Determining the Optimal Price in the Steel Industry Using Multilateral Monopoly Patterns with the Approach of Neural Networks and Game Theory

IntroductionThe field of supply chain management has focused on crucial topics such as coordination, cooperation, and competition among chain members. Game theory has emerged as a valuable tool for examining supply chain management issues, as it analyzes various situations and their impact on supply chain performance (Naimi Sediq et al., 2013; Shafi'i et al., 2018). While every action and performance within the supply chain influences the outcomes of the game, it does not solely determine them. The goal is to balance the parties involved in the supply chain and create satisfaction for the end customer (Metinfer et al., 2018).Although extensive research has been conducted in supply chain management within the steel industry, the impact of sanctions on Nash equilibria and the application of three approaches (Cournot, Stackelberg, and collusion) to achieve game balance in different scenarios have not been thoroughly investigated. This research aims to fill this gap by addressing the mentioned research question. The current study focuses on determining the optimal price using an intelligent decision-making system based on game theory within the steel industry, considering the presence or absence of the sanctions variable.Our country currently possesses several relative advantages in terms of steel production conditions, including abundant and affordable energy, iron ore and refractory raw materials, considerable steel production experience, and a skilled and cost-effective workforce. By acquiring new production technology, these advantages enable our country to play a competitive and influential role in the global steel market. However, the steel industry faces significant challenges such as price fluctuations, extreme price disparities across regions, and delayed delivery due to a lack of efficient supply chain management. Therefore, the main research question aims to provide a model that incorporates key variables, such as the supply and demand of final and intermediate products in the steelmaking industry and the future trends in production and quantity changes.Research methodThis article introduces a composite model that combines artificial neural networks and game theory to assist stakeholders in the steel industry in determining optimal production levels and price levels. To predict the price of steel, three techniques were employed: Bayesian neural networks, support vectors, and Grassberg anti-diffusion. Additionally, to address the issue of binary iden