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A conceptual framework for agent-based modelling of logistics services

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  • Roorda, Matthew J.
  • Cavalcante, Rinaldo
  • McCabe, Stephanie
  • Kwan, Helen

Abstract

This paper presents an agent-based microsimulation framework that represents the diversity of roles and functions of actors in the freight system, how they interact through markets and how interactions between actors are established in markets through contracts. The framework provides sensitivity to technology trends, business trends, and policy scenarios. Logistics costs, outsourcing of logistics services to third party logistics firms, growth or retraction of various industry sectors, and the impact of new supply channels are explicitly represented. Data sources available in Toronto, Canada and new data collection efforts required for model estimation are described.

Suggested Citation

  • Roorda, Matthew J. & Cavalcante, Rinaldo & McCabe, Stephanie & Kwan, Helen, 2010. "A conceptual framework for agent-based modelling of logistics services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(1), pages 18-31, January.
  • Handle: RePEc:eee:transe:v:46:y:2010:i:1:p:18-31
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    Cited by:

    1. He, Zhou & Cheng, T.C.E. & Dong, Jichang & Wang, Shouyang, 2016. "Evolutionary location and pricing strategies for service merchants in competitive O2O markets," European Journal of Operational Research, Elsevier, vol. 254(2), pages 595-609.
    2. Edoardo Marcucci, Amanda Stathopoulos, Romeo Danielis, 2012. "Urban freight policies and tourism. The case of own-account operators in Rome’s limited traffic zone," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - Italian Review of Economics, Demography and Statistics, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 66(2), pages 121-139.
    3. Sharman, Bryce W. & Roorda, Matthew J., 2013. "Multilevel modelling of commercial vehicle inter-arrival duration using GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 94-107.
    4. repec:eee:transe:v:103:y:2017:i:c:p:69-86 is not listed on IDEAS
    5. Mosahar Tarimoradi & M. H. Fazel Zarandi & Hosain Zaman & I. B. Turksan, 0. "Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art," Journal of Intelligent Manufacturing, Springer, vol. 0, pages 1-29.
    6. Maggi, Elena & Vallino, Elena, 2016. "Understanding urban mobility and the impact of public policies: The role of the agent-based models," Research in Transportation Economics, Elsevier, vol. 55(C), pages 50-59.
    7. Gernot Liedtke & Hanno Friedrich, 2012. "Generation of logistics networks in freight transportation models," Transportation, Springer, vol. 39(6), pages 1335-1351, November.
    8. Gatta, Valerio & Marcucci, Edoardo, 2014. "Urban freight transport and policy changes: Improving decision makers' awareness via an agent-specific approach," Transport Policy, Elsevier, vol. 36(C), pages 248-252.
    9. Johan Joubert & Kay Axhausen, 2013. "A complex network approach to understand commercial vehicle movement," Transportation, Springer, vol. 40(3), pages 729-750, May.
    10. Theodore Tsekeris & Klimis Vogiatzoglou, 2011. "Spatial agent-based modeling of household and firm location with endogenous transport costs," Netnomics, Springer, vol. 12(2), pages 77-98, July.
    11. Vidal Vieira, José Geraldo & Fransoo, Jan C., 2015. "How logistics performance of freight operators is affected by urban freight distribution issues," Transport Policy, Elsevier, vol. 44(C), pages 37-47.
    12. Gerard Jong & Inge Vierth & Lori Tavasszy & Moshe Ben-Akiva, 2013. "Recent developments in national and international freight transport models within Europe," Transportation, Springer, vol. 40(2), pages 347-371, February.
    13. Niu, Yi-Feng & Lam, William H.K. & Gao, Ziyou, 2014. "An efficient algorithm for evaluating logistics network reliability subject to distribution cost," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 175-189.
    14. Mesa-Arango, Rodrigo & Ukkusuri, Satish V., 2014. "Attributes driving the selection of trucking services and the quantification of the shipper’s willingness to pay," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 142-158.
    15. Ruan, Minyan & Lin, Jie (Jane) & Kawamura, Kazuya, 2012. "Modeling urban commercial vehicle daily tour chaining," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(6), pages 1169-1184.
    16. Theodore Tsekeris & Klimis Vogiatzoglou & Stelios Bekiros, 2011. "Multi-Regional Agent-Based Modeling of Household and Firm Location Choices with Endogenous Transport Costs," ERSA conference papers ersa10p479, European Regional Science Association.

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