Incorporating exhibitor competitiveness and price sensitivity: A Staged Crossover Hybrid Genetic Algorithm (SCHGA) for optimizing booth pricing

Exhibitor participation is crucial to the success of an exhibition. However, a key challenge for organizers is how to meet the diverse demands of enterprises regarding booth pricing and related services. By analyzing real exhibition data, this study explores a method to improve the overall revenue of both exhibitors and organizers. However, previous studies have overlooked three key factors that affect exhibitors’ overall revenue: booth pricing, enterprise competitiveness, and internal enterprise strength. To address this gap, we propose a booth pricing model that incorporates exhibitor competitiveness and price sensitivity, along with multiple constraints, to better accommodate their flexible demands. To effectively solve the proposed mathematical model, this study presents the Staged Crossover Hybrid Genetic Algorithm (SCHGA). The algorithm adopts a fine-grained coordinate-point crossover mechanism, in which crossover is performed at the coordinate-point level. After identifying the better-performing coordinate points in one direction, the algorithm accelerates the matching of better points in the other direction, thereby speeding up convergence and improving solution quality. Experimental results on real exhibition data show that SCHGA outperforms two advanced algorithms and five basic algorithms in terms of convergence quality and stability. Therefore, SCHGA can effectively assist exhibition organizers in booth pricing and allocation decisions.