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Ontology-based multi-agent system for urban freight transportation

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  • Nilesh Anand
  • Ron van Duin
  • Lori Tavasszy

Abstract

Congestion, pollution, and safety are some of the most worrisome side-effects of the urban goods movement activities. These problems are generally attributed to the underlying characteristics of the domain such as heterogeneous stakeholders, their conflicting objectives and resulting distributed decision-making. Such autonomous decision-making stakeholders do not efficiently cooperate and coordinate while performing city logistics activities. The ensuing inefficient use of resources (e.g. goods delivery vehicle, time, etc.) gives rise to the above-mentioned problems. To reduce the negative externalities of urban goods movement, we first must understand the decision-making process of the city logistics stakeholder under different situations. Agent-based simulation modelling technique is such an approach where distributed decision-making of the multiple stakeholders can be included by modelling each entity as an autonomous agent. In this paper, we propose the use of a knowledge data model of urban freight domain - city logistics ontology - to develop an agent-based model. City logistics ontology is a knowledge model which includes city logistics entities (e.g. stakeholders, resources, etc.) and relationships between them in a structured form. The paper focuses on the usefulness of ontology in the development of agent-based model for city logistics domain, and attempts to demonstrate the effectiveness of agent technology in analysing the urban freight decision-making processes.

Suggested Citation

  • Nilesh Anand & Ron van Duin & Lori Tavasszy, 2014. "Ontology-based multi-agent system for urban freight transportation," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(2), pages 133-153, July.
  • Handle: RePEc:taf:rjusxx:v:18:y:2014:i:2:p:133-153
    DOI: 10.1080/12265934.2014.920696
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    References listed on IDEAS

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    Cited by:

    1. Mathieu Gardrat & Pascal Pluvinet, 2021. "Markov based mesoscopic simulation tool for urban freight: SIMTURB," Working Papers halshs-03284321, HAL.
    2. Thoen, Sebastiaan & Tavasszy, Lóránt & de Bok, Michiel & Correia, Goncalo & van Duin, Ron, 2020. "Descriptive modeling of freight tour formation: A shipment-based approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    3. Anand, Nilesh & van Duin, Ron & Tavasszy, Lorant, 2021. "Carbon credits and urban freight consolidation: An experiment using agent based simulation," Research in Transportation Economics, Elsevier, vol. 85(C).
    4. Wooseok Do & Hyeongjun Park & Koohong Chung & Dongjoo Park, 2019. "An Effects Analysis of Logistics Collaboration: The Case of Pharmaceutical Supplies in Seoul," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    5. Pei Liu & Dong Mu & Daqing Gong, 2017. "Eliminating Overload Trucking via a Modal Shift to Achieve Intercity Freight Sustainability: A System Dynamics Approach," Sustainability, MDPI, vol. 9(3), pages 1-24, March.
    6. Amaya, Johanna & Delgado-Lindeman, Maira & Arellana, Julian & Allen, Jaime, 2021. "Urban freight logistics: What do citizens perceive?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    7. Firdausiyah, N. & Taniguchi, E. & Qureshi, A.G., 2019. "Modeling city logistics using adaptive dynamic programming based multi-agent simulation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 74-96.
    8. Sakai, Takanori & Romano Alho, André & Bhavathrathan, B.K. & Chiara, Giacomo Dalla & Gopalakrishnan, Raja & Jing, Peiyu & Hyodo, Tetsuro & Cheah, Lynette & Ben-Akiva, Moshe, 2020. "SimMobility Freight: An agent-based urban freight simulator for evaluating logistics solutions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    9. Liao, Haiyan & Holguín-Veras, José & Calderón, Oriana, 2023. "Comparative analysis of the performance of humanitarian logistic structures using agent-based simulation," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    10. Alena Brettmo & Jon Williamsson, 2020. "The Role of ‘Influencers’ as Drivers of a More Sustainable Urban Freight Sector," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    11. Priscila Pereira Suzart Carvalho & Ricardo Araújo Kalid & Jorge Laureano Moya Rodríguez & Sandro Breval Santiago, 2019. "Interactions among stakeholders in the processes of city logistics: a systematic review of the literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 567-607, August.
    12. Tavasszy, Lóránt A., 2020. "Predicting the effects of logistics innovations on freight systems: Directions for research," Transport Policy, Elsevier, vol. 86(C), pages 1-6.
    13. Sergio Maria Patella & Gianluca Grazieschi & Valerio Gatta & Edoardo Marcucci & Stefano Carrese, 2020. "The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review," Sustainability, MDPI, vol. 13(1), pages 1-29, December.
    14. Nilesh Anand & Ron van Duin & Hans Quak & Lori Tavasszy, 2015. "Relevance of City Logistics Modelling Efforts: A Review," Transport Reviews, Taylor & Francis Journals, vol. 35(6), pages 701-719, November.
    15. Giulio Mangano & Giovanni Zenezini & Anna Corinna Cagliano, 2021. "Value Proposition for Sustainable Last-Mile Delivery. A Retailer Perspective," Sustainability, MDPI, vol. 13(7), pages 1-15, March.

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