AI-Driven Mango Logistics Simulation

The risk flow in Figure 1 represents the effectiveness of launching AI to select policies in an environment. Using an OpenAI-gym library, the system simulates mango allocation during the export period in a customized risk field. This library is a toolkit for reinforcement learning. It includes several benchmark problems that expose standard interfaces and compare algorithm performance (Brockman et al., 2016 ). The simulation system with this library can construct a scene of fruit logistics, such as the fruit loss process (loss at the farm level, loss due to transportation, loss at the wholesale level, loss during storage, loss at the retail level, loss at the consumer level, and loss during processing). In the simulation, the intelligent agent drives the reaction process. Only a small probability of mango loss may occur using the FIFO and LSFO policies, as shown in Joas et al. (2010 (link)).