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An optimal credit term decision in supply chain finance often needs to be made in a dynamic way considering the varying market demand among other factors. We study the dynamic credit term optimization problem (DCTOP), where a supplier determines the credit term in conjunction with its production and inventory decision, while anticipating a buyer's order quantity in a leader-follower game setting. The DCTOP is first approached to using a continuous time optimal control model, with analytical results characterizing the structural properties of the optimal solution. To complement the structural properties, we then develop a discrete time bilevel programming model to provide computationally tractable and implementable numerical solutions. A comprehensive computational study shows significant advantage of our optimal solutions over the heuristic credit term rules in practice, and provides managerial insights regarding the impacts of key problem parameters on the optimal solutions and coordination scheme. [ABSTRACT FROM AUTHOR]

This paper studies life reinsurance as a solution to default risk in equity-linked life insurance products with surplus participation. The problem is considered under both perfect and asymmetric information about the insurer's risk profile between the reinsurer and the insurer. In both cases, we analyze the existence of proportional reinsurance and the impact of the insurer's investment strategies on optimal reinsurance. We find that the more risky the insurer's investment portfolio is, the more risk the insurer tends to transfer to the reinsurer. Further, information asymmetry leads to additional information costs and consequently to a larger reinsurance premium and a lower reinsurance share. [ABSTRACT FROM AUTHOR]

This paper studies a partially observed stochastic Stackelberg game with two followers, where state satisfies a linear stochastic differential equation of mean-field type, and cost functionals are quadratic. Using decomposition technique and backward separation approach, we derive the followers' optimal strategy. The leader focuses on an optimal control problem driven by a fully coupled mean-field forward–backward stochastic differential equation with conditional expectation. By layer-by-layer decomposition technique, some inequalities and Riccati equations, we not only get the existence and uniqueness of solution to the corresponding Hamiltonian system but also give a feedback form of Stackelberg solution. Finally, we tackle a government debt problem by the above theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to study the production of consumption goods within a price floor carbon emission trading scheme. A stochastic Stackelberg game is set up between the environment authority (Player 1), and the manufacturer/producer (Player 2). In such a hybrid emission trading scheme, the authority sets an emissions target and producers will choose to participate in carbon trading on this basis, subject to a stochastic demand with information delayed. When the stochastic demand is determined by the market sale price and the scale of carbon emission reduction, we apply the maximum principle to obtain both the optimal production and market price. We show when the demand is determined by both the market sales price and carbon emission reduction, manufacturers are persuaded to set the market sales price first (ex ante incentive) and then proceed to a carbon trading scheme, thus providing a backward-looking advantage to manufacturers. Consequently, the manufacturer and the environmental authority can find a subgame-perfect Nash equilibrium via backward induction. [ABSTRACT FROM AUTHOR]

The collaborative optimization of speed and energy management for fuel cell hybrid electric vehicles (FCHEVs) in car-following scenario is an important and effective means to achieve vehicle energy conservation. In view of the unclear coupling relationship between dual energy source degradation and energy consumption in existing strategies, and the difficulty of considering both global optimization and real-time performance, this paper proposes a Stackelberg game based FCHEV following energy management strategy. Firstly, models for energy consumption and performance degradation of fuel cells/lithium batteries are established and incorporated into a unified dimensional comprehensive vehicle cost function. Secondly, a hierarchical decoupling based energy management strategy for vehicle following is proposed to achieve decoupling control of vehicle following speed and power distribution. Thirdly, considering safety, comfort, fuel economy, and energy durability, a bi-level programming model corresponding to the car-following control layer and energy management layer is established. Based on the Stackelberg game theory, a bi-level differential genetic algorithm is designed to offline optimize the core parameters of the strategy. Finally, simulation and experimental results show that compared to the model predictive control method, this method can reduce average vehicle spacing error by 37.7%, average impact by 2.4%, equivalent hydrogen consumption by 9.3%, and energy degradation cost by 13.9%, achieving a balance between optimized performance and real-time performance. [ABSTRACT FROM AUTHOR]

In today's power systems, electric vehicles (EVs) constitute a significant factor influencing electricity dynamics, with their important role anticipated in future smart grid systems. An important feature of electric vehicles is their dual capability to both charge and discharge energy to/from their battery storage. Notably, the discharge capability enables them to offer vehicle-to-grid (V2G) services. However, most V2G research focuses on passenger cars, which typically already have their own specific usage purposes and various traveling schedules. This situation may pose practical challenges in providing ancillary services to the grid. Conversely, electric school buses (ESBs) exhibit a more predictable usage pattern, often deployed at specific times and remaining idle for extended periods. This makes ESBs more practical for delivering V2G services, especially when prompted by incentive price signals from grid or utility companies (UC) requesting peak shaving services. In this paper, we introduce a V2G energy sharing model focusing on ESBs in various schools in a single community by formulating the problem as a leader–follower game. In this model, the UC assumes the role of the leader, determining the optimal incentive price to offer followers for discharging energy from their battery storage. The UC aims to minimize additional costs from generating energy during peak demand. On the other hand, schools in a community possessing multiple ESBs act as followers, seeking the optimal quantity of discharged energy from their battery storage. They aim to maximize utility by responding to the UC's incentive price. The results demonstrate that the proposed model and algorithm significantly aid the UC in reducing the additional cost of energy generation during peak periods by 36% compared to solely generating all electricity independently. Furthermore, they substantially reduce the utility bills for schools by up to 22.6% and lower the peak-to-average ratio of the system by up to 9.5%. [ABSTRACT FROM AUTHOR]

In this article, we investigate a model‐free adaptive funnel control framework for nonlinear multiagent systems under jamming attacks. An equivalent time‐varying linear data model is built for agents by utilizing the dynamic linearization approach. Then, a predefined funnel function is presented such that the tracking errors are expected to evolve within a predetermined range. The transient and steady‐state behaviors of the tracking error can be improved by adjusting the parameter values of the funnel function. Moreover, the Stackelberg game is employed to determine the optimal transmission power between the transmitters and jammers considering the state of the transmission channel, which influences the jamming attack rate. Based on funnel control, a model‐free adaptive funnel control scheme is presented to guarantee that the tracking error remains within a funnel boundary (even in the presence of jamming attacks) and the transient and steady‐state performances are improved. The effectiveness of the proposed algorithm is further demonstrated through simulations. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce, the online channels encroaching on the offline sales market are becoming more serious, which will definitely harm the offline market. Moreover, there exists a certain percentage of consumers (mostly elderly people) who are not able to purchase online because they lack digital skills. Therefore, understanding the impact of the purchase channel preference and service level on pricing decisions is vital for the dual-channel supply chain management. Focusing on the channel preference and service level, we first develop an optimal pricing model containing centralized and decentralized decision-making for an online and offline retailer by deploying the Stackelberg game. We first develop a Stackleberg game to capture such a dual-channel supply chain with the offline channel preference and service level. Secondly, under centralized decision-making, we derive the optimal retail prices and obtain the optimal total profit. Thirdly, under decentralized decision-making, we obtain the optimal retail prices and optimal total profit as well. Moreover, extensive monotonicity properties when system parameters change are obtained. Relying on the theoretical results, firstly, we show that the improvement of the offline service level would lead to higher pricing of the commodities for both online and offline channels. From our numerical results, when the service level is improved, the offline and online optimal pricing increases by 47.5% and 31.1%, respectively, which may contradict the conventional belief that the improvement of one channel would harm another one. Secondly, we demonstrate that the benefit of improving the offline service level has a diminishing marginal effect. The numerical results show that when the current service level is low, the effectives of improving the service level is roughly five times that when the service level is high. This indicates that the investment in improving the offline service level should not be unlimited. Thirdly, we show that the pricing decision under centralized decision-making should be adopted with the existence of both the offline channel preference and offline service. [ABSTRACT FROM AUTHOR]

It is of great significance to study the effectiveness of knowledge transfer between the new and the veteran employees within enterprises for promoting sustainable innovation from the perspective of innovation quality management. However, few studies have examined the impact of innovation quality on the effectiveness of knowledge transfer between the new and veteran employees. In addition, knowledge of how reward and punishment incentives affect the effectiveness of knowledge transfer in innovation quality management remains insufficient. Since the amount of knowledge transfer is an important aspect of the effectiveness of knowledge transfer, this paper constructs a Stackelberg game model with an innovation-quality-oriented threshold of the knowledge transfer amount and investigates the amount of knowledge transfer between new and veteran employees in the collaborative innovation of research and development (R&D) projects within enterprises. A case study was used to reveal that the innovation-quality-oriented threshold for the knowledge transfer amount can effectively promote the amount of knowledge transfer between the new and the veteran employees in collaborative innovation. Moreover, reward is more effective than punishment in promoting the amount of knowledge transfer to exceed the innovation-quality-oriented threshold. This study enriches the theories of knowledge transfer games under quality management. By virtue of end-to-end project management strategies, modern multimedia technologies, and reward incentives this study can be used to conduct quality control during project execution, to promote knowledge retention in R&D projects, the innovation quality of projects, and the achievement of the Sustainable Development Goals (SDGs). The research methodology employed in this paper was limited to a case study, and the data utilized are not empirical data. [ABSTRACT FROM AUTHOR]

Animal husbandry is an important industry related to the national economy and people's livelihood, and its coordinated development can continuously enhance the quality, efficiency, and competitiveness of the livestock industry, and better meet the people's diversified consumption demand for livestock and poultry products. In this paper, considering the government's green subsidy to the leading enterprises in the green supply chain of livestock products, the article uses the stackelberg game method to establish the decision-making models of the green supply chain of livestock products led by the co-operatives and the slaughtering and processing enterprises, respectively, and measures the optimal greenness of the livestock products. Different from the previous optimal decision-making and coordination of interests in the case of only considering economic benefits, this paper combines economic and ecological benefits to construct a coordination mechanism of interests in the green supply chain of livestock products in line with the concept of ecological priority and green development. The conclusions showed that: (1) Under the background of considering economic and ecological benefits, the influence of greenness sensitivity factor, freshness sensitivity factor, and government subsidy factor on the decision-making of green supply chain of livestock products is not affected by the operation mode and decision-making scenario, with the increase in the three factors, the greenness, freshness, and total benefits of the green supply chain of livestock products. (2) The greenness, freshness, and total benefits of green supply chain of livestock products are higher in the operation mode led by slaughtering and processing enterprises, which is more favorable to the development of green supply chain of livestock products from the perspective of supply chain development. (3) Compared with decentralized decision-making mode, both benefit-sharing and cost-sharing contracts increase the greenness and freshness of livestock products, significantly reduce the retail price of livestock products, which improves the benefits of the co-operatives, slaughtering and processing enterprises, 3PL (third-party logistics) service providers, and retailers as well as the entire green supply chain of livestock products. Based on the research findings, this article proposes relevant management insights to better promote the coordination of interests in the green supply chain of livestock products. [ABSTRACT FROM AUTHOR]

As the scale of units within virtual power plants (VPPs) continues to expand, establishing an effective operational game model for these internal units has become a pressing issue for enhancing management and operations. This paper integrates photovoltaic generation, wind power, energy storage, and constant-temperature responsive loads, and it also considers micro gas turbines as auxiliary units, collectively forming a typical VPP case study. An operational optimization model was developed for the VPP control center and the micro gas turbines, and the game relationship between them was analyzed. A Stackelberg game model between the VPP control center and the micro gas turbines was proposed. Lastly, an improved D3QN (Dueling Double Deep Q-network) algorithm was employed to compute the VPP's optimal operational strategy based on Stackelberg game theory. The results demonstrate that the proposed model can balance the energy complementarity between the VPP control center and the micro gas turbines, thereby enhancing the overall economic efficiency of operations. [ABSTRACT FROM AUTHOR]

This paper studies continuous-time models for newsvendor problems with dynamic replenishment, financial hedging and Stackelberg competition. These factors are considered simultaneously and the high-dimensional stochastic control models are established. High-dimensional Hamilton-Jacobi-Bellman (HJB) equations are derived for the value functions. To circumvent the curse of dimensionality, a deep learning algorithm is proposed to solve the HJB equations. A projection is introduced in the algorithm to avoid the gradient explosion during the training phase. The deep learning algorithm is implemented for HJB equations derived from the newsvendor models with dimensions up to six. Numerical outcomes validate the algorithm's accuracy and demonstrate that the high-dimensional stochastic control models can successfully mitigate the risk. [ABSTRACT FROM AUTHOR]

Decision-making regarding the low-carbon supply chain, considering corporate social responsibility (CSR) and the heterogeneous preferences of consumers, has become an urgent topic to be explored. This paper explores the decision-making problem of a low-carbon supply chain considering the heterogeneous preferences of consumers under different CSR situations, analyzes the influence of important parameters on each equilibrium solution, compares the size relationship of each equilibrium solution under different CSR situations, and verifies the conclusions obtained through numerical simulation. Then, based on the obtained equilibrium solution, a CSR evolutionary game model of the low-carbon supply chain is constructed, and the evolutionary stability strategies of the two sides on the CSR game are solved. Finally, the evolutionary trajectory of the game system is intuitively presented using a simulation method, and the influence of the main parameters on the evolutionary trends of the two sides is analyzed. The findings are as follows: (1) When both manufacturers and retailers undertake CSR, the retail price and wholesale price are their lowest, while carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility are the highest. (2) When a company undertakes CSR, carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility all increase with the increase in the CSR degree of the company and the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers. (3) The evolutionary stability strategy for both manufacturers and retailers is to undertake CSR. In addition, the initial proportion of manufacturers and retailers that undertake CSR, the low-carbon preference of low-carbon consumers, and the increase in the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers can encourage both members of the supply chain to undertake CSR. [ABSTRACT FROM AUTHOR]

It is challenging for decision‐makers (DMs) to deal with uncertainties in multi‐level agricultural water resource systems, where DMs independently make decisions but have different levels of power. In this paper, we model the multi‐level agricultural water resources system under deep uncertainties as a Stackelberg game, use multi‐level programming to solve equilibrium water allocation problems, and introduce robustness metrics into multi‐level programming to balance solution feasibility and model optimality within uncertain environments. The approach is applied to a shallow groundwater area with three decision levels, pursuing, from the top level to the bottom one, high food production, fair water allocation, and increased economic benefit. The model generated a series of optimal equilibrium solutions with different robustness degrees. DMs can choose "rational" solutions according to their acceptable costs, oriented robustness degree, expected objective values, and advance risk assessment of uncertainties. Among these solutions, we capture a critical point with high objective values and strong robustness, where DMs can accomplish both objective optimality and solution robustness with a low cost. The proposed approach in this study provides a posterior decision support to consider solution robustness while designing policies in multi‐level agricultural water resource systems under deep uncertainties. Key Points: An introduction of robust measure into multi‐level programming balances solution robustness and model optimalityThere is a critical point that decision makers can achieve high objective values and strong robustness with low costs [ABSTRACT FROM AUTHOR]

This paper explores the strategic motivation for a platform to open its superior logistics service to a third-party seller with an endogenous service level. We consider a Stackelberg game between the platform and the seller who sells products to consumers who perceive the platform's product as having a higher value than the seller's product. We characterize the equilibrium results in two schemes regarding opening or not opening the service and present conditions for the platform to open the service and the seller to accept the service. In equilibrium, the platform's logistics service remains at the same level before and after opening. Particularly, we demonstrate that the motivation for the platform to open the service is not simply collecting extra revenue from the service but can be understood from mitigating price competition and securing its demand and price. We find that the platform is always willing to open the logistics system because it provides the platform an additional tool to manipulate the seller's pricing behavior and therefore improves its own profit. With a high commission rate, the platform is even willing to subsidize the seller for using the logistics service. A Pareto improvement can be realized for two firms when consumers are highly sensitive to the service level. Consumers are worse off after service opening in most cases. Our analysis offers insights into the incentives of one retailer providing high-quality service for its rival when retailers differentiate in price and service. [ABSTRACT FROM AUTHOR]

When dealing with large graphs, community detection is a useful data triage tool that can identify subsets of the network that a data analyst should investigate. In an adversarial scenario, the graph may be manipulated to avoid scrutiny of certain nodes by the analyst. Robustness to such behaviour is an important consideration for data analysts in high-stakes scenarios such as cyber defense and counterterrorism. In this paper, we evaluate the use of overlapping community detection methods in the presence of adversarial attacks aimed at lowering the priority of a specific vertex. We formulate the data analyst's choice as a Stackelberg game in which the analyst chooses a community detection method and the attacker chooses an attack strategy in response. Applying various attacks from the literature to nine real network datasets, we find that, when the attacker has a sufficient budget, overlapping community detection methods outperform non-overlapping methods, often overwhelmingly so. This is the case when the attacker can only add edges that connect to the target and when the capability is added to add edges between neighbours of the target. We also analyze the tradeoff between robustness in the presence of an attack and performance when there is no attack. Our extensible analytic framework enables network data analysts to take these considerations into account and incorporate new attacks and community detection methods as they are developed. [ABSTRACT FROM AUTHOR]

To respond to the goal of "carbon peaking and carbon neutrality," reducing carbon emissions in the construction industry is urgent and necessary. A sound profit allocation based on carbon emission reduction is essential to promote low-carbon initiatives implementation by construction supply chain enterprises. In this paper, considering the potential impact of an effective emission reduction scheme (ETS), the Stackelberg game models are established to explore the carbon emission reduction decisions and profit allocation mechanism under decentralized, collaborative and centralized decision-making scenarios between developers and contractors. By conducting a comparative analysis, we obtained insights into the level of enterprises' carbon emission reduction effort, the profit allocation proportion and the impact of ETS. The main findings can be summarized as follows. (1) The proportion of carbon emission reduction profit allocation in the collaborative scenario is higher than that in the decentralized one. (2) For contractors, carbon emission reduction efforts are the highest in the centralized scenario, followed by the collaborative one. For developers, the carbon emission reduction effort is highest in the centralized scenario, but is followed by the decentralized one. When the carbon emission reduction effectiveness of the contractor is greater than a certain level (about 0.52), the carbon emission reduction volume in the collaborative scenario is larger than that in the decentralized scenario. (3) Total profits are most sensitive to carbon trading prices in the centralized scenario, followed by the collaborative scenario. This research provides valuable reference for the government and construction enterprises to promote carbon emission reduction initiatives and low-carbon development in the construction sector. [ABSTRACT FROM AUTHOR]

In light of recent terrorist attacks, we introduce and study a Stackelberg game between a government and a terrorist. In this game, the government positions a number of heavily‐armed rapid response teams on a line segment (e.g., a long boulevard or shopping avenue) and then the terrorist attacks a location with the highest potential impact of an attack. This potential impact, which we call damage, is the product of the time it takes the closest rapid response team to react and the damage caused per time unit, which is modeled via a damage rate function. We prove that there exists a subgame perfect Nash equilibrium that balances the possible damage on all intervals of the line segment that result from positioning the rapid response teams. We discuss the implications for various types of damage rate functions including one mimicking a busy boulevard with various hotspot locations. [ABSTRACT FROM AUTHOR]

This paper designs an incentive Stackelberg strategy for the discrete-time stochastic systems with mean-field terms. Sufficient conditions for the existence of such a design are suggested. Moreover, the incentive strategy is obtained as a feedback form including the deviation of the state and its mathematical expectation. Also, the stability analysis is involved. It is found that the stability can be guaranteed by the follower. In addition, the specific algorithm is proposed and its effectiveness is checked by two examples. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper analyzes the balanced interaction strategy of an integrated energy system (IES) operator and an industrial user in the operation process of the IES under the demand-side management (DSM) based on game theory. Firstly, we establish an electric–thermal IES, which includes a power grid, a heat grid and a natural gas grid. Secondly, a two-stage Stackelberg dynamic game model is proposed to describe the game behavior of IES operators and industrial users in the process of participating in DSM. The interactions between the IES operator (leader) and the user (follower) are formulated into a one-leader–one-follower Stackelberg game, where optimization problems are formed for each player to help select the optimal strategy. A pricing function is adopted for regulating time-of-use (TOU), which acts as a coordinator, inducing users to join the game. Then, for the complex two-stage dynamic game model established, the lower user-side constraint optimization problem is replaced by its KKT condition, so that the two-stage hierarchical optimization problem is transformed into a single-stage mixed-integer nonlinear optimization model, and the branch-and-bound method is introduced to solve it. Finally, the equilibrium strategies and income values of both sides of the game are obtained through a case simulation, and the dynamic equilibrium strategy curves under different capacity configurations are obtained through the sensitivity analysis of key parameters. The equilibrium income of the IES is USD 93.859, while the equilibrium income of industrial users in the park is USD 92.720. The simulation results show that the proposed method and model are effective. [ABSTRACT FROM AUTHOR]

An integrated zero-carbon power plant aggregates uncontrollable green energy, adjustable load, and storage energy resources into an entity in a grid-friendly manner. Integrated zero-carbon power plants have a strong demand response potential that needs further study. However, existing studies ignore the green value of renewable energy in power plants when participating in demand response programs. This paper proposed a mathematical model to optimize the operation of an integrated zero-carbon power plant considering the green value. A demand response mechanism is proposed for the independent system operator and the integrated zero-carbon power plants. The Stackelberg gaming process among these entities and an algorithm based on dichotomy are studied to find the demand response equilibrium. Case studies verify that the mechanism activates the potential of the integrated zero-carbon power plant to realize the load reduction target. [ABSTRACT FROM AUTHOR]

To investigate the value of information sharing in dual-channel retailing, we construct a supply chain consisting of one manufacturer and one retailer engaged in a Stackelberg game. In contrast to prior research, we study the interplay between each player's channel and information sharing strategies by examining boundary cases. Furthermore, we study two alternative timings for the manufacturer to set the wholesale price: after information sharing (mode AD) or before information sharing (mode BD). Our results show that the market conditions and timing to set wholesale prices can affect each player's channel strategy. Under specific circumstances, both the manufacturer and retailer may opt to strategically abandon their channels to maximize profits. Moreover, we find that in mode AD, information sharing consistently benefits the manufacturer and supply chain, while it may negatively impact the retailer. In mode BD, information sharing benefits the manufacturer while may hurt the retailer and supply chain due to boundary cases. Therefore, information sharing agreement can be reached in most cases under modes AD and BD. However, it is noteworthy that with a medium demand variance and low channel substitution rate, information sharing may not be reached under mode BD. Finally, we compare mode AD and BD and discover that in interior equilibrium cases, the manufacturer always favors mode AD while the retailer and supply chain prefer mode BD. However, preferences may alter when considering the boundary cases: in most cases, the supply chain and retailer prefer mode AD. [ABSTRACT FROM AUTHOR]

In the supply chain management of electronic products, asymmetric cost information is a prevalent issue that can lead manufacturer to misreport costs, thereby exacerbating supply chain imbalances. This study focuses on the electronic product supply chain with an extended warranty service, where the manufacturer bears the after-sales responsibility during the extended warranty period. It explores the decision-making (DM) issues within the supply chain under different information environments and power structures. The Stackelberg game theory is employed to solve and analyze these models, and the main findings are as follows: (1) When supply chain information is symmetrical, centralized DM is the best choice. However, in cases where the supply chain adopts decentralized DM, it is more beneficial for the retailer and the supply chain if the retailer assumes the role of DM leader. Additionally, when the retail price sensitivity coefficient is low, the manufacturer will compete with the retailer for DM priority. Conversely, when the retail price sensitivity coefficient is higher, the manufacturer is better off as a follower in DM; (2) When the supply chain information is asymmetric, the manufacturer may engage in misreporting, which benefits the manufacturer but is detrimental to both the supply chain and the retailer. Moreover, if the price sensitivity coefficient is low, the manufacturer should lead the supply chain DM. Otherwise, the retailer should take the lead in supply chain DM. Adopting such a flexible strategy will prove advantageous for all parties involved in the supply chain. (3) The strategy of "reducing the retail price and increasing the extended warranty price" is a favorable strategy for the supply chain. [ABSTRACT FROM AUTHOR]

In order to improve the operating benefits of the distribution network and reduce the energy consumption costs of small-micro-industrial parks, an electricity market clearing considering small-micro-industrial parks is proposed based on the Stackelberg game. First, an optimal operating model of multiple stakeholders is established for integrated energy suppliers, the electricity market, and small-micro-industrial parks. In this model, an optimal electricity supply model for integrated energy suppliers is established with the goal of maximizing the operating benefits. A market clearing optimization model is established for the electricity market with the goal of maximizing the social surplus profit. In addition, an energy utilization optimization model is established for the small-micro-industrial parks with the goal of minimizing the energy procurement costs. Second, with the electricity market as the leader, the integrated energy suppliers and the small-micro-industrial parks as the followers, a leader-follower game strategy is proposed based on the Stackelberg game theory to achieve the maximizing benefits for multiple stakeholders. Finally, the simulation indicates that the proposed strategy can find the best profit point during the game process and achieve a balance between supply and demand. [ABSTRACT FROM AUTHOR]

With the ongoing development of new power systems, the integration of new energy sources is facing increasingly daunting challenges. The collaborative operation of shared energy storage systems with distribution networks and microgrids can effectively leverage the complementary nature of various energy sources and loads, enhancing energy absorption capacity. To address this, a shared energy storage capacity allocation method based on a Stackelberg game is proposed, considering the integration of wind and solar energy into distribution networks and microgrids. In this approach, a third-party shared energy storage investor acts as the leader, while distribution networks and microgrids serve as followers. The shared energy storage operator aims to maximize annual revenue, plan shared energy storage capacity, and set unit capacity leasing fees. Upon receiving pricing, distribution networks and microgrids aim to minimize annual operating costs, determine leased energy storage capacity, and develop operational plans based on typical daily scenarios. Distribution networks and microgrids report leasing capacity, and shared energy storage adjusts leasing prices, accordingly, forming a Stackelberg game. In the case study results, the annual cost of MGs decreased by 29.63%, the annual operating cost of the ADN decreased by 11.25%, the cost of abandoned light decreased by 60.77%, and the cost of abandoned wind decreased by 27.79% to achieve the collaborative optimization of operations. It is proven that this strategy can improve the economic benefits of all parties and has a positive impact on the integration of new energy. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Vehicles (IoV), the demand for wireless spectrum resources has significantly increased. Dynamic spectrum sharing technology is regarded as a key solution to alleviate the shortage of spectrum resources. However, during the spectrum sharing process, security issues and a low utilization of the shared spectrum may arise. This study designs a consortium blockchain for trustworthy dynamic spectrum sharing in IoV environments. An improved asynchronous byzantine fault-tolerant algorithm is also designed to address the instability of signals in this scenario, and the allocation and management of spectrum resources between vehicles and base stations are further optimized using the Stackelberg game, ultimately deployed automatically through smart contracts. Simulation results show that our method not only significantly improves the system's response time but also ensures communication quality and can maintain efficient operation under high network delay and complex scenarios. [ABSTRACT FROM AUTHOR]

Joint decisions regarding the product service system and supply chain coordination configuration have been made in practice. However, embodying the configuration decisions of the product service system in the supply chain decision-making process has not been clearly described. Existing relevant decision models are originated from an 'all-in-one' approach that assumes that both parties' decisions can be integrated into one optimization problem by aggregating two different types of objectives into a single objective function. Such an assumption neglects the complex trade-offs underlying two different decision-making problems and fails to reveal the inherent coupling of the product service system and supply chain configurations. This study constructs a bi-objective Stackelberg game model for the coordinated optimization of a product service system and supply chain configuration. Product service system decisions are modeled as an upper-level optimization problem (called a leader). Supply chain decisions are modeled as a lower-level optimization problem (called a follower). To solve the bi-objective model, an adaptive NSGA-II algorithm based on the dominance strength (ANSGA-II-DS) was developed and implemented. Finally, the effectiveness and practicability of the ANSGA-II-DS algorithm were verified by calculating the standard test function ZDT1-3, using numerical examples, and comparing it with the NSGA-II algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Global environmental concerns and resource scarcity are driving the growth in sales of electric vehicles (EVs). Reusing and recycling retired batteries from EVs has significant economic value and reduces the environmental burden. Rising raw material prices have intensified competition among recyclers; in particular, recyclers without corporate social responsibility (CSR) have been added. These observations lead to a game-theoretical model consisting of three players: a battery manufacturer, a recycler with CSR and a recycler without CSR (non-CSR). The non-CSR recycler enjoys a cost advantage over the CSR recycler, but may not be considered by the consumers with high environmental awareness (CEA). We explore the incentive strategies for CSR recyclers outperform, and how the equilibrium is affected by the recyclers' Stackelberg game. Results show that (1) the deposit- refund is the most profitable strategy for all members and the whole supply chain if raw material price rises high enough; otherwise, a contract strategy should be adopted. (2) Improving CEA and echelon utilization ratio is more conducive to the implementation of revenue-sharing contract. In addition, increasing CEA contributed to CSR recycler collects more retired batteries instead of non-CSR recycler. (3) Stackelberg game between recyclers may hurt supply chain. However, CSR recycler may benefit from the non-CSR recycler-led Stackelberg game. Our work provides the basis of incentive strategies for different participants in the closed-loop supply chain of retired batteries, in particular, to encourage retired batteries flow to CSR recyclers. [ABSTRACT FROM AUTHOR]

The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in the Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility. [ABSTRACT FROM AUTHOR]

Extended warranties are widely adopted and accepted in the marketplace by manufacturers and retailers as it helps to enhance the customers' post-sale satisfaction. In closed-loop supply chains, the extended warranty not only generates profit for the manufacturer, but also provides warranty returns of the new products for remanufacturing. In this paper, a two-period model is developed and optimal pricing strategies for the extended warranties are derived. We compare the optimal pricing and retailing strategies of the extended warranties for remanufactured and new products offered by the manufacturer with and without the retailer's own extended warranty while considering the competition between the manufacturer and the retailer for the extended warranty of new products. We find that the introduction of the retailer's extended warranty does not always hurt the manufacturer's profit. Numerical analyses also show that there exists an optimal extended warranty length for the manufacturer that maximises its profit. Moreover, we show that the retailer cannot extract more profit by increasing the length of its own extended warranty. [ABSTRACT FROM AUTHOR]

Alongside the rapid advancement of artificial intelligence technologies, the complexity of society continues to escalate, and the design of multi-agent system rules becomes increasingly crucial, optimize participant engagement and addressing the issue of fairness and equilibrium win rates. The card game Legends of the Three Kingdoms (LTK), as a complex multi-agent system, represents an important abstraction of real-world scenarios, requiring the implementation of proper incentive mechanisms. Based on the mechanism and Stackelberg, this paper optimizes player strategies and system fairness. Firstly, we constructed a multi-agent Stackelberg game model for team battles. Then, we analyzed the impact of decision-making factors on players. Subsequently, we defined three kinds of fairness and improved the incentive mechanism. The results indicate that our mechanism optimizes participants' game behaviors, enhancing fairness among team players. Win rates were improved from 56.2% VS 43.8% to 51.4% VS 48.6%. With three different fairness measures, the win percentage fairness increased by 72 %, and the first elimination fairness increased by about 79 %. Our research provides a reference for understanding and analyzing complex computational models and facilitates the resolution of various resource allocation and system design issues.