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

Kartick Dey (), Debajyoti Chatterjee (), Subrata Saha () and Ilkyeong Moon ()
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Kartick Dey: University of Engineering and Management
Debajyoti Chatterjee: Indian Institute of Information Technology and Management (IIITM-Kerala)
Subrata Saha: Aalborg University
Ilkyeong Moon: Seoul National University

Annals of Operations Research, 2019, vol. 279, issue 1, No 7, 187-219

Abstract: 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.

Keywords: Rebate; Inventory; Optimal control; Hybrid bat algorithm (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10479-018-3110-x

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