The application of Blockchain Technology (BT) makes consumers trace more product information and enhances their trust in the product brand, but it also brings cost pressure to some participants who adopt the technology. Thus, it is particularly important how to encourage and support more enterprises to participate in BT. This study incorporates the altruistic preference into a BT-enabled three-echelon supply chain consisting of a powerful retailer, a supplier, and a manufacturer. We investigate the optimal strategies of the supply chain in different scenarios: without and with the BT, and without and with the altruistic preference. Then, we explore the effects of the BT and the retailer’s altruistic preference on the profit performance of the supply chain system. The results show that the retailer, the manufacturer, and the supplier do not always benefit from the traceability of the BT, and while the powerful retailer’s altruistic preference decreases his or her own profit but increases the profits of the manufacturer, the supplier, and the entire supply chain. Finally, a profit redistribution mechanism is designed to coordinate the supply chain with the retailer’s altruistic preference under the BT.

Based on the retail inventory operation of Heilan Home, this study incorporates the price factor into inventory environment involving trapezoidal time-varying products. A joint pricing and ordering issue with deteriorating items under partial backlogged shortages is firstly explored in a fixed selling cycle. The corresponding optimization model aiming at maximizing profit performance of inventory system is developed, the theoretical analysis of solving the model is further provided, and the modelling frame generalizes some inventory models in the existing studies. Then, a solving algorithm for the model is designed to determine the optimal price, initial ordering quantity, shortage time point, and the maximum inventory level. Finally, numerical examples are presented to illustrate the model, and the results show the robustness of the proposed model.