An iterated greedy‐based metaheuristic with local search for the rank pricing problem

Abstract The rank pricing problem involves determining optimal prices for a set of products while accounting for customers' budgets and preferences. This study develops an iterated greedy‐based metaheuristic to efficiently solve this problem. The core idea is to generate a sequence of solutions by iteratively applying destruction and reconstruction phases. In this process, some components of a solution are removed, yielding partial solutions from which complete solutions are reconstructed. A local search method with three neighborhood exploration strategies is then applied. Computational experiments demonstrate the effectiveness of the proposed algorithm by comparing its performance with exact and heuristic methods from the literature. It consistently finds optimal or near‐optimal solutions for instances with known optima. For most cases where the optimal solution is unknown, the algorithm matches or outperforms the best‐known solutions. Moreover, it achieves these results with significantly lower computational times, reinforcing its suitability for solving the rank pricing problem.