Fangfang Guo

In order to better understand the competitive dynamics between e-commerce platforms and traditional retail outlets, a Stackelberg game model was developed. Subsequently, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed to determine the Pareto solution set for this multi-objective optimization problem. The findings reveal that: a) The effect of consumer reference quality can lead enterprises to adjust their strategy levels downwards, potentially resulting in profit loss under certain conditions. b) When the influence of competitive intensity on market demand is minimal, a reduction in enterprise profits occurs in both centralized and cost-sharing decision-making frameworks, with more significant detriment observed in the cost-sharing mode; conversely, when the influence is substantial, enhancements in competitive intensity can significantly increase overall system profits. c) The model's validity was confirmed through the application of the NSGA-II.

Manufacturers are increasingly leveraging both online and offline channels to diversify their sales strategies. However, competition between these channels presents challenges in maximising profits for all parties involved. This study investigates the use of cost-sharing contracts by manufacturers to promote marketing in both online and offline channels, with the goal of achieving Pareto improvements in supply chain profitability. The model also accounts for consumers' reference quality perceptions in online channels, offering a comprehensive evaluation of how cost-sharing contracts influence the operational strategies and performance of both online and offline enterprises. An empirical analysis is conducted using the "US Stores Sales" dataset from Kaggle, comprising 4,249 samples with 20 recorded characteristics per sample. The findings indicate that: (1) Cost-sharing in marketing efforts facilitates a Pareto improvement in profits for manufacturers, online enterprises, and offline retailers, with manufacturers experiencing the most significant benefit. (2) When the manufacturer assumes a larger share of marketing costs for one channel (e.g., online or offline) and a smaller share for the other, the party receiving the higher cost-sharing proportion typically sees increased profitability, while the other party's profitability may diminish. (3) Empirical analysis suggests that manufacturers should prioritise supporting online businesses' marketing activities, as this strategy is more likely to result in higher overall profits for the manufacturer. (4) Interestingly, when equal cost-sharing proportions are offered to both online and offline enterprises for the sake of fairness, the manufacturer's profitability is enhanced. Moreover, the profitability of online enterprises tends to increase when the equal cost-sharing proportion is smaller. These findings validate the proposed model and underscore the critical role of strategic cost-sharing contracts in optimising Online to Offline (O2O) supply chain performance. Further research could explore the implications of varying consumer preferences and digitalisation trends on the effectiveness of such strategies.

Correctly responding to the arrival of unexpected innovative events can help franchise system members rationalize their operational strategies and cooperative alliance models to improve performance, while the exact timing of successful product innovation is often unknown. Based on this, we consider the impact of innovation success on the franchising system, find the optimal strategies and system profits under three modes of horizontal cooperative alliance, non-cooperative and vertical cooperative alliance in the system with the help of differential game theory, and explore the impact of uncertainty of innovation realization on the optimal operation strategies and cooperative alliance mode selection of system members. It is found that: the prediction of the likelihood of innovation realization changes the time preference of decision makers to focus more on current profits; both horizontal cooperative alliance and vertical cooperative alliance models are effective in achieving Pareto improvements in total system profits, regardless of before and after innovation, but the improvements are more pronounced for the vertical cooperative alliance model, and the improvements are more significant at high realization rates-high enhancement rates; regardless of before and after innovation, for franchisors, the franchisor’s R&D innovation effort increases, while the cooperation or non-cooperation among franchisees does not affect their R&D effort. For franchisors, vertical cooperative alliances with franchisees are more likely to promote their local service level and advertising investment.

The continuous evolution of consumer behavior in the modern era of consumption has prompted enterprises to explore the underlying behavioral factors of consumers and cater to their particular needs. Moreover, developing a rational operational behavior model and responding effectively to the dynamic market environment have become critical concerns for businesses. This study examines the impact of consumer reference price effects and enterprise short-sighted behavior on strategic selection and performance, employing differential game theory to construct a game model between manufacturers and retailers. Utilizing Behrman's continuous dynamic programming theory, analytical solutions for various models are derived, followed by comparative analyses and numerical examples. The research reveals that: (1) manufacturers' behavior patterns are found to be dominant, favoring far-sighted behavior, which not only enhances profits but also enables consumers to access higher quality and cost-effective products; retailers should opt for collaboration with far-sighted manufacturers and exhibit a preference for short-sighted behavior. (2) In terms of overall system profit, the FM model emerges as the optimal combination. (3) When the reference price effect has a small impact on market demand, enterprises can make use of the reference price effect to actively promote marketing and gain profit; as the influence increases, intensifying the degree of influence effectively augments profits.

The concept of "green supply chain" has gained increasing attention in the pursuit of sustainable development by enterprises globally, leading to the optimization of supply chain management. However, the behavior patterns of some members in green supply chains exhibit differences. Hence, analyzing the choices of different behavior patterns among green supply chain members is of practical significance in improving supply chain governance and achieving sustainable development. In this study, a differential game model is constructed using differential game theory to analyze the behavior patterns among members of a green supply chain. Analytical solutions under different models are obtained with the aid of Bellman continuous dynamic programming theory, and comparative analysis and numerical examples are carried out to explore the operation strategies of green supply chain members and the choice of shortsighted and far-sighted behavior patterns. Our findings indicate that suppliers playing a leading role in the green supply chain should exhibit far-sighted behavior as decision makers, while retailers should also display far-sighted behavior. This study adopts a dynamic perspective and is innovative and cutting-edge, providing targeted recommendations for enterprises and serving as a reference for the future development of green supply chain management.

The scientific location and layout of emergency material storage and rescue points in urban areas are critical aspects of emergency management. In this study, a multi-objective programming optimization model was constructed based on related theories, incorporating multiple goal combinations with different dimensions according to various disaster scenarios and urban emergency needs. The weight factors of emergency timeliness, economy, and safety were considered, and the multi-objective model optimization problem was transformed into a single-objective comprehensive optimization model problem using the weight method. The analysis decision function was utilized to study the transformation and solution method of the urban emergency rescue point location model. Heuristic optimization algorithms were employed to perform average segmentation calculations on the preset neighborhoods, constantly changing and narrowing the neighborhood range until the algorithm termination conditions were met, approaching the domain range of the optimal solution. Additionally, another precision parameter was utilized to control the accuracy of the final solution neighborhood range. The optimization of emergency vehicle scheduling was used to synergistically solve the problem of reserve rescue point location layout and optimization solution. The results of the example demonstrate the feasibility of constructing a multi-objective model with multiple combinations of different dimensions of objectives and the rationality of the Dijkstra heuristic optimization algorithm used. This study provides multiple methodologies and alternative site selection plans for decision-makers to select the required multi-objective reserve rescue point location model based on different urban disaster situations and their own emergency rescue needs.

In the e-commerce market, many e-commerce platforms act as resellers when selling products, and act as agents when selling other products. In the sales process, e-commerce platforms can either build their own blockchain anti-counterfeit traceability platforms or cooperate with third-party blockchain anti-counterfeit traceability platforms. This will generate four scenarios: 1) reseller, building its own platform (RE); 2) reseller, cooperating with a third-party platform (RO); 3) agent, building its own platform (ME); 4) agent, cooperating with a third-party platform (MO). Therefore, this paper constructs a differential game model under four modes to explore the interaction between the choice of sales mode and the choice of anti-counterfeit traceability service strategy. The results show that suppliers’ profits are influenced by various aspects. On the one hand, in small-scale markets, the situation in which suppliers can realize higher profits evolves from ME to RO as the wholesale price increases, and in large-scale markets, suppliers are more profitable in the ME mode. On the other hand, with the increase of market scale and the decrease of unit price of anti-counterfeit traceability service of third-party platform, the situation that suppliers can achieve higher profit evolves from RE to RO and then to RE. For e-commerce platform, self-built platform is a better choice. In the small-scale market, as the market size increases, the cost performance of anti-counterfeit traceability service decreases, and the best choice for e-commerce platform evolves from resale to agency sales, and in the large-scale market, the best choice for e-commerce platform is resale.