Optimization of Various Costs in Inventory Management using Neural Networks

Abstract

The process of maintaining the right quantity of inventory to meet demand, minimising logistics costs, and avoiding frequent inventory problems, including reserve outs, overstocking, and backorders is known as inventory optimisation. One has a finite capacity and is referred to as an owned warehouse (OW), which is located near the market, while the other has an endless capacity and is referred to as a rented warehouse (RW), which is located away from the market. Here, lowering the overall cost is the goal. Neural networks are employed in these works to either maximise or minimise cost. The findings produced from the neural networks are compared with a mathematical model and neural networks. Findings indicate that neural networks outperformed both conventional mathematical models and neural networks in terms of optimising the outcomes. The best way to understand supervised machine learning algorithms like neural networks is in the context of function approximation. The benefits and drawbacks of the two-warehouse approach compared to the single warehouse plan will also be covered. We investigate cost optimisation using neural networks in this chapter, and the outcomes will also be compared using the same.