ABSTRACT
Reflection in the backdrop of global manufacturing and commercial aggregation is the significance of inventory control. It is not only a vital component of supply chain management but also one of how a value-added supply chain can be realized. Consequently, the issue of inventory control optimization is gaining attention, and the issue of inventory control management is undergoing new research and facing new challenges. This paper establishes a centrally controlled multi-location inventory ordering and allocation model based on the inventory control model, combined with the concept and research content of a multi-location inventory system, based on the knowledge of supply chain management theory and the related methods of inventory control optimization. This study employs the genetic annealing evolutionary algorithm with the global search capability of the genetic algorithm and the local search capability of the simulated annealing algorithm to solve the ordering and allocation models in the multi-location inventory model to obtain the optimal ordering quantity and the optimal allocation quantity, thereby achieving the minimum total cost of multi-location inventory. In this study, the model-solving process is implemented through MATLAB programming, and the studied problem is solved and validated by using arithmetic examples to derive the optimal order quantity and optimal allocation quantity under the ordering and allocation models, respectively, to validate the efficacy of the centrally controlled distributed inventory system.
