Yanhui Li

Advances in Internet technology allow retailers to adjust prices at low costs and to adopt dynamic pricing strategies to attract consumers. Consumers can easily access to the price history through the Internet, forming expectations of future price reduction, resulting in an increase in strategic purchase behavior. This behavior increases the uncertainty of market demand and the operational risk of retailers. Therefore, it is an important topic to develop reasonable pricing and inventory strategies to deal with consumer strategic behavior and reduce the pressure on retailers. Considering that consumer strategic behavior is influenced by product discount price, the impact of product pricing on demand during different sales periods is modeled using a multinomial logit (MNL) selection model. A mixed‐integer nonlinear programming (MINLP) model is constructed, with pricing and inventory as decision variables, to maximize the retailer’s total profit. To simplify the problem, the model is approximately linearized as a mixed‐integer linear programming model (MILP). It is found that this model and method can effectively solve the problem of product pricing and optimal inventory decision during the discount period and can deal with large‐scale problems efficiently. Retailers can use historical transaction data to understand consumers’ purchasing behavior, influence the sales cycle demand through pricing strategies, and guide consumers’ purchasing behavior.

The volatility of agricultural commodity prices significantly affects market stability and financial market dynamics, especially during periods of economic uncertainty and global shocks. Accurate price prediction, however, remains challenging due to the complex, nonlinear characteristics of agricultural markets and the diverse range of influencing factors. To overcome these challenges, this study develops a novel price forecasting framework that combines advanced time series decomposition, swarm intelligence optimization, and deep learning techniques. The proposed framework employs successive variational mode decomposition (SVMD) to deconstruct the raw price data into multiple components, effectively capturing the underlying nonlinear patterns and dynamic features. These components are then fed into a CNN-augmented BiLSTM model, enhanced with an attention mechanism to extract both temporal dependencies and intricate data relationships. To fine-tune the model’s hyperparameters, this study introduces a multiple strategies dung beetle optimisation algorithm (MSDBO), which integrates four strategic modifications to improve the balance between global search, local exploration, and convergence efficiency. Using historical data from corn and wheat markets as case studies, the experimental findings demonstrate that the proposed SVMD-MSDBO-CNN-BiLSTM-A model significantly outperforms nine baseline approaches. Specifically, it reduces the Mean Absolute Percentage Error (MAPE) by 25.78% and 37.57%, respectively, and enhances directional accuracy ( D _stat ) by 1.15% and 14.53% compared to the top single models.

As e-commerce competition intensifies, brand companies face challenges such as product homogenization, consumer trust erosion, and price wars. Blockchain technology (BCT) has emerged as a solution to enhance transparency and optimize competitive strategies. In this study, the adoption of BCT and pricing decisions between weak and strong brand companies is analyzed in an asymmetric competitive environment. A game-theoretic framework is developed to analyze whether each company should adopt BCT. The results show that when the difference in brand awareness is small, BCT adoption allows pricing advantages to be shifted between weak and strong brands. As the difference in brand awareness increases, both types of brands reduce their BCT investments. Moreover, when one brand adopts BCT, the other often follows suit to maintain competitiveness. Three equilibrium scenarios are identified based on consumer privacy concerns and differences in brand awareness. Interestingly, the strategy where only the weak brand adopts BCT does not constitute an equilibrium in any scenario. Finally, when the difference in brand awareness is significant and price competition is high, either both brands adopt BCT or neither does. This paper provides strategic insights into BCT adoption and its implications for competitive behavior in asymmetric markets.

The need to reduce carbon emission to cope with climate change has gradually become a global consensus, which also poses a great challenge to cold-chain logistics companies. It forces them to implement green distribution strategies. To help the distribution companies reduce carbon emission, this paper studies two aspects—carbon tax value and investing in the freshness-keeping cost—and proposes corresponding solutions. A new green vehicle-routing model for fresh agricultural products with the goal of minimizing the total cost is proposed. To solve the model proposed, an improved ant-colony optimization (IACO) is designed specifically. On one hand, the experimental results show that the increase in carbon tax will restrict the carbon emission behaviors of the distribution companies, but it will also reduce their economic benefits to a certain extent, at the same time. On the other hand, investing in the freshness-keeping cost can help actively achieve the carbon emission reduction target, reduce the loss of fresh agricultural products in the distribution process, improve the company’s economic benefits and satisfy customers. The comparison results of different algorithms prove that the IACO proposed in this paper is more effective in solving the model, which can help increase the economic benefits of the companies and reduce carbon emission. This study provides a new solution for cold-chain logistics distribution companies to reduce carbon emission in the distribution process, and also provides a reference for government departments to formulate carbon tax policies.

Considering information security and personalization paradoxes, this paper investigates the optimal information security investments of a firm and purchasing decisions of consumers with different privacy concerns in online personalization. It is found that consumers and firm tend to fall into a prisoner's dilemma in Nash equilibrium. To improve security investment efficiency and boost personalization consumption, firm‐led Stackelberg game and firm‐led Stackelberg game with punishment mechanism are proposed and analyzed. The results indicate that the security information measures firm take in advance can effectively increase the waverers' willingness to purchase personalized products. Government punishment helps to facilitate a win‐win situation but does not work when proportion of wavers in the market is very small. Furthermore, the responses of equilibrium strategies and payoffs to related characteristics are discussed numerically. Finally, some managerial insights are recommended to firms who provide personalized products for making proper security investment and to government departments for formulating security punishment policies.

In this paper, we look into the optimal sales strategy of e-commerce platform which sells fresh-product advertising in advance-selling period and spot-selling period, respectively, when advance-selling and spot-selling integration mode is applied. The study found that, with the increase of advertising investment, the marginal benefit of e-commerce platform is declining; whether advertising during the advance-selling period or the advertising during the spot-selling period, the e-commerce platform can improve its profit, but under the same advertising effect factor, the optimal advertising investment for the sale period is smaller, and the profit when advertising in the advance-selling period is greater. When advertising in different periods, the optimal advertising and price strategy depends on the maximum valuation of consumers about the product and the advertising effect factor.

In cold chain logistics, fresh agricultural products are susceptible to deteriorate due to the passage of time in the distribution process. To reduce the loss of cargo, this research integrates the traditional refrigeration cost into the freshness-keeping cost invested in the process of transportation and unloading goods. We rely on the investment of freshness-keeping cost to reduce the cargo damage cost caused by the distribution process and then propose a new vehicle routing problem (VRP). According to all relevant costs, this research builds a mathematical model with the goal of minimizing the total distribution cost. A hybrid ant colony optimization is designed to solve the problem, and the effectiveness of the model and algorithm are verified through two sets of comparative experiments. To determine which products should be invested in freshness-keeping cost to reduce the total distribution cost, we perform numerical analysis on the relevant parameters in the model. Results provide decision-making references for cold chain logistics distribution enterprises in the design of distribution routes.

Fresh produce has increasingly become an important part of people’s diet. However, the loss of fresh produce in the supply chain has existed for a long time and is difficult to overcome. Some companies use their own information management systems or use information systems built by other companies to release and manage fresh agricultural product information in a timely manner, thereby reducing product loss caused by the “bullwhip effect.” However, this will also bring pressure on construction costs and the risk of information leakage. Based on the evolutionary game model, this paper conducts process modeling and analysis on the behavior of enterprise groups participating in information sharing. It is concluded that the greater the difference between the income obtained through information sharing and the cost of building information system, the higher the likelihood of enterprises participating in information sharing. In addition, the greater the profit from the construction of information platform, the smaller the profit of “free rider,” and the smaller the risk of information leakage, the greater the enthusiasm of enterprises to participate in information sharing. Finally, some suggestions are proposed from the perspective of maximizing supply chain benefits.

At present, China regards supply chain innovation and application as a new national strategy, which leads to a large demand for applied supply chain professionals. In order to provide useful references for applied universities that have already set up the supply chain management (SCM) program and promote the healthy development of Chinaʼs application-oriented SCM for undergraduates, this paper discussed the necessity and feasibility of setting up the SCM program in Chinese universities. In addition, it analyzes the current situation of SCM programs from three aspects involving approval, type of universities, and issues of application-oriented undergraduate. Based on practical experience in specialty development, in this paper, specific measures were shown from three dimensions: requirements of knowledge, ability, and quality, curriculum setting, and teaching material design.