A Heuristic for bundle pricing with interrelated products

Bundle pricing is a widely used strategy in marketing analytics and revenue management, yet most existing heuristics rely on restrictive assumptions about product valuations, such as independence or additivity, and about costs, such as being zero or additivite. We address the mixed bundling problem by proposing a general heuristic algorithm, the pricing-assignment heuristic (PAH), and its extension, E-PAH, which are designed to accommodate heterogeneous customer types and general forms of product interrelatedness, including complementarity and substitutability. The proposed approach alternates between pricing and assignment steps to iteratively improve the seller's profit, and can be initialized from any feasible solution or used as a post-processing tool to enhance existing heuristics. Through extensive numerical experiments, we show that our method consistently outperforms standard benchmarks such as component pricing, pure bundling, and bundle size pricing, achieving on average profit improvements of up to 43% for independent products and up to 53% when products exhibit interdependencies. The algorithm remains effective in the presence of general cost structures, highlighting its flexibility and practical relevance. Overall, the proposed heuristic provides a simple, scalable, and broadly applicable framework for mixed bundling in complex and realistic market environments.