A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

The manufacturing sector consumes most of the global energy and had been in focus since the outbreak of the energy crisis. One of the proposed strategies to overcome this problem is to implement appropriate scheduling, such as Hybrid Flow Shop Scheduling. Therefore, this study aims to create a Hybrid Archimedes Optimization Algorithm (HAOA) for solving the Energy-Efficient Hybrid Flow Shop Scheduling Problem (EEHFSP). It is hoped that this helps to provide new insights into advanced HAOA methods for resolving the EEHFSP as the algorithm has the potential to be a more efficient alternative. In this study, three stages of EEHFSP were considered in the problem as well as a sequence-dependent setup and removal times in the second stage. Experiments with three population variations and iterations were presented for testing the effect of HAOA parameters on energy consumption. Furthermore, ten job variations are also presented to evaluate the performance of the HAOA algorithm and the results showed that HAOA iteration and the population did not affect the removal and processing of energy consumption, but impacted that of setup and idle. The comparison of these ten cases revealed that the proposed HAOA produced the best total energy consumption (TEC) when compared to the other algorithms