Search Results (89)

Synergizing soil quality improvement and greening for increased yields are essential to ensuring grain security and developing sustainable agriculture, which has become a key issue in agricultural cultivation. This study considers a contract farming supply chain composed of a risk-averse farmer and a risk-neutral firm making green and efficient technology (GET) investments, which refers to the use of technology monitoring to achieve fertilizer reduction and yield increases with yield uncertainty. Based on the CvaR (Conditional value at Risk) criterion, the Stackelberg game method is applied to construct a two-level supply chain model and analyze different cooperation mechanisms. The results show that when the wholesale price is moderate, both sides will choose the cooperative mechanism of cost sharing to invest in technology; the uncertainty of yield and the degree of risk aversion have a negative impact on the agricultural inputs and GET investment, and when yield fluctuates greatly, the farmer invests in GET to make higher utility but lowers profits for the firm and supply chain. This study provides a theoretical basis for GET investment decisions in agricultural supply chains under yield uncertainty and has important practical value for promoting sustainable agricultural development and optimizing supply chain cooperation mechanisms. Full article

As global energy systems shift to low-carbon models, microgrid systems play an increasingly vital role in decentralized energy management. This study proposes a collaborative scheduling strategy, incorporating both power and carbon contribution for multi-microgrid systems. Through the utilization of a cooperative Stackelberg game and a Nash bargaining model, a bi-level game framework is established between grid operators and microgrid alliances, enabling efficient resource sharing and equitable benefit distribution. To accurately assess each microgrid’s impacts, a VCG (Vickrey–Clarke–Groves)-based mechanism is introduced to quantify its marginal contribution to both power supply and carbon mitigation. The contribution factors are then embedded into the bargaining process, guiding incentive-compatible allocation. Furthermore, to improve computational efficiency and enable distributed problem-solving, an enhanced analytical target cascading (ATC) algorithm is applied. Experimental results reveal that this approach improves both economic and environmental performance, effectively reducing carbon emissions and dependence on the main grid. Full article

In UGC-based knowledge trading platforms, the abundance of personalized content often leads to varying quality levels. By incorporating embedded advertising, platforms can incentivize knowledge producers to produce high-quality content; however, the uncertainty in managing embedded advertisements increases the complexity of pricing knowledge products. This paper examines the impact of embedded advertising on the pricing of knowledge products, aims to maximize the profits of both knowledge producer and the platform. Based on Stackelberg game theory, two pricing decision models are developed under different advertising management modes: the platform-managed mode (where the platform determines the advertising intensity) and the advertiser-managed mode (where the advertiser determines the advertising intensity). The study analyzes the effects of UGC product quality, consumer sensitivity to advertising, and power structure on knowledge product pricing, and derives threshold conditions for optimal pricing. The results indicate that (1) When the quality of UGC knowledge product exceeds a certain threshold, platform-managed advertising becomes profitable. (2) Under the platform-managed mode, both the platform and knowledge producer can adopt price-increasing strategies to enhance profits. (3) Under the advertiser-managed mode, the platform can leverage differences in power structure to optimize revenue, while knowledge producer can actively enhance his pricing power to achieve mutual benefits with the platform. This study provides theoretical support and practical guidance for advertising cooperation mechanisms and pricing strategies for knowledge products in UGC-based knowledge trading platforms. Full article

In the context of global e-commerce platform supply chains dominated by Alibaba and Amazon, power reconfiguration among tripartite stakeholders (platforms, manufacturers, and retailers) remains a critical yet underexplored issue in supply chain contract design. To analyze the strategic interactions between platforms, manufacturers, and retailers, as well as how platforms select the contract format within a tripartite supply chain, this study proposes a Stackelberg game-theoretic framework incorporating participation constraints to compare fixed-fee and revenue-sharing contracts. The results demonstrate that revenue-sharing contracts significantly enhance supply chain efficiency by aligning incentives across members, leading to improved pricing and sales outcomes. However, this coordination benefit comes with reduced platform dominance, as revenue-sharing inherently redistributes power toward upstream and downstream partners. The analysis reveals a nuanced contract selection framework: given the revenue sharing rate, as the additional value increases, the optimal contract shifts from the mode RR to the mode RF, and ultimately to the mode FF. Notably, manufacturers and retailers exhibit a consistent preference for revenue-sharing contracts due to their favorable profit alignment properties, regardless of the platform’s value proposition. These findings may contribute to platform operations theory by (1) proposing a dynamic participation framework for contract analysis, (2) exploring value-based thresholds for contract transitions, and (3) examining the power-balancing effects of alternative contract formats. This study offers actionable insights for platform operators seeking to balance control and cooperation in their supply chain relationships, while providing manufacturers and retailers with strategic guidance for contract negotiations in platform-mediated markets. These findings are especially relevant for large e-commerce platforms and their partners managing the complexities of contemporary digital supply chains. Full article

In a multi-beam communication scenario where Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communications coexist, the limited spectrum of resources force V2V users to reuse the orthogonal frequency bands allocated to I2V, inevitably introducing cross-layer interference between I2V and V2V. Furthermore, the adoption of a multi-beam communication architecture exacerbates beam interference, significantly degrading the overall network’s communication and sensing performance. To address these challenges, this paper proposes an integrated sensing and communication (ISAC) beam allocation algorithm, termed IMSBA, which jointly optimizes beam direction, transmission power, and spectrum resource allocation to effectively mitigate the interference between I2V and V2V while maximizing the overall network performance. Specifically, IMSBA employs a joint optimization framework combining Multi-Agent Proximal Policy Optimization (MAPPO) with a Stackelberg game. Within this framework, MAPPO leverages vehicle perception data to dynamically optimize V2V beam steering and frequency selection, while the Stackelberg game reduces computational complexity through hierarchical decision-making and optimizes the joint power allocation among V2V users. Additionally, the proposed scheme incorporates a V2V cooperative sensing domain-sharing mechanism to enhance system robustness under adverse conditions. The experimental results demonstrated that, compared with existing baseline schemes, IMSBA achieved a 92.5% improvement in V2V energy efficiency while significantly enhancing both communication and sensing performance. This study provides an efficient and practical solution for spectrum-constrained scenarios in millimeter-wave Internet-of-Things (IoT), offering substantial theoretical insights and practical value for the efficient operation of intelligent transportation system (ITSs). Full article

With the increasing connection between integrated natural gas, thermal energy, and electric power systems, the integrated energy system (IES) needs to coordinate the internal unit scheduling and meet the different load demands of customers. However, when the energy subjects involved in scheduling are engaged in conflicts of interest, aspects such as hierarchical status relationships and cooperative and competitive relationships must be considered. Therefore, this paper studies the problem of achieving optimal energy scheduling for multiple subjects of source, storage, and load under the same distribution network while ensuring that their benefits are not impaired. First, this paper establishes a dual master-slave game model with a shared energy storage system (SESS), IES, and the alliance of prosumers (APs) as the main subjects. Second, based on the Nash negotiation theory and considering the sharing of electric energy among prosumers, the APs model is equated into two sub-problems of coalition cost minimization and cooperative benefit distribution to ensure that the coalition members distribute the cooperative benefits equitably. Further, the Stackelberg-Stackelberg-Nash three-layer game model is established, and the dichotomous distributed optimization algorithm combined with the alternating direction multiplier method (ADMM) is used to solve this three-layer game model. Finally, in the simulation results of the arithmetic example, the natural gas consumption is reduced by 9.32%, the economic efficiency of IES is improved by 3.95%, and the comprehensive energy purchase cost of APs is reduced by 12.16%, the proposed model verifies the sustainability co-optimization and mutual benefits of source, storage and load multi-interested subjects. Full article

In the following study, power allocation in underwater cooperative communication systems was investigated using game theory. To balance the energy consumption of nodes, extend their lifespan, and improve communication quality, a Stackelberg power control algorithm based on a non-uniform pricing mechanism was proposed. The interaction model between the transmitting and relay nodes was constructed as a two-layer Stackelberg game, which consisted of leaders and followers. The transmitting node acts as the leader, with its objective function comprising its transmission cost and the purchasing transmission power cost of the relay nodes. The relay nodes act as followers, with their objective function comprising revenue from selling power and their transmission cost. In addition, the remaining energy is incorporated into the objective function to balance the energy consumption of the nodes. Our simulation results indicate that, compared with algorithms that do not consider remaining energy, this algorithm improves the communication quality of the cooperative system and extends the network’s lifetime. Full article

The engagement of emerging market participants in electricity markets exerts dual influences on price formation mechanisms and operational dynamics. To quantify the impacts on locational marginal prices and stakeholders’ economic interests when EV aggregators (EVAs), cloud energy storage operators (CESSOs), and load aggregators (LAs) collectively participate in market competition, this study develops a bi-level game-theoretic framework for market equilibrium analysis. The proposed architecture comprises two interdependent layers: The upper-layer Stackelberg game coordinates strategic interactions among EVA, LA, and CESSO to mitigate bidding uncertainties through cooperative mechanisms. The lower-layer non-cooperative Nash game models competition patterns to determine market equilibria under multi-agent participation. A hybrid solution methodology integrating nonlinear complementarity formulations with genetic algorithm-based optimization was developed. Extensive numerical case studies validate the methodological efficacy, demonstrating improvements in solution optimality and computational efficiency compared to conventional approaches. Full article

The current generation of integrated power systems is represented by the Adaptive Power and Thermal Management System (APTMS). The coupled performance between the APTMS and the aircraft engine significantly increases the difficulty of energy management and optimization. This article establishes an energy-coupled Amesim model of the APTMS and the aircraft engine to analyze performance conflicts. Energy optimization based on the Stackelberg game model is established, with the aircraft engine as the leader and the APTMS as the follower. The Adaptive Chaotic Particle Swarm Optimization (ACPSO) algorithm is introduced to search for the game equilibrium solution. Simulation results indicate that this energy management strategy can achieve equilibrium and alleviate performance conflict. In flight, the optimal strategy depends on thrust–fuel flow characteristics and cooling power demand. Finally, compared with the multi-objective optimization algorithm MOPSO and the non-cooperative Cournot game model, the advantages of this energy management system based on the Stackelberg game are verified. Full article

As environmental challenges become increasingly pressing, companies are integrating low-carbon innovations into supply chain management to achieve economic performance while promoting environmental sustainability and social responsibility. This study explores how low-carbon innovation in supply chains can be driven by consumer orientation and market-driven strategies, contributing to sustainable development. Using Stackelberg game theory, the study develops centralized and decentralized decision-making models and solves them through differential game methods. Numerical simulations are employed to analyze the impact of consumer preferences for low-carbon products and market strategies on supply chain decisions and overall profitability. The results show that consumer demand for low-carbon products plays a crucial role in driving low-carbon innovation within supply chains. Market strategies, particularly their sensitivity to consumer preferences, significantly influence decision-making processes. Further analysis reveals that the centralized decision-making model offers greater advantages in resource optimization and responsiveness to market shifts, while the decentralized model allows independent decision-making by supply chain participants, balancing competition and co-operation. This enables firms to achieve both economic benefits and reduce their environmental footprint, thereby contributing to sustainable development. This research highlights the importance of aligning consumer demand with market strategies to foster low-carbon innovation. The findings provide valuable theoretical insights and practical strategies to help supply chain companies enhance their competitiveness and contribute to the sustainable development of global supply chains. Full article

Against the backdrop of an increasingly sound supply chain system and thriving e-commerce, it is becoming increasingly common for retailers to introduce their own brands of products and for manufacturers to open up direct online sales channels. The existence of these two supply chain decisions is complex and involves interactions. Moreover, the introduction of online direct sales channels will bring about differences in prices and service quality between channels, resulting in a free-riding effect on the internet. However, existing related research rarely considers the role of network free-riding effect in this supply chain system. This article integrates the network free-riding effect into the supply chain model by setting the network free-riding rate. According to whether retailers introduce their own brand products and manufacturers open up online direct sales channels, four supply chain scenarios are formed, and the reverse recursion method is used to obtain the profit functions for each of these four scenarios. Then, a Stackelberg game model is established to determine the response strategies of manufacturers and retailers based on the changes in profits of manufacturers caused by retailer decisions and the changes in profits of retailers caused by manufacturer decisions. Through analysis, it was discovered that a key factor affecting decision-making within the supply chain system is the retailer’s channel advantage. When the channel advantage of retailers is strong, manufacturers will open up online direct sales channels to weaken the channel advantage of retailers. Retailers will not introduce their own brand products but are more inclined to cooperate with manufacturers. When the channel advantage of retailers is weak, retailers will attract consumers and consolidate their channel advantage by introducing high-quality, low-priced private label products, while manufacturers will maintain cooperation with retailers and adopt a strategy of not opening up online direct sales channels. We also analyzed the Nash equilibrium state under different channel advantages of retailers. Full article

Unmanned aerial vehicles (UAVs) can be utilized as airborne base stations to deliver wireless communication and federated learning (FL) training services for ground vehicles. However, most existing studies assume that vehicles (clients) and UAVs (model owners) offer services voluntarily. In reality, participants (FL clients and model owners) are selfish and will not engage in training without compensation. Meanwhile, due to the heterogeneity of participants and the presence of free-riders and Byzantine behaviors, the quality of vehicles’ model updates can vary significantly. To incentivize participants to engage in model training and ensure reliable outcomes, this paper designs a reliable incentive mechanism (FedBeam) based on game theory. Specifically, we model the cooperation problem between model owners and clients as a two-layer Stackelberg game and prove the existence and uniqueness of the Stackelberg equilibrium (SE). For the cooperation among model owners, we formulate the problem as a coalition game and based on this, analyze and design a coalition formation algorithm to derive the Pareto optimal social utility. Additionally, to achieve reliable FL model updates, we design a weighted-beta (Wbeta) reputation update mechanism to incentivize FL clients to provide high-quality model updates. The experimental results show that compared to the baselines, the proposed incentive mechanism improves social welfare by 17.6% and test accuracy by 5.5% on simulated and real datasets, respectively. Full article

This paper takes corporate social responsibility goodwill and consumers’ reference low-carbon level as endogenous variables of joint carbon emission reduction in the “supplier–manufacturer–retailer–consumer” supply chain system. The joint carbon emission reduction strategies of this four-tier system are analyzed from a dynamic perspective by considering random factors that affect the endogenous variables. Three stochastic differential games are proposed to examine the mechanism between each player, namely the cooperative model, Nash non-cooperative model, and Stackelberg master–slave model. Compared to the Nash non-cooperative game, the manufacturer/supplier-led Stackelberg master–slave game leads to Pareto improvement in the profits of the entire supply chain system and each player. The cooperative game demonstrates the highest expected emission reduction and corporate social responsibility goodwill, but also the highest variance. More importantly, the reference low-carbon level embraces consumers’ subjective initiative in the dynamic of carbon emission reduction. This level is an internal benchmark used to compare against the observed low-carbon level. This paper provides a theoretical foundation for strategic decision-making in emission reduction, contributing to sustainable development. By addressing environmental, economic, and social sustainability, it promotes climate action through carbon reduction strategies and offers policy recommendations aligned with the Sustainable Development Goals. Full article

In the context of supply disruption, having a resilient supply chain is crucial for the survival and growth of enterprises. It is also essential for gaining a competitive advantage in a turbulent environment. Enterprises need to invest in supply chain resilience to better deal with future uncertainties. This paper constructs a Stackelberg game model with the manufacturer as the leader and the retailer as the follower. We explored how supply chain-related factors under supply interruption risk affect supply chain resilience investment, and studied how to choose supply chain coordination strategies to improve the effectiveness of manufacturer capacity recovery and mutual profits in the context of supply interruption. The study also analyzes the asymmetrical impact of changes in product order quantity, supply disruption probability, and the capacity recovery coefficient on retailer decision-making and the profits of supply chain members. The results indicate that manufacturer profits are negatively correlated with supply disruption probability, while retailer profits are positively correlated with supply disruption probability when product order quantities are low and negatively correlated when product order quantities are high. The supply chain resilience investment is positively correlated with the supply disruption probability. Furthermore, the effectiveness of the cost-sharing contract is closely related to product order quantity and supply disruption probability. When the product order quantity $d<{\alpha }_L{\displaystyle \frac{-c[\left(1-\xi \right)a_L+\xi a_H]+s{\alpha }_H\xi +w{\alpha }_L(1-\xi )}{k}}$ or ${\alpha }_H{\displaystyle \frac{-c[\left(1-\xi \right)a_L+\xi a_H]+s{\alpha }_H\xi +w{\alpha }_L(1-\xi )}{k}}<d<{\displaystyle \frac{{\alpha }_H[\left(1-\xi \right)a_L+\xi a_H](w-c)}{k}}$, manufacturers can withstand the risk of supply interruption by investing in supply chain resilience alone. But when the product order quantity is ${\alpha }_L{\displaystyle \frac{-c[\left(1-\xi \right)a_L+\xi a_H]+s{\alpha }_H\xi +w{\alpha }_L(1-\xi )}{k}}<d<{\alpha }_H{\displaystyle \frac{-c[\left(1-\xi \right)a_L+\xi a_H]+s{\alpha }_H\xi +w{\alpha }_L(1-\xi )}{k}}$ and ${\displaystyle \frac{{\alpha }_H[\left(1-\xi \right)a_L+\xi a_H](w-c)}{k}}<d$, the use of cost-sharing contracts is more effective. Additionally, when the sensitivity analysis is conducted, the capacity recovery coefficient positively correlates with supply chain profits in a decentralized mode. However, under the cost-sharing contract mode, it exhibits a U-shaped fluctuation pattern, indicating that the impact of improving capacity recovery efficiency on the profits of both parties is not symmetrical and linear. As $\xi$ approaches 0.5, the profits of manufacturers and retailers decrease. Instead, it undergoes an initial decline followed by a subsequent increase, highlighting the nonlinear benefits of capacity recovery strategies under the cooperative approach. Full article

Compared to the development history of traditional FVs (fossil-fuel vehicles), although NEVs (new-energy vehicles) have many advantages and huge development potential, they are still in the early stages of development. The current research about NEV diffusion mainly focuses on policies, competition, and cooperation with FVs, as well as consumer-related factors, in which consumers are generally assumed as rational. In order to study the impact of irrational consumer factors on NEV diffusion, this study takes the prospect theory into consideration. Through a literature analysis, the loss-aversion factor is introduced to establish a Stackelberg game model, in which the FV market is the leader and the NEV market is the follower. A backward induction method is used to solve the optimal decision strategy of each party in the game, and the Python Sympy library is employed for calculation and simulation. The results show that as the loss-aversion reference point $\lambda$ increases, the price, demand, and profit of NEVs increase, while the price, demand, and profit of FVs decrease, and their sum profit also shows a downward trend. While, as the loss-aversion degree k increases, the price, demand, and profit of NEVs decrease, while the price, demand, and profit of FVs increase, and their sum profit also shows an upward trend. Full article