With the improvement of social environmental awareness, the dual‐channel green product sales mode has been widely used by many manufacturing firms. In this paper, we consider a dual‐channel green supply chain where one manufacturer produces a green product and sells it through one retail channel and its own direct channel. Consumers in the two channels have different perceptions of the product energy efficiency level due to different purchasing experiences. The product energy efficiency level evolves over time and is characterized as a dynamic variable. By developing and solving the Stackelberg differential game problems under the dynamic and static wholesale pricing strategies, respectively, we obtain the main results in this paper. First, the manufacturer has more incentives to invest in green innovation when more consumers buy the green product through the direct channel. Second, the manufacturer prefers to adopt the dynamic wholesale pricing strategy in most cases and prefers the static one only when the consumers in both channels have relatively high energy efficiency perceptions. By introducing the transfer payment contract, we show that the static wholesale pricing strategy may be the better choice, which leads to a win‐win outcome for both members. Finally, sensitivity analysis further provides some managerial insights and verifies the robustness of the results.

The precise identification of postural behavior plays a crucial role in evaluation of animal welfare and captive management. Deep learning technology has been widely used in automatic behavior recognition of wild and domestic fauna species. The Asian slow loris is a group of small, nocturnal primates with a distinctive locomotion mode, and a large number of individuals were confiscated into captive settings due to illegal trade, making the species an ideal as a model for postural behavior monitoring. Captive animals may suffer from being housed in an inappropriate environment and may display abnormal behavior patterns. Traditional data collection methods are time-consuming and laborious, impeding efforts to improve lorises’ captive welfare and to develop effective reintroduction strategies. This study established the first human-labeled postural behavior dataset of slow lorises and used deep learning technology to recognize postural behavior based on object detection and semantic segmentation. The precision of the classification based on YOLOv5 reached 95.1%. The Dilated Residual Networks (DRN) feature extraction network showed the best performance in semantic segmentation, and the classification accuracy reached 95.2%. The results imply that computer automatic identification of postural behavior may offer advantages in assessing animal activity and can be applied to other nocturnal taxa.

Inventory level has a significant impact on the goodwill of products to customers, which seldom becomes the focus of previous studies. In this paper, joint dynamic pricing, advertising, and production decision-making problem is investigated, where the demand rate depends on sales price and goodwill. The inventory and backlog as well as advertisement are considered as goodwill-building factors. The optimal dynamic pricing, advertising, and production policies are derived by using Pontryagin’s maximum principle. Numerical examples are provided to demonstrate the obtained results, and sensitivity analysis of main system parameters is carried out to obtain some managerial insights. We find that when the initial goodwill is relatively high, the firm’s profit first decreases and then increases with respect to the impact intensity of inventory on goodwill; otherwise, the firm always benefits from a higher impact intensity of inventory on goodwill. Furthermore, the optimal production and advertising policies are complementary caused by the feature of inventory-dependent goodwill.

This paper considers the optimal decisions of pricing and warranty level for substitutable products in a fuzzy supply chain environment, where the consumer demands, manufacturing costs, and warranty costs are characterized as fuzzy variables. Expected value manufacturer-leader Stackelberg models and two chance-constrained programming models, that is, theλ-optimistic andλ-pessimistic manufacturer-leader Stackelberg models, are established. The corresponding closed-form solutions are obtained by using the game-theoretical approach and fuzzy theory. Finally, numerical examples are presented to illustrate the effectiveness of the models and to provide managerial insights for decision makers. The results show that it would be beneficial for the whole supply chain when the warranty services are introduced into the market, and the firm which provides the warranty service has the advantage in holding more dominant position.