In a world where food and pharmaceutical security are vital components of passive defense, the management and preservation of supply chains for environmentally sensitive pharmaceuticals are crucial. Governments prioritize pharmaceutical logistics to ensure the availability of essential drugs, particularly during crises and sanctions. In countries like Iran, the high sensitivity of biological materials and their vulnerability to heat necessitate continuous monitoring of drug distribution systems. Delays due to traffic, transportation accidents, and human error can severely disrupt the supply chain, leading to drug trafficking, medication loss, and public health risks.

This paper addresses a significant challenge in Iran's pharmaceutical logistics: managing the massive volume of spatiotemporal data from refrigerated drug distribution vehicles. To optimize the segmentation of distribution fleet routes, the paper introduces a method named SEGA, inspired by the genetic algorithm, offering an innovative approach to saving time and enhancing performance. This spatiotemporal data, including refrigerator temperature, external temperature, vehicle speed, and location, is critical for maintaining the safety and quality of pharmaceuticals. However, the continuous, real-time transmission of this data can lead to database overflow, necessitating efficient data management and summarization techniques.

Experiments and simulations demonstrate that the SEGA method significantly improves the performance of the genetic algorithm, effectively segmenting and summarizing the data. The results indicate that this approach can enhance the system's speed and efficiency by up to 92%, providing a robust solution for optimizing pharmaceutical logistics in challenging environments.