Value chains of Road Freight Transport operations: An agentbased modelling proposal
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2019
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Elsevier
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Salas, A., Cases, B., & García Palomares, J. C. (2019). Value chains of road freight transport operations: An agent-based modelling proposal. Procedia Computer Science, 151, 769-775. https://doi.org/10.1016/J.PROCS.2019.04.104
Abstract
Freight transport operations generate information flows and cause the creation of value by the agents that take part in them. These processes precede the flow of goods and continue until the end of the operation. The article proposes the design of an agent-based model with the structure of a directed graph to simulate the diffusion of information through it. The model considers the existence of five types of agents, three of them related to the transport activity and two external to it, all of them geographically located and interrelated by a certain distance. The goal of the model is to simulate the transfer of information between road freight transport agents through their operational decision making, in order to understand the behaviour of the variables that intervene in them and to assess the needed conditions for the successfully develop of the operation. This is a first step in the theoretical construction of the model, consequently provisional results have been omitted in this article.
Description
En: Procedia Computer Science, Volumen 151, 2019, páginas 769-775.
Tipo de fuente: Conference Proceeding
Tipo Aportación congreso / Conference Paper
The 8th International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS)
Part of special issue: The 10th International Conference on Ambient Systems, Networks and Technologies (ANT 2019) / The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40 2019) / Affiliated Workshops
Meeting10th International Conference on Ambient Systems, Networks and Technologies (ANT) / 2nd International Conference on Emerging Data and Industry 4.0 (EDI40).
LocationLeuven, BELGIUM.
DateAPR 29-MAY 02, 2019.
This research is funded by the Specific Research Fund (FEI17/34) of the Complutense University of Madrid and supported by the Guitrans Foundation, dedicated to the innovation and development of the road freight transport sector that includes more than 500 transport companies in the territory of Gipuzkoa, Basque Country.
Referencias bibliográficas:
• World Bank (2017). Measuring and analizing the impact of GVCs on economic developement: Global Value Chain development report 2017. World Bank Group: Washington.
• Roorda, M. J., Cavalcante, R., McCabe, S. & Kwan, H. (2010). A conceptual framework for agent-based modelling of logistics services. Transportation Research Part E: Logistics and Transportation Review, 46(1), 18-31.
• Ramstedt, L. & Woxenius, J. (2006). Modelling approaches to operational decision-making in freight transport chains. In Proc. 18th NOFOMA Conference, Oslo.
• Wooldridge, M. & Jennings, N. R. (1995). Intelligent agents: Theory and practice. The knowledge engineering review, 10(2), 115-152.
• Holmgren, J. (2008). Multi-agent-based simulation and optimization of production and transportation. (Doctoral thesis). Blekinge Institute of Technology.
• Ramstedt, L. (2008). Transport policy analysis using multi-agent-based simulation. (Doctoral thesis). Blekinge Institute of Technology.
• Davidsson, P., Holmgren, J., Persson, J. A. & Ramstedt, L. (2008). Multi agent based simulation of transport chains. In Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems-Volume 2 (pp. 1153-1160).
• Schröder, S., Zilske, M., Liedtke, G. & Nagel, K. (2012). A computational framework for a multi-agent simulation of freight transport activities. In Annual Meeting Preprint (pp. 12-4152).
• Cavalcante, R.A. (2013). Freight market interactions simulation (FREMIS): an agent-based modelling framework. (Doctoral thesis). University of Toronto.
• Matteis, T., Liedtke, G. & Wisetjindawat, W. (2016). A framework for incorporating market interactions in an agent based model for freight transport. Transportation Research Procedia, 12, 925-937.
• Démare, T. (2016). Une approche systémique à base d’agents et de graphes dynamiques pour modéliser l’interface logistique portmétropole. (Doctoral thesis). Normandie Université, Le Havre.
• Démare, T., Bertelle, C., Dutot, A. & Lévêque, L. (2017). Modeling logistic systems with an agent-based model and dynamic graphs. Journal of Transport Geography, 62, 51-65.
• Taillandier, P., Vo, D. A., Amouroux, E. & Drogoul, A. (2010). GAMA: a simulation platform that integrates geographical information data, agent-based modeling and multi-scale control. In International Conference on Principles and Practice of Multi-Agent Systems (pp. 242-258).
• Stojanović, Đ. (2017). Road freight transport outsourcing trend in Europe–what do we really know about it?. Transportation research procedia, 25, 772-793.
• Kaplinsky, R., & Morris, M. (2000). A handbook for value chain research (Vol. 113). University of Sussex, Institute of Development Studies.
• Brazinskas, S. & Beinoravičius, J. (2014). SMEs and integration driving factors to regional and global value chains. Procedia-Social and Behavioral Sciences, 110, 1033-1041.
• Rodrigue, J.P. (2017). The Geography of Transport Systems (4th Ed.). New York: Routledge
• Heuser, C. & Mattoo, A. (2017). Services Trade and Global Value Chains. In World Bank (ed.), Measuring and analyzing the impact of GVCs on economic development: Global Value Chain development report 2017. World Bank: Washington.
• Gavaud, O., Brehier, O., Guilbault, M. & Niérat, P. (2011). La sous-traitance dans le transport routier de marchandises: les enseignements de l’enquête ECHO (2004), Recherche Transports Sécurité, 27(2), 104-119.
• Reis, V. (2014). Analysis of mode choice variables in short-distance intermodal freight transport using an agent-based model. Transportation research part A: Policy and practice, 61, 100-120.
• Newman, M. E. (2003). The structure and function of complex networks. SIAM review, 45(2), 167-256.
• Argote, L. & Miron-Spektor, E. (2011). Organizational learning: From experience to knowledge. Organization science, 22(5), 1123-1137.
• Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL













