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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

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  • 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
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    References listed on IDEAS

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    1. Robert Axelrod, 1997. "Advancing the Art of Simulation in the Social Sciences," Working Papers 97-05-048, Santa Fe Institute.
    2. 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.
    3. 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.
    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.
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