Discrete Event Simulation Based on a Multi-Agent System for Japanese Rice Harvesting Operations

Malte Grosse (), Kiyoshi Honda, Peter Thies and Cornelius Specht
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Malte Grosse: Graduate School of Engineering, Chubu University, 1200 Matsumoto, Aichi Prefecture, Kasugai 487-0027, Japan
Kiyoshi Honda: Graduate School of Engineering, Chubu University, 1200 Matsumoto, Aichi Prefecture, Kasugai 487-0027, Japan
Peter Thies: Institute for Applied Artificial Intelligence, Hochschule der Medien (Stuttgart Media University), Nobelstraße 10, 70569 Stuttgart, Germany
Cornelius Specht: Institute for Applied Artificial Intelligence, Hochschule der Medien (Stuttgart Media University), Nobelstraße 10, 70569 Stuttgart, Germany

Agriculture, 2025, vol. 15, issue 16, 1-14

Abstract: Existing rice harvesting models often lack depth or extensibility and are limited in their scope across the agriculture value chain, from crop planting to postharvest handling. A multi-agent system (MAS) offers flexibility and scalability and supports the simulation and modeling of complex real-world scenarios. This paper introduces a novel approach utilizing an MAS to simulate rice harvesting operations (including additional pre- and post-harvesting operations). Initially, a generic MAS was created, and it was then subsequently adapted to the agricultural context of rice farming in Central Japan. The localized MAS consists of agents such as weather, farm, rice centers, fields, crops and multiple agriculture machinery. Additionally, the introduced MAS environment is based on a discrete event simulation that enables communication across various independent agents. The system includes different harvesting schedule policies which determine the harvesting order for multiple paddy fields on specific days. The system was evaluated through two distinct experiments: (i) ‘Model Verification Simulation’, which successfully demonstrated the replication of actual historical farming practices, and (ii) ‘Operational Efficiency Simulation’, which compared the overall farm efficiency under different scheduling policies as well as different environmental conditions (e.g., rainfall). The simulation successfully generated a dataset containing traits and performance indicators that replicate the patterns observed in real-world data, while also approximating the operational behaviors and workflows of actual rice harvesting systems. Future studies could further evaluate the model’s robustness to confirm its practical applicability.

Keywords: multi-agent system; harvesting model; discrete event simulation; Japanese rice harvesting; agriculture logistics; queuing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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