Sales Training Based Optimization: A New Human-inspired Metaheuristic Approach for Supply Chain Management

Sustainable Lot Size Optimization is an important challenge of Supply Chain Management as it seeks to balance the economic goals of minimizing costs with environmental and social objectives, ensuring efficient production and inventory management while reducing environmental impact and enhancing social responsibility.Metaheuristic algorithms play a crucial role in solving Sustainable Lot Size Optimization problems by efficiently exploring large and complex search spaces to find near-optimal solutions that balance economic, environmental, and social objectives, often outperforming traditional optimization methods in terms of flexibility and scalability.With this attitude, in this paper, a new metaheuristic algorithm called Sales Training Based Optimization (STBO) is designed to solve Sustainable Lot Size Optimization applications.The fundamental inspiration in the design of STBO draws upon human behaviors observed during sales training.The theoretical framework of STBO is thoroughly described, and its implementation process is mathematically formulated in two distinct stages: the exploration phase and the exploitation phase.The efficiency of STBO to address Sustainable Lot Size Optimization applications has been evaluated on 10 study scenarios.The optimization outcomes reveal that STBO consistently delivers highly effective solutions by seamlessly integrating exploration with exploitation throughout the search.Furthermore, a thorough comparison was conducted, revealing how STBO's results stack up against those from twelve widely recognized metaheuristic algorithms.The simulation findings conclusively demonstrate that the STBO approach consistently outperforms competitors, achieving superior performance across all study scenarios.These insights confirm that the STBO approach serves as a highly reliable and potent optimization tool, capable of addressing a wide range of optimization challenges in diverse applications.