Dynamic versus static rebates: an investigation on price, displayed stock level, and rebate-induced demand using a hybrid bat algorithm

Abstract

Joint determination of price, rebate, investment in preservation technology, and order quantity is a complex task for retailers today. To help retailers, this paper presents an investigation on a replenishment policy for deteriorating products that focused on the choice between dynamic and static rebates under the price, displayed stock level, and rebate-induced demand. With the objective of maximizing the retailer’s profit, six different models were formulated under static and dynamic environments to identify optimal price-and-rebate pair and preservation technology investment policy. Optimal control theory was employed to determine the rate of dynamic rebate. A hybrid bat algorithm (HBA) is developed to find solutions for the proposed non-linear optimization problems. The efficiency of the proposed algorithm was verified with standard test functions. Price sensitivity, the nature of the product, and display stock elasticity were found to be decisive parameters for a retailer’s rebate strategy. Dynamic rebate on initial price of the product can significantly improve the profit of the retailer. The retailer’s investment decision was also significantly influenced by the nature of the product. Sensitivity analyses were carried out to offer managerial insights.