Skip to main content
SpringerLink
Log in

An Agent-Based Approach to Multi-criteria Process Optimization in In-House Logistics

  • Conference paper
  • First Online:
Dynamics in Logistics

Part of the book series: Lecture Notes in Logistics ((LNLO))

  • 1982 Accesses

Abstract

One of the crucial enablers of the fourth industrial revolution is the implementation of autonomy in supply chains. Increased autonomy in logistics adds flexibility and robustness to supply chains. However, decentralized local decision making also creates new challenges since optimization problems now have to be solved in a decentralized manner. This research project proposes to apply agent technology to solve optimization problems in a distributed way in order to maintain efficiency while benifitting from the advantages of decentralization.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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.

    http://www.fipa.org/specs/fipa00061/index.html.

References

  • Brieber B, Szymanski L (2012) Autonomous logistics—potentials of multiagent-systems and advanced scenarios for virtual logistics lab 2.0. In: Warden T, Werthmann D, Uckelmann D, Lawo M (eds) Technical report 65. Technologie-Zentrum Informatik (TZI), Universität Bremen, Bremen, Germany

    Google Scholar 

  • Broy M (2010) Cyber-physical systems. Springer, Berlin

    Google Scholar 

  • Davis SM (1987) Future perfect. Addison Wesley

    Google Scholar 

  • FIPA ACL Message Structure Specification (Standard 00061G) (2002) Foundation for intelligent physical agents

    Google Scholar 

  • Fleisch E, Mattern F (eds) (2005) Das Internet der Dinge – Ubiquitous Computing und RFID in der Praxis. Springer, Berlin

    Google Scholar 

  • Fogliatto FS, da Silveira GJC, Borenstein D (2012) The mass customization decade: an updated review of the literature. Int J Prod Econ 138(1):14–25. doi:10.1016/j.ijpe.2012.03.002

    Article  Google Scholar 

  • Greulich C, Edelkamp S, Gath M (2013) Agent-based multimodal transport planning in dynamic environments. In: KI 2013: advances in artificial intelligence, pp 74–85

    Google Scholar 

  • Günthner WA, Wilke M, Zäh MF, Rudolf H (2006) Planung von Produktionsprozessen und Materialflusssteuerung. In: Lindemann U, Reichwald R, Zäh MF (eds) Individualisierte Produkte - Komplexität beherrschen in Entwicklung und Produktion. Springer, Berlin, pp 151–161

    Google Scholar 

  • Kagermann H, Wahlster W, Helbig J (2013) Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0. Abschlussbericht Des Arbeitskreises Industrie (April)

    Google Scholar 

  • Kaplan AM, Haenlein M (2006) Toward a Parsimonious definition of traditional and electronic mass customization. J Prod Innov Manage 23(2):168–182

    Article  Google Scholar 

  • Liu H, Zhao Q, Huang N, Zhao X (2013) A simulation-based tool for energy efficient building design for a class of manufacturing plants. IEEE Trans Autom Sci Eng 10(1):117–123. doi:10.1109/TASE.2012.2203595

    Article  Google Scholar 

  • Parunak HVD, Savit R, Riolo RL (1998) Agent-based modeling vs. equation-based modeling: a case study and users’ guide. In: Multi-agent systems and agent-based simulation, first international workshop (MABS), vol 1534. Springer, Berlin, pp 10–25

    Google Scholar 

  • Piller FT, Moeslein K, Stotko CM (2004) Does mass customization pay? An economic approach to evaluate customer integration. Prod Plann Control 15(4):435–444

    Article  Google Scholar 

  • Russell S, Norvig P (2004) Künstliche Intelligenz: Ein moderner Ansatz, 2nd edn. Pearson Studium, London

    Google Scholar 

  • Schwarz C, Hahn A, Sauer J (2012) Multi agent simulation of a warehouse. In: Proceedings of the 2012 international conference on logistics and maritime systems (LOGMS), pp 487–496

    Google Scholar 

  • Tu M, Lin J-H, Chen R-S, Chen K-Y, Jwo J-S (2009) Agent-based control framework for mass customization manufacturing with UHF RFID technology. Syst J IEEE 3(3):343–359

    Article  Google Scholar 

  • Wooldridge M, Jennings NR (1995) Intelligent agents: theory and practice. Knowl Eng Rev 10(02):115–152. doi:10.1017/S0269888900008122

    Article  Google Scholar 

  • Yao S, Han X, Yang Y, Rong (Kevin) Y, Huang SH, Yen DW, Zhang G (2007) Computer aided manufacturing planning for mass customization: part 2, automated setup planning. Int J Adv Manuf Technol 32(1–2):205–217

    Google Scholar 

  • Zäh MF, Rudolf H (2003) Computer aided process planning as an enabler for mass customization: state of the art and future areas for research. In: Reichwald R, Piller F, Tseng M (eds) Leading Mass Customization and Personalization from an Emerging Stage to a Mainstream Business Model. Proceedings of the MCPC 03: 2nd Interdisciplinary World Congress on Mass Customization and Personalization

    Google Scholar 

Download references

Acknowledgments

This research is supported by the International Graduate School for Dynamics in Logistics (IGS) at the University of Bremen.

Author information

Authors and Affiliations

  1. University of Bremen, Am Fallturm 1, 28359, Bremen, Germany

    Christoph Greulich

Authors
  1. Christoph Greulich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christoph Greulich .

Editor information

Editors and Affiliations

  1. Lehrstuhl für ABWL und Logistikmanagemen, University of Bremen, Bremen, Germany

    Herbert Kotzab

  2. University of Bremen, Bremen, Germany

    Jürgen Pannek

  3. Planung und Steuerung produktionstechnischer Systeme, Bremer Institut für Produktion und Logistik (BIBA), Bremen, Bremen, Germany

    Klaus-Dieter Thoben

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Greulich, C. (2016). An Agent-Based Approach to Multi-criteria Process Optimization in In-House Logistics. In: Kotzab, H., Pannek, J., Thoben, KD. (eds) Dynamics in Logistics. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-319-23512-7_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23512-7_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23511-0

  • Online ISBN: 978-3-319-23512-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation