IDEAS home Printed from https://ideas.repec.org/h/zbw/hiclch/209294.html
   My bibliography  Save this book chapter

Decision Support Platform for Urban Freight Transport

In: Operational Excellence in Logistics and Supply Chains: Optimization Methods, Data-driven Approaches and Security Insights. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 22

Author

Listed:
  • Quijano, Pedro David Guti

Abstract

Urban transport in cities is composed of many micro-transport systems. Each one of these has negative effect on the urban mobility. The study and analysis of one of these micro-transport systems is an effective way to understand the problem and consequently to propose new solutions, as well as a source of information to improve the urban transport system. In this paper we focus on the urban goods transport system that emerges from a particular supply chain, we support on a GIS (Geographic information system) computer simulation model to understand and analyze a case study. The simulation model developed is an example on how the abstraction approach of agent-based modeling (ABM) integrated with GIS can be used to represent the complexity inherent in the urban transport system. The novelty of our approach lies mainly in the fact of being able to analyze the combined effect between supply chain management and transportation system. The use of ABM allow us to model behaviors, negotiations and other social properties of the system's entities represented as agents, it can also help with understanding the emerging system's behaviors from an independent level. As a result of our study, the conceptual multi-agent model is presented and implemented in JAVA programing language, which is used for simulation experiments on the analysis of routing and departure time choice, with the goal to propose a plan to improve the micro-transport system under study.

Suggested Citation

  • Quijano, Pedro David Guti, 2015. "Decision Support Platform for Urban Freight Transport," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Blecker, Thorsten & Kersten, Wolfgang & Ringle, Christian M. (ed.), Operational Excellence in Logistics and Supply Chains: Optimization Methods, Data-driven Approaches and Security Insights. Proceedings of the Hamburg , volume 22, pages 419-440, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:209294
    DOI: 10.15480/882.1265
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/209294/1/hicl-2015-22-419.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.15480/882.1265?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    2. Robert Axelrod, 1997. "Advancing the Art of Simulation in the Social Sciences," Working Papers 97-05-048, Santa Fe Institute.
    3. Ivanov, Dmitry & Sokolov, Boris & Kaeschel, Joachim, 2010. "A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations," European Journal of Operational Research, Elsevier, vol. 200(2), pages 409-420, January.
    4. Chu, Chi-Leung & Leon, V. Jorge, 2008. "Single-vendor multi-buyer inventory coordination under private information," European Journal of Operational Research, Elsevier, vol. 191(2), pages 485-503, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    2. Matteo Richiardi, 2003. "The Promises and Perils of Agent-Based Computational Economics," LABORatorio R. Revelli Working Papers Series 29, LABORatorio R. Revelli, Centre for Employment Studies.
    3. Smith, Peter, 2008. "New perspectives on realism, tractability, and complexity in economics," MPRA Paper 10899, University Library of Munich, Germany.
    4. Hommes, Cars & Huang, Hai & Wang, Duo, 2005. "A robust rational route to randomness in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 29(6), pages 1043-1072, June.
    5. Youwei Li & Xue-Zhong He, 2005. "Long Memory, Heterogeneity, and Trend Chasing," Computing in Economics and Finance 2005 113, Society for Computational Economics.
    6. Brock, William A. & Hommes, Cars H. & Wagener, Florian O. O., 2005. "Evolutionary dynamics in markets with many trader types," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 7-42, February.
    7. Scott C. Linn & Nicholas S. P. Tay, 2007. "Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning," Management Science, INFORMS, vol. 53(7), pages 1165-1180, July.
    8. Nicolas Audet & Toni Gravelle & Jing Yang, 2002. "Alternative Trading Systems: Does One Shoe Fit All?," Staff Working Papers 02-33, Bank of Canada.
    9. Ivan Jericevich & Murray McKechnie & Tim Gebbie, 2021. "Calibrating an adaptive Farmer-Joshi agent-based model for financial markets," Papers 2104.09863, arXiv.org.
    10. Christian Peretti, 2007. "Long Memory and Hysteresis," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 363-389, Springer.
    11. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    12. Günter Küppers & Johannes Lenhard, 2005. "Validation of Simulation: Patterns in the Social and Natural Sciences," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-3.
    13. Andrew W. Bausch, 2014. "Evolving intergroup cooperation," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 369-393, December.
    14. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    15. Ulfia A. Lenfers & Julius Weyl & Thomas Clemen, 2018. "Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models," Land, MDPI, vol. 7(3), pages 1-17, August.
    16. Mikhail Anufriev & Giulio Bottazzi, 2005. "Price and Wealth Dynamics in a Speculative Market with an Arbitrary Number of Generic Technical Traders," LEM Papers Series 2005/06, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    17. Marco Casari, 2002. "Can genetic algorithms explain experimental anomalies? An application to common property resources," UFAE and IAE Working Papers 542.02, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    18. Alexander Ludwig & Alexander Zimper, 2013. "A decision-theoretic model of asset-price underreaction and overreaction to dividend news," Annals of Finance, Springer, vol. 9(4), pages 625-665, November.
    19. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    20. Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:hiclch:209294. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://hicl.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.