Inventory Control in multi-item Systems with Probable Demand Using Particle Swarm Algorithm (Case study: Novin Ghate Caspian Company)

Inventory control and orders planning are among the key issues in developing the economic policies of industrial units, which requires attention to the factors and conditions governing the organization and the market. In this context, an optimal balance among inventories, ordering costs and maintenance costs can have a crucial role in preventing the loss of capital and shortages in the inventories. The purpose of this paper is to control the inventory in multi-item systems under the conditions of probabilistic demand and warehouse limit. The problem is studied by replacing the scheduling horizons with short-term periods in the general model of periodic orders which has solved the problem using the Particles swarm optimization algorithm. The results illustrated the connection of the inventory amount at two times of t-1 & t. The model's advantage is the dynamics of the general model of orders, especially in conditions of uncertainty in the business environment due to dramatic changes in the market conditions that are close to each other. This can make the general model of orders more dynamic and reflect the real conditions better and it can help managers determine the economic value at different times which is of high importance considering the limitations of definitive inventory control formulas. The model has been implemented on four different products in Novin Gateh Co. Since in manufacturing organizations, due to the presence of raw materials, particles, and inventories in the process, the role of inventory control is more evident, the proposed model can be used to create a reliable stream of items and inventory of the organization taking into account the elements of time, location, quantity, quality and costs.