Optimizing production scheduling with the spotted hyena algorithm: A novel approach to the flow shop problem

Abstract

The spotted hyena optimization algorithm (SHOA) is a novel approach for solving the flow shop-scheduling problem in manufacturing and production settings. The motivation behind SHOA is to simulate the social dynamics and problem-solving behaviors of spotted hyena packs in order to identify and implement optimal schedules for jobs in a flow shop environment. This approach is unique compared to other optimization algorithms such as WOA, GWO, and BA. Through extensive experimentation, SHOA has been shown to outperform traditional algorithms in terms of solution quality and convergence speed. The purpose of this study is to present the details of the SHOA algorithm, demonstrate its effectiveness, and compare its performance with other optimization approaches. The method used in this study includes extensive experimentation and comparison with other algorithms. The findings of this study show that SHOA is a promising tool for optimizing production processes and increasing efficiency. The implications of this study are that SHOA can be used as an effective tool for solving flow shop-scheduling problems in manufacturing and production settings.