When creating a product line, a retailer must make several decisions simultaneously: the selection of the product types to include in the product line as well as the order quantity and price of each selected product type. This study investigates joint product line decisions by considering the dynamic substitutions of products as driven by the valuations that customers place on the products and the availability of each product type, which changes as consumers purchase a product. An integer programming model was developed for joint decisions of product selection, price, and order quantity in the product line problem to maximize the total profit of the retailer. To solve the model, we propose a hybrid genetic algorithm (HGA) that uses special genetic operators and heuristic algorithms to ensure the feasibility and efficiency of solutions. Computational experiments demonstrate the superiority of the proposed HGA over the solution obtained by CPLEX for large scale problems. Useful managerial insights on the joint product line decisions are also derived from the numerical results. (C) 2017 Elsevier B.V. All rights reserved.