Joint Pricing and Inventory Replenishment Decisions with Returns and Expediting under Reference Price Effects

Abstract

This paper considers a single-item joint pricing and inventory replenishment problem under reference price effects in consecutive T periods. Demands in consecutive periods are sensitive to price and reference price with general demand distribution. At the end of each period, after the demand realization, a firm can return excess stocks to a supplier or place an expediting order to reduce the loss by shortage. Unfilled demands are fully backlogged. In order to maximize the total expected discounted profit with reference price effects the optimal pricing and inventory replenishment policies for regular order and the inventory adjustment decisions for returning/expediting are derived. The optimal replenishment policy for regular order is a base-stock policy, the optimal pricing policy is a base-stock-list-price policy, and the optimal policy for returning/expediting inventory adjustment follows a dual-threshold policy. Furthermore, the analysis of the operational impacts (from the perspective of adding returning/expediting and reference price effects, respectively) is researched. Numerical results also show that considering both returning/expediting and reference price effects is more profitable than considering only one of them.

Rnn-Ade-Based Multi-Product Replenishment-Pricing Modeling Study

“…The historical selling price and wholesale price of vegetables are a set of regular time-series data [13]. The RNN recurrent neural network has strong model fitting ability as well as good prediction performance for time series data [2]. Therefore, in order to satisfy the historical pricing constraints of commodity sales, an RNN recurrent neural network is constructed to predict the pricing and wholesale price of vegetable commodities on the coming day.…”

“…To solve this problem, different scholars have proposed diverse strategies. For example, Yuan Li [2] focuses on developing replenishment models based on market demand and cost analysis. Their study shows that these models work effectively under specific conditions but may fall short in considering the effects of historical sales data on future demand forecasts.…”

Dual-Channel Retailer’s Multi-Period Inventory and Pricing Policy with the Quantity-Sales Mode

“…Hence, customers buying online and offline are D et = d e ε t and D rt = d r ε t , respectively. Note that the retailer could meet customer demand by placing an expedited order [31][32][33] if the on-hand stock is below the customer demand. Thus, all the demand can be satisfied in each period [34].…”

Research on Vegetable Replenishment and Pricing Optimization Decision Based on SARIMA Model and Regression Analysis

“…In order to develop more rational pricing strategies, it is crucial to take into account the influence of returns, inventory, and the inherent value of the products on decision-making. Zhao Lin et al [1] proposed a replenishment and pricing strategy that takes into account returns and fixed costs using stochastic dynamic programming. Meanwhile, Chen Jun et al [2] examined pricing and inventory replenishment decisions for agricultural products with dual-channel sales.…”