A. Jiménez-Cordero, S. Pineda, J. M. Morales González
The Rank Pricing Problem (RPP) is a bilevel optimization problem with binary variables aimed at finding optimal pricing strategies to maximize total benefit while accounting for customer preferences that influence prices. Traditional exact methods for solving RPP can be computationally expensive. This paper proposes a novel two-phase heuristic approach. In phase one, Variable Neighborhood Search (VNS) or a genetic algorithm generate an initial pricing strategy, leveraging their effectiveness in combinatorial optimization. Phase two applies four local searches that refine the solution using RPP-specific information, avoiding additional optimization problems. While the method lacks optimality guarantees, experiments show it surpasses Mixed Integer Program solvers in solution quality and efficiency.
Keywords: Rank Pricing Problem, Variable Neighborhood Search, genetic algorithm, heuristic approaches, bilevel optimization, combinatorial optimization
Scheduled
AMC4 Prediction and Classification
June 11, 2025 10:30 AM
MR 1