RT Journal Article
T1 An enhanced heuristic framework for solving the Rank Pricing Problem
A1 Jiménez-Cordero, María Asunción
A1 Pineda-Morente, Salvador
A1 Morales-González, Juan Miguel
K1 Algoritmos genéticos
K1 Heurística
K1 Optimización combinatoria
K1 Precios
AB The Rank Pricing Problem (RPP) is a challenging bilevel optimization problem with binary variables whose objective is to determine the optimal pricing strategy for a set of products to maximize the total benefit, given that customer preferences influence the price for each product. Traditional methods for solving RPP are based on exact approaches which may be computationally expensive. In contrast, this paper presents a novel heuristic approach that takes advantage of the structure of the problem to obtain good solutions. The proposed approach consists of two phases. Firstly, a standard heuristic is applied to get a pricing strategy. In our case, we choose to use the Variable Neighborhood Search (VNS), and the genetic algorithm. Both methodologies are very popular for their effectiveness in solving combinatorial optimization problems. The solution obtained after running these algorithms is improved in a second phase, where four different local searches are applied. Such local searches use the information of the RPP to get better solutions, that is, there is no need to solve new optimization problems. Even though our methodology does not have optimality guarantees, our computational experiments show that it outperforms Mixed Integer Program solvers regarding solution quality and computational burden.
PB Elsevier
SN 0957-4174
YR 2025
FD 2025-03-12
LK https://hdl.handle.net/10630/38534
UL https://hdl.handle.net/10630/38534
LA eng
NO Jiménez-Cordero, A., Pineda, S., & Morales, J. M. (2025). An enhanced heuristic framework for solving the Rank Pricing Problem. Expert Systems with Applications, 276, 127122.
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026