A Hybrid Genetic Algorithm-Based Optimization Method for Agricultural E-Commerce Logistics Network

Abstract

Given the quick development in trade volume and the continuous development of country product e-commerce stages, it is extremely critical to concentrate in-depth mining and investigation of online review data on increasing consumer fulfillment. The objective of this project is to create an integrated decision emotionally supporting network utilizing a bleeding edge multi-objective genetic algorithm to optimize the supply chain of the biodiesel business, a critical agro-industry in Indonesia. By examining the water balance of the underground water system, an optimization model of the structure of the country business was developed utilizing genetic algorithms. The model was then solved utilizing an accelerated genetic algorithm. By combining the evolutionary algorithm with a real-world example to examine, this study aims to address the problem of thin dissemination channels in businesses and promote the application of the model in enterprises with comparable qualities. Two of the various goals taken into account in this analysis include minimising the expected number of deteriorated products and limiting total supply chain cost (TSCC) (ENDP). The second goal is critical for the agro-modern industry. The variables that need to be optimised include the quantities of coconut or palm oil carried from suppliers to plants, the biodiesel shipped from plants to customer zones, and plant stocks. The numerical outcome demonstrated the strength and dependability of the genetic algorithm established in this work, which can deliver positive outcomes.