A Hybrid Data-Mining Algorithm and Data-Driven Supply Chain Modeling for Allocation Goods to Warehouses and Warehouse Service to Customers

In this research, the issue of product allocation in a situation that there are a large number customers and goods are various, is investigated. Expanding the level of Internet access and increasing the desire of online shopping, raise the number of customers. In a situation where there is a great variety of goods and a large number of customers, it is difficult to solve issues such as on-time delivery of goods or services, selection and ordering in decentralized warehouses, and the issue of warehouse allocation to customers. To solve these challenges, the use of mathematical modeling with meta-heuristic solution methods has been proposed so far, but due to the large number of allocation modes, solving mathematical models is very complex and it takes time. With the improvement of computing power and storage space, data-driven methods have been studied by researchers to solve these challenges. In this study, a hybrid data-driven solution that uses data mining and mathematical modeling to manage the variety of goods and the number of customers has been proposed, that manages the variety of goods and the number of customers, and can solve mathematical models in less time. This method has been implemented on the data of "DigiKala".