Green Transportation Route Model for Industrial Goods Delivery with Genetic Algorithm

The urgency of goods delivery activities in routine and important industries has the potential to damage the environment, high transportation costs and delays in goods delivery. In an effort to handle complex problems, it is necessary to determine routes considering distance and time to different locations in order to get the best green transportation route. The data used are manufacturing industry latitude and longitude coordinate point data from factory data, distributor data and shop/retailer data. Next, the data is processed using Extract Transform Load (ETL) with Google Collab Tool Python to get the best new distance used in the green transportation route determination model. The aim of the research is to design a green transportation route model using a genetic algorithm. The benefits of research are it can provide information for industry in determining the best route, thereby making delivery of goods to consumers more effective and transport costs efficient; Can optimize green transportation routes reducing the impact of pollution resulting from shipping goods; The research method is a green transportation route model with a genetic algorithm. The results of this research produced a green transportation route model with a genetic algorithm for the Palembang factory to the distributor with the best distence 272.443766121975, the Baturaja factory to the distributor best distence 272.4437714642356, and from the Lampung factory to the distributor best distence 272.4437512424067.