Ontology-based multi-agent system for urban freight transportation

International Journal of Urban Sciences

Volumn 18, Issue 2, 2014, Pages 133-153

  Anand, Nilesh ^a   van Duin, Ron ^a   Tavasszy, Lori ^a,b  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b TNO   (Netherlands)

Author keywords

agent based model; city logistics; multi stakeholder; ontology

Indexed keywords

EID: 84904958263     PISSN: 12265934     EISSN: 21616779     Source Type: Journal    
DOI: 10.1080/12265934.2014.920696     Document Type: Article

References (29)

1. Ambrosini, C., & Routhier, J. (2004). Objectives, methods and results of surveys carried out in the field of urban freight transport: An international comparison. Transport Reviews, 24(1), 57-77. doi: 10.1080/0144164032000122343
2. Anand, N., Duin, J. H. R., & Tavasszy, L. (2014). Quality of semantic data for the city logistics domain: A comparison with the stakeholders' perspectives. Paper presented at the Transportation Research Board 93th Annual Meeting, Washington DC.
3. Anand, N., Quak, H., van Duin, R., & Tavasszy, L. (2012a). City logistics modeling efforts: Trends and gaps-a review. Procedia-Social and Behavioral Sciences, 39, 101-115. doi: 10.1016/j.sbspro.2012.03.094
4. Anand, N., Yang, M., van Duin, J. H. R., & Tavasszy, L. (2012b). GenCLOn: An ontology for city logistics. Expert Systems with Applications, 39(15), 11944-11960. http://dx.doi.org/10.1016/j.eswa.2012.03.068. doi: 10.1016/j.eswa.2012.03.068
5. Anand, N., Meijer, D., van Duin, J. H. R., & Tavasszy, L. (2013). Validation of an agent based model using participatory gaming approach: A case of city logistics. Proceedings of Conference on Agent-Based Modeling in Transportation Planning and Operations, Virginia Tech, Blacksburg, Virginia.
6. Axelrod, R. (2006). Agent-based modeling as a bridge between disciplines. Handbook of Computational Economics, 2, 1565-1584. doi: 10.1016/S1574-0021(05)02033-2
7. Bergkvist, M., Davidsson, P., Persson, J., & Ramstedt, L. (2005). A hybrid micro-simulator for determining the effects of governmental control policies on transport chains. Multi-Agent and Multi-Agent-Based Simulation, 3415, 236-247. doi: 10.1007/978-3-540-32243-6_19
8. BMJ Group. (2012). The t tests. Retrieved November 6, 2012, from http://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/7-t-tests.
9. Borshchev, A., & Filippov, A. (2004). From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. Paper presented at the Proceedings of the 22nd international conference of the system dynamics society. Retrieved from http://www.econ.iastate.edu/tesfatsi/systemdyndiscreteeventabmcompared.borshchevfilippov04.pdf
10. Crooks, A. T., Castle, C. J., & Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6), 417-430. doi: 10.1016/j.compenvurbsys.2008.09.004
11. Crainic, T., Ricciardi, N., & Storchi, G. (2009). Models for evaluating and planning City Logistics systems. Transportation science, 43(4), 432-454. doi: 10.1287/trsc.1090.0279
12. Davidsson, P., Henesey, L., Ramstedt, L., Törnquist, J., & Wernstedt, F. (2005). An analysis of agent-based approaches to transport logistics. Transportation Research Part C, 13(4), 255-271. doi: 10.1016/j.trc.2005.07.002
13. Donnelly, R. (2007). A hybrid microsimulation model of freight flows. Paper presented at the 5th international Conference on City Logistics, Kyoto, pp. 235-246.
14. Friesz, T. L., & Holguín-Veras, J. (2005). Dynamic game-theoretic models of urban freight: Formulation and solution approach. In A. Reggiani & L. A. Schintler (Eds.), Methods and Models in Transport and Telecommunications (pp. 143-161). Berlin: Springer.
15. Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing. Int. J. Hum.-Comput. Stud., 43(5-6), 907-928. doi: 10.1006/ijhc.1995.1081
16. Hunt, J. D., & Stefan, K. J. (2007). Tour-based microsimulation of urban commercial movements. Transportation Research Part B: Methodological, 41(9), 981-1013. doi: 10.1016/j.trb.2007.04.009
17. Kumar, V., & Srinivasan, S. (2010). A review of supply chain management using multi-agent system. International Journal of Computer Science, 7(5), 198-205.
18. Le Ber, F., & Chouvet, M. P. (1999). An agent-based model for domain knowledge representation. Data & Knowledge Engineering, 29(2), 147-161. doi: 10.1016/S0169-023X(98)00041-X
19. Liedtke, G., & Schepperle, H. (2004). Segmentation of the transportation market with regard to activity-based freight transport modelling. International Journal of Logistics Research and Applications, 7(3), 199-218. doi: 10.1080/13675560412331298464
20. OECD. (2003). Delivering the goods: 21st century challenges to urban goods transport. Paris: Author. Retrieved from http://www.internationaltransportforum.org/pub/pdf/03DeliveringGoods.pdf
21. Puckett, S. M., Hensher, D. A., Rose, J. M., & Collins, A. (2007). Design and development of a stated choice experiment for interdependent agents: Accounting for interactions between buyers and sellers of urban freight services. Transportation, 34(4), 429-451. doi: 10.1007/s11116-007-9114-z
22. Roorda, M., Cavalcante, R., McCabe, S., & Kwan, H. (2009). A conceptual framework for agent-based modelling of logistics services. Transportation Research Part E: Logistics and Transportation Review, 46(1), 18-31. doi: 10.1016/j.tre.2009.06.002
23. Russo, F., & Comi, A. (2006). Demand models for city logistics: a state of the art and a proposed integrated system. In E. Taniguchi & R. Thompson (Eds.), Recent advances in city logistics (pp. 91-105). Amsterdam: Elsevier.
24. Stevenson, G., & Dobson, S. (2011). Sapphire: Generating Java runtime artefacts from OWL ontologies. Paper presented at the Advanced Information Systems Engineering Workshops. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-22056-2_46
25. Swaminathan, J. M., Smith, S. F., & Sadeh, N. M. (1994). Modeling the dynamics of supply chains. Paper presented at the Proceedings of AAAI-94 SIGMAN Workshop on Intelligent Manufacturing Systems. Retrieved from http://www-preview.ri.cmu.edu/pub_files/pub2/swaminathan_j_m_1994_1/swaminathan_j_m_1994_1.pdf
26. Tamagawa, D., Taniguchi, E., & Yamada, T. (2010). Evaluating city logistics measures using a multi-agent model. Procedia - Social and Behavioral Sciences, 2(3), 6002-6012. doi: 10.1016/j.sbspro.2010.04.014
27. Taniguchi, E., & Tamagawa, D. (2005). Evaluating city logistics measures considering the behavior of several stakeholders. Journal of the Eastern Asia Society for Transportation Studies, 6, 3062-3076.
28. Terzi, S., & Cavalieri, S. (2004). Simulation in the supply chain context: a survey. Computers in Industry, 53(1), 3-16. doi: 10.1016/S0166-3615(03)00104-0
29. Thompson, R. G., & Taniguchi, E. (2001). City logistics and freight transport. In A. Brewer, K. J. Button, & D. A. Hensher (Eds.), Handbook of logistics and supply chain management (pp. 393-405). Amsterdam: Elsevier Science Ltd.