?ar. Accessed 4 Mar 2022
neptune.ai: 10 real-life applications of reinforcement learning—neptune.ai. https://neptune.ai/blog/reinforcement-learning-applications. Accessed 4 Mar 2022
Arzate Cruz, C., Igarashi, T.: A survey on interactive reinforcement learning. In: Proceedings of the 2020 ACM designing interactive systems conference, Eindhoven, Netherlands, pp. 1195–1209 (2020). Accessed 11 Feb 2022
Arora, S., Doshi, P.: A survey of inverse reinforcement learning: Challenges, methods and progress. Artif. Intell. 297, 103500 (2021). https://doi.org/10.1016/j.artint.2021.103500. Accessed 25 Feb 2022
Padakandla, S.: A survey of reinforcement learning algorithms for dynamically varying environments. ACM Comput. Surv. 54(6), 1–25 (2021). https://doi.org/10.1145/3459991. Accessed 26 Feb 2022
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Süße, M., Ahrens, A., Richter-Trummer, V., Ihlenfeldt, S. (2023). Assisted Facility Layout Planning for Sustainable Automotive Assembly. In: Dröder, K., Vietor, T. (eds) Future Automotive Production Conference 2022. Zukunftstechnologien für den multifunktionalen Leichtbau. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-39928-3_13
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