Integrated optimization of assortment, inventory and pricing considering omnichannel retailer’s risk aversion and customer’s time preference

Abstract

This paper mainly discusses the joint optimization of product assortment, inventory and pricing for an omnichannel retailer under uncertain demand, seeking to maximize the retailer’s Worst-case Conditional Value-at-Risk (WCVaR) based profit and customer’s expected utility. The risk aversion of decision-maker and the time preference of customers are depicted by the WCVaR and quasi-hyperbolic discounting function, respectively. A bi-objective stochastic optimization model is developed. To alleviate the computational burden, a linearization method is applied to convert the problem into a mixed integer linear programming that proves to be solvable within reasonable CPU times using the augmented ε-constraint method. Numerical studies are conducted to investigate the applicability of the proposed model and the efficiency of the solution approach. Our experimental results show that offering customers with high expected utility typically requires low prices and small assortments. We also make other counterintuitive observation that the price of products sometimes increases when the expected utility increases. Furthermore, WCVaR has superiority compared with the risk-neutral model in terms of robustness and stability. The time preference of customers has non-negligible impacts on decision-making results. In addition, the further sensitivity analyses investigate the impacts of various key parameters on the performance of omnichannel retailing.