Showing papers on "Stackelberg competition published in 2022"

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Abstract: Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy preserving and Quality of Service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this article, we propose a novel wirelessly powered edge intelligence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. First, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly powered multiagent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.

Technology license sharing strategy for remanufacturing industries under a closed-loop supply chain management bonding

Abstract: Remanufacturing is getting attention nowadays due to increasing waste and corresponding emissions. One of the important factors of remanufacturing is the quality of the remanufactured products. The collection and distribution of used products require proper management. Based on this situation, this study discusses a hybrid closed-loop supply chain management in cooperation with a hybrid production system. The vendor comes up with the policy of sharing remanufacturing responsibility by sharing the technology license with other supply chain players. The carbon cap restricts emissions from the entire hybrid production system of the vendor. Other factors of this proposed study are service by the retailer and quality, gift policy, and customer awareness by the vendor. This study examines the scenario under random market demand. Classical optimization provides the solution under the Stackelberg game policy where the vendor acts as leader and the retailer & third party act as followers. This paper considers two scenarios: Scenario A for a continuous distribution and Scenario B for no specific distribution. A comparison is drawn between various motivating factors-based policies to control supply chain management.

E‐Commerce supply chains with considerations of cyber‐security: Should governments play a role?

Abstract: E-commerce supply chains and their members face risks from cyber-attacks. Consumers who purchase goods online also risk having their private information stolen. Thus, businesses are investing to improve cyber-security at a nontrivial cost. In this paper, we conduct a Stackelberg game-theoretical analysis. In the basic model, we first derive the equilibrium pricing and cyber-security level decisions in the e-commerce supply chain. Based on real-world practices, we then explore whether governments should impose cyber-security penalty schemes. Our findings show that when the government is characterized by having sufficiently high emphasis on consumer surplus, implementing the penalty scheme is beneficial to social welfare. Then, we extend the analysis to examine how adopting systems security enhancing technologies (such as blockchain) will affect the government's choice of imposing penalty. We uncover that when it is beneficial to have government's penalty scheme, the technology benefit-to-cost ratio is a critical factor that governs whether the optimal penalty will be lower or higher with the adoption of systems security enhancing technologies. To generate more insights, we conduct further analyses for various extended modeling cases (e.g., with alliance, competition, and the defense-level dependent penalty scheme) and find that our main results remain robust. One important insight we have uncovered in this study is that imposing government penalty schemes on cyber-security issues may do more harm than good; while once it is beneficial to implement, the government should charge the heaviest possible fine. This finding may explain why in the real world, governments basically always adopt a polarized strategy, that is, either do not impose penalty or impose a super heavy penalty, on cyber-security issues.

Optimal decisions in a dual-channel competitive green supply chain management under promotional effort

Abstract: The awareness of the environment has been extensively studied in the past decade. In this study, the production of eco-friendly and comparatively less harmful green innovative products is considered under an uncertain environment to reduce their detrimental effect on the environment under green supply chain management. Owing to the complexity of green innovation, the various pricing decisions of the players, green innovation level, and promotional effort under the centralized, manufacturer Stackelberg, and vertical Nash policies are studied. The relations among the parameters are analytically investigated such that the profits are optimized for various cases. The optimal level of green innovation, promotional effort, prices, and profits are achieved by varying the market potential and price parameters. It is also observed that the cost coefficients of green innovation and promotional effort have the highest effect on the optimal level. Green innovation is very effective in improving the players’ profit margin, and the manufacturer must decide the extent of green innovation to optimize the profits. The optimal level of the promotional effort for the various cases is determined such that the players can gain the most. It is found from the study that a dual-channel supply chain is more efficient than a single-channel supply chain for green products.

Intelligent Jamming Defense Using DNN Stackelberg Game in Sensor Edge Cloud

Abstract: To ensure an accurate power allocation against increasing intelligent jamming attacks on the offloading link of computation tasks, we investigate interactions between a cluster head node and an intelligent jammer using a Stackelberg game framework, under the constraint of the total power to use and the limited knowledge of its own channel gain for each player. In this game, the intelligent jammer gathers channel gain information and processes it using a deep neural network (DNN) to infer the accurate jamming power as an attack strategy. The cluster head node also exploits DNN to infer an accurate transmission power as a defense strategy according to the varying channel gain. We model the optimization of the attack and defense strategies using single channel jamming DNN (SJnet), multiple channel jamming DNN (MJnet), single channel sensor DNN (SSnet), and multiple channel sensor DNN (MSnet) for the single (multiple) channel jamming attacks. In addition, we extend the design to the scenario where the intelligent jammer can launch a hybrid mode jamming attack, and propose a DNN Stackelberg game-based defense scheme. Numerical simulation results demonstrate that our proposed mechanism is superior to other power allocation mechanisms under different scenarios in the sensor edge cloud.

Online peer-to-peer lending platform and supply chain finance decisions and strategies

Abstract: Abstract Online peer-to-peer (P2P) lending platform is an emerging FinTech business model that establishes a link between investors and recipients of capital in supply chains (SCs). Businesses face capital constraints impacting directly on their final product price and demand. This article studies optimal decisions and operational strategies in a logistics network considering two capital-constrained manufacturers who produce products of different qualities and sell them to a retailer having deterministic demand over a specific period. The high quality product manufacturer borrows capital through an online P2P lending platform with a service fee, while the low quality product manufacturer pre-sells products for competing with the high quality product manufacturer. In this study, we find optimal prices of the SC participants, service rate of the online P2P platform and percentage of the pre-ordering quantity of the retailer. We analyse optimal Stackelberg and Nash equilibrium of the SC participants. We find that an increase in the amount of opportunity cost will cause a decrease in the pre-ordering quantity of the retailer affecting the SC profit in numerous ways. The online P2P lending platform should consider the amount of the retailer’s target profit in determining the platform’s service rate. We posit some practical insights based on our numerical study and observations for SC managers enabling them to take appropriate measures about their optimal strategies according to the networks’ existing economic conditions.

Channel Integration Choices and Pricing Strategies for Competing Dual-Channel Retailers

Abstract: The “buy online and pick up in store” (BOPS) mode is gaining tremendous popularity among retailers since it is convenient for consumers and brings additional store sales to retailers. However, operating the BOPS channel requires additional investment, which is a challenge for retailers. This article considers the pricing strategies of competing dual-channel retailers, focuses on whether and when they should adopt the BOPS strategy, and explores the impacts of market factors on the equilibrium outcomes. Since retailers’ decisions are usually made sequentially in reality, we use the Stackelberg game model to analyze retailers’ optimal strategies. First, we show that the follower's price is not always lower than the leader's price. Specifically, when the unit additional profit from cross-selling of the follower is low enough, the follower will set a higher price than the leader. Second, we find that retailers prefer the BOPS strategy when the fixed costs for offering BOPS channels are low enough, or when the difference between the additional profits from cross-selling of two retailers is sufficiently large. Third, we present an interesting insight: an increase in product return probability or retailer cost of handling a returned product can be beneficial to retailers.

Food Delivery Service and Restaurant: Friend or Foe?

Abstract: With food delivery services, customers can hire delivery workers to pick up food on their behalf. To investigate the long-term impact of food delivery services on the restaurant industry, we model a restaurant serving food to customers as a stylized single-server queue with two streams of customers. One stream consists of tech-savvy customers who have access to a food delivery service platform. The other stream consists of traditional customers who are not able to use a food delivery service and only walk in by themselves. We study a Stackelberg game, in which the restaurant first sets the food price; the food delivery platform then sets the delivery fee; and, last, rational customers decide whether to walk in, balk, or use a food delivery service if they have access to one. If the restaurant has a sufficiently large established base of traditional customers, we show that the food delivery platform does not necessarily increase demand but may just change the composition of customers, as the segment of tech-savvy customers grows. Hence, paying the platform for bringing in customers may hurt the restaurant’s profitability. We demonstrate that either a one-way revenue-sharing contract with a price ceiling or a two-way revenue-sharing contract can coordinate the system and create a win-win situation. Furthermore, under conditions of no coordination between the restaurant and the platform, we show, somewhat surprisingly, that more customers having access to a food delivery service may hurt the platform itself and the society, when the food delivery service is sufficiently convenient, and the delivery-worker pool is large enough. This is because the restaurant can become a delivery-only kitchen and raise its food price by focusing on food-delivery customers only, leaving little surplus for the platform. This implies that limiting the number of delivery workers can provide a simple yet effective means for the platform to improve its own profitability while benefiting social welfare. This paper was accepted by Charles Corbett, operations management.

Improving Physical Layer Security in IRS-Aided WPCN Multicast Systems via Stackelberg Game

Abstract: This paper investigates an intelligent reflecting surface (IRS) aided secure wireless powered communication network (WPCN), where a transmitter first harvests energy from a power station (PS), and then uses the collected energy to transmit information to multiple internet of things (IoT) devices in the form of multicast in the presence of multiple eavesdroppers. An IRS is deployed to enhance the efficiency of wireless energy transfer (WET) and secure wireless information transfer (WIT). Considering that the PS and transmitter belong to different service providers, we model this energy interaction through a Stackelberg game and propose an IRS-aided energy trading and secure communication (IRS-ETSC) scheme, in which the transmitter needs to pay an energy price as an incentive for the PS energy service. Specifically, the transmitter as the leader can control the energy price, WET time, two-stage phase shifts, and beamforming vector, while the PS as the follower can adjust the transmit power. To solve the non-convex leader game problem, we propose a two-step approach to decompose the original problem into two subproblems. The first subproblem can be solved independently by an efficient alternating optimization (AO) based algorithm, in which the closed-form optimal beamforming vector and energy phase shifts are alternately optimized. Then, the second subproblem is relaxed by semidefinite relaxation (SDR) and solved by an iterative algorithm based on block coordinate descent (BCD), where the optimal energy price, optimal WET time, and suboptimal information phase shifts can be obtained by golden section method and successive convex approximation (SCA) respectively. Both subproblems can converge to a stationary point. Numerical results show that compared with the traditional non-IRS scheme, the proposed IRS-ETSC scheme achieves utility improvement for both the PS and transmitter.

SDN-Based Resource Allocation in Edge and Cloud Computing Systems: An Evolutionary Stackelberg Differential Game Approach

Abstract: Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge and cloud computing (ECC) system has been expected as a promising solution to handle the increasing computational applications with low-latency and on-demand services of computation offloading, which requires new computing resource sharing and access control technology paradigms. This work establishes a software-defined networking (SDN) based architecture for edge/cloud computing services in 5G heterogeneous networks (HetNets), which can support efficient and on-demand computing resource management to optimize resource utilization and satisfy the time-varying computational tasks uploaded by user devices. In addition, resulting from the information incompleteness, we design an evolutionary game based service selection for users, which can model the replicator dynamics of service subscription. Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs). Then we derive the optimal pricing and allocation strategies of cloud computing resource based on the replicator dynamics of users’ service selection. These strategies can promise the maximum integral utilities to all computing service providers (CPs), meanwhile the user distribution can reach the evolutionary stable state at this Stackelberg equilibrium. Furthermore, simulation results validate the performance of the designed resource sharing mechanism, and reveal the convergence and equilibrium states of user selection, and computing resource pricing and allocation.

Reselling or agency selling? The strategic role of live streaming commerce in distribution contract selection

Abstract: The booming live streaming business has increased the number of consumer options for purchase channels, which has had a significant impact on firm sales models and management practices. This paper considers a multiechelon supply chain in which an upstream supplier can sell products through an online platform and a live streaming sales channel, and the online platform can choose to sign a reselling agreement or an agency selling agreement with the supplier. By constructing a Stackelberg game, we theoretically derive the equilibrium strategies of the supply chain members under different selling agreements in the presence of a live streaming sales channel. Specifically, we find that under the reselling agreement, the optimal retail price for the platform decreases with an increase in the commission rate, while the retail price for the live streaming channel always increases with an increase in the commission rate, and the relationship between the wholesale price and the commission rate depends on the ratio of the coefficient on the internet celebrity’s effort for the live streaming channel to that for the online platform. Under the agency selling agreement, there is a threshold in the agency fee. The impact of the commission rate on pricing strategy on one side of the threshold is opposite that on the other side. Furthermore, we numerically explore the impacts of the system parameters on the selection of the sales model, profit, and the decision-making of the supply chain members and identify the conditions under which agency selling and reselling are chosen when there is a live streaming sales channel. Interestingly, we find that the internet celebrity should not charge a commission rate that is too large; otherwise, it harms others without benefiting the celebrity. Moreover, when the supplier chooses to hire an internet celebrity to sell goods via live streaming, the supplier should choose a celebrity with either an extremely large following or with little influence but should not choose a celebrity with only moderate influence.

A cooperative Stackelberg game based energy management considering price discrimination and risk assessment

Abstract: This paper presents a bi-level energy management framework that can help the retail market to coordinate peer-to-peer (P2P) energy trading among multiple prosumers. To this end, the interaction process is formulated as a cooperative Stackelberg game model, where a retailer acts as the leader that determines price discrimination for various prosumers, with the goal of maximizing the social welfare. On the other hand, prosumers act as followers and react to the leader’s decision in a cooperative manner. Based on a general Nash bargaining scheme, prosumers participate in P2P energy trading to share their idle energy resource with neighbors while allocating the cooperative revenue based on their contribution. Considering the uncertainty of renewable energy, a stochastic programming approach with Conditional Value at Risk (CVaR) is employed to characterize the expected losses by the retailer. The hierarchic energy interaction is formulated as a nonlinear bi-level programming model, a two-phase approach is proposed to address the formulation with a power function in the lower-level. By using Karush-Kuhn-Tucker conditions, a bi-level model is transformed into an equivalent single-level mixed-integer linear programming problem in first phase. Furthermore, the second phase completes the market clearing and determines the payments of the prosumers according to the scheduling results. Numerical cases are performed to demonstrate the effectiveness of the proposed model.

Considering Consumers’ Green Preferences and Government Subsidies in the Decision Making of the Construction and Demolition Waste Recycling Supply Chain: A Stackelberg Game Approach

Abstract: Resource utilization of construction and demolition waste (CDW) is regarded to be an important means of achieving the sustainable development of the economy and the environment. However, previous research has not fully considered the green degree of products in the demand function of CDW remanufactured products. This study aimed to clarify how consumers’ green preferences and government subsidies affect decision making in the supply chain. First, a CDW resource utilization supply chain model composed of building materials manufacturers and retailers was constructed using consumer behavior theory. Second, the optimal decision making of members under conditions of decentralized and centralized decision making was analyzed using the Stackelberg game solution. Finally, the validity of the model and conclusions were verified by numerical simulation. The main conclusions are as follows. Government subsidies have a different impact on the pricing of new building materials products and CDW remanufactured products. Under decentralized decision making, the optimal profit of the CDW resource utilization supply chain with government subsidies is higher. However, under centralized decision making, the optimal profit is also related to consumers’ green preferences. According to consumers’ green preferences, choosing different decision-making models can not only improve the total profit of the CDW resource utilization supply chain, but also improve the reuse rate of CDW.

Deep Reinforcement Learning-Based Charging Pricing for Autonomous Mobility-on-Demand System

Abstract: The autonomous mobility-on-demand (AMoD) system plays an important role in the urban transportation system. The charging behavior of AMoD fleet becomes a critical link between charging system and transportation system. In this paper, we investigate a strategic charging pricing scheme for charging station operators (CSOs) based on a non-cooperative Stackelberg game framework. The Stackelberg equilibrium investigates the pricing competition among multiple CSOs, and explores the nexus between the CSOs and AMoD operator. In the proposed framework, the responsive behavior of AMoD operator (order-serving, repositioning, and charging) is formulated as a multi-commodity network flow model to solve an energy-aware traffic flow problem. Meanwhile, a soft actor-critic based multi-agent deep reinforcement learning algorithm is developed to solve the proposed equilibrium framework while considering privacy-conservation constraints among CSOs. A numerical case study with city-scale real-world data is used to validate the effectiveness of the proposed framework.

A Hierarchical Price-Based Demand Response Framework in Distribution Network

Abstract: In the evolving local retail electricity market hierarchy, demand response providers (DRPs) are becoming viable interlink for the interaction between distribution system operator (DSO) and customers in demand response (DR) assessment in distribution network. This hierarchical structure is rendered in price based DR and is suggested as the proposed study. It is formulated as a tri-level two-stage DR in a theoretic game framework using two-loop Stackelberg game. In first stage, DSO (leader) and DRPs (followers) interact to determine optimal dynamic retail price, while optimizing their interests independently and in second stage, DRP as leader sets its optimal dynamic price to the customers (followers) for inducing DR. The existence and uniqueness of Stackelberg equilibrium is confirmed using backward induction and validated the optimality theoretically. It is formulated as nonlinear program with the consideration of ac network operating constraints. A nested reformulation and decomposition algorithm as a solution method is designed and is implemented to solve the problem. It is illustrated on IEEE 33-bus and a real 108-bus Indian distribution system. The detailed numerical results demonstrate the effectiveness of the proposed model and, scalability and tractability of the algorithm to solve the large scale problem reasonably.

A Deep Reinforcement Learning-Based Resource Management Game in Vehicular Edge Computing

Abstract: Vehicular Edge Computing (VEC) is a promising paradigm that leverages the vehicles to offload computation tasks to the nearby VEC server with the aim of supporting the low latency vehicular application scenarios. Incentivizing VEC servers to participate in computation offloading activities and make full use of computation resources is of great importance to the success of intelligent transportation services. In this paper, we formulate the competitive interactions between the VEC servers and vehicles as a two-stage Stackelberg game with the VEC servers as the leader players and the vehicles as the followers. After obtaining the full information of vehicles, the VEC server calculates the unit price of computation resource. Given the unit prices announced by VEC server, the vehicles determine the amount of computation resource to purchase from VEC server. In the scenario that vehicles do not want to share their computation demands, a deep reinforcement learning based resource management scheme is proposed to maximize the profits of vehicles and VEC server. The extensive experimental results have demonstrated the effectiveness of our proposed resource management scheme based on Stackelberg game and deep reinforcement learning.

IIoT Data Sharing Based on Blockchain: A Multileader Multifollower Stackelberg Game Approach

Abstract: The evolution of the Industrial Internet of Things (IIoTs) greatly increases the volume of data generated by the connected IIoT devices. IIoT data are playing an increasingly important role in various industrial sectors. IIoT data sharing helps enterprises make better production decisions and respond to market changes timely. However, the distrust among IIoT entities and IIoT entities’ distrust of data-sharing platforms may hinder the realization of data sharing. In this article, a decentralized IIoT data-sharing scheme based on blockchain and edge computing is proposed. A Proof of Storage and Transmission (PoST) consensus mechanism is proposed to meet data storage and transmission requirements of data owners in IIoT data-sharing networks. Based on the manufacture ties of data owners, shared data request probabilities are derived. The IIoT data sharing interactions between data owners and edge devices are modeled as a multiple-leader and multiple-follower Stackelberg game. The alternating direction method of multipliers (ADMMs) algorithm is used to obtain the optimal IIoT data sharing solutions in a distributed manner. Simulation results show that compared with the cooperative scheme, the total profit of edge devices is maximally increased by 59%, and the total utility of data owners is maximally increased by 52%.