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Parallel Agent-Based Framework for Analyzing Urban Agricultural Supply Chains

Author

Listed:
  • Manuel Ignacio Manríquez

    (Centre for Biotechnology and Engineering (CeBiB), Departamento de Ingeniería Informática, Universidad de Santiago, Santiago 15782, Chile)

  • Veronica Gil-Costa

    (CONICET, Facultad de Cs. Fisico Matematicas y Naturales, Universidad Nacional de San Luis, San Luis 5700, Argentina)

  • Mauricio Marin

    (Centre for Biotechnology and Engineering (CeBiB), Departamento de Ingeniería Informática, Universidad de Santiago, Santiago 15782, Chile)

Abstract

This work presents a parallel agent-based framework designed to analyze the dynamics of vegetable trade within a metropolitan area. The system integrates agent-based and discrete event techniques to capture the complex interactions among farmers, vendors, and consumers in urban agricultural supply chains. Decision-making processes are modeled in detail: farmers select crops based on market trends and environmental risks, while vendors and consumers adapt their purchasing behavior according to seasonality, prices, and availability. To efficiently handle the computational demands of large-scale scenarios, we adopt an optimistic approximate parallel execution strategy. Furthermore, we introduce a credit-based load balancing mechanism that mitigates the effects of heterogeneous communication patterns and improves scalability. This framework enables detailed analysis of food distribution systems in urban contexts, offering insights relevant to smart cities and digital agriculture initiatives.

Suggested Citation

  • Manuel Ignacio Manríquez & Veronica Gil-Costa & Mauricio Marin, 2025. "Parallel Agent-Based Framework for Analyzing Urban Agricultural Supply Chains," Future Internet, MDPI, vol. 17(7), pages 1-27, July.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:7:p:316-:d:1705318
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    References listed on IDEAS

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    1. Lalendra Gurung & Janak Singh Rawal & Puspa RC & Ganesh Raj Joshi & Ashmita Mandal, 2024. "Vertical Farming In Urban Agriculture: Opportunities, Challenges, And Future Directions," Big Data In Agriculture (BDA), Zibeline International Publishing, vol. 6(2), pages 106-112, July.
    2. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    3. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
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