Intelligent products pricing in dynamic competition based-on Stackelberg game theory

Optimising product price is essential in dynamic competitive markets to maximise the total profit of all players and secure their survival in the market. This study addresses the intelligent optimisation of product prices in a competitive environment using Stackelberg game theory (SGT), where both a leader and follower player are considered. The objective is to determine the optimum selling prices for five main products to maximise the profits of all the players. Novel aspects of this study are the integration of optimisation models of all of the players and incorporation demand prediction accuracy into the optimisation process, ensuring that the predicted demand resulting from optimised prices aligns with historical demand data—a factor that has been disregarded by prior studies. Genetic Algorithm (GA) is employed for the optimisation algorithm due to the complexity of the model that involves numerous parameters and decision variables. The results demonstrate that the proposed products selling prices not only enhances the total profits of all of the players but also ensures that the predicted demand pattern closely fits the historical demand data pattern, validating the effectiveness of the approach.