Energy-Efficient Flow Shop Scheduling Using Hybrid Grasshopper Algorithm Optimization

Abstract

Manufacturing companies have a significant impact on environmental damage, and energy consumption in manufacturing companies is a widespread issue because the energy used is derived from fossil fuels. This research aims to minimize energy consumption using develop Hybrid Grasshopper Algorithm Optimization (HGAO). The focus of the issue in this article is the Permutation Flow Shop Scheduling Problem (PFSSP). A case study was conducted in offset printing firms. The results showed that the HGAO algorithm is capable of reducing energy consumption in offset printing firms. The higher the population of search agents and iterations produces less energy consumption. The HGAO algorithm is also compared with the genetic algorithm (GA). The results show that HGAO is more efficient in reducing energy consumption than GA.