<![CDATA[The manufacturing sector accounts for a dominant proportion of global energy consumption. This sector has become the center of attention since the concern of the energy crisis rose. One of the strategies proposed to overcome this issue is implementing appropriate scheduling, such as Hybrid Flow Shop Scheduling. This research aimed to develop a Hybrid Archimedes Optimization Algorithm (HAOA) to solve Energy-Efficient Hybrid Flow Shop Scheduling (EEHFSP). In this research, three stages of EEHFSP are considered in a problem that involves sequence-dependent setup time in the second stage. The removal time also is involved in the second stage. The results indicated that the iteration and the population of HAOA did not affect the removal and processing energy consumptions but affected the setup and idle energy consumptions. The algorithm comparison of ten cases showed that the proposed HAOA resulted in an optimum TEC compared to the other algorithms. The manufacturing sector accounts for a dominant proportion of global energy consumption. This sector has become the center of attention since the concern of the energy crisis rose. One of the strategies proposed to overcome this issue is implementing appropriate scheduling, such as Hybrid Flow Shop Scheduling. This research aimed to develop a Hybrid Archimedes Optimization Algorithm (HAOA) to solve Energy-Efficient Hybrid Flow Shop Scheduling (EEHFSP). In this research, three stages of EEHFSP are considered in a problem that involves sequence-dependent setup time in the second stage. The removal time also is involved in the second stage. The results indicated that the iteration and the population of HAOA did not affect the removal and processing energy consumptions but affected the setup and idle energy consumptions. The algorithm comparison of ten cases showed that the proposed HAOA resulted in an optimum TEC compared to the other algorithms.]]>
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