Optimal Cooperative Strategy Research Articles

Strategic cooperation with capital-constrained supplier and downstream competition in complex product systems

The paper characterizes cooperation and coordination strategies in complex product systems (CoPS). Capital-constrained supplier and downstream competition are considered in a “manufacturer-suppliers (MS)” mode with a weak main manufacturer and a powerful supplier. The capital-constrained supplier provides a key element to the main manufacturer and encroaches in the downstream market through simultaneous production of substitutable final products. The weak main manufacturer designs a revenue-sharing contract based on a relationship-specific investment to cooperate with the powerful supplier and finally improve its indigenous technological capability. By building Stackelberg and bargaining games, the co-opetition equilibriums and coordination strategies are derived and compared under different conditions. The results show that the cost structure, the abilities of the two players, and the ability of the main manufacturer’s technology learning have significant effects on the optimal strategies; moreover, the impacts of the threshold value of the supplier’s capital and the relationship-specific investment on the optimal cooperation strategies are very different. Overall, the coordination strategy can alleviate downstream competition and enhance both players’ profitability effectively.

Competition and cooperation in a supply chain with an offline showroom under asymmetric information

Offline showrooms develop rapidly to resolve consumers’ uncertainty about whether products fit their needs when they purchase online. This paper considers a supply chain where an offline showroom provides experience service for an existing online retailer and intends to introduce a new competing online retailer to satisfy consumers’ heterogeneous demand. The offline showroom has better knowledge of demand information due to closer to offline consumers. We examine the impact of competition and the offline showroom's optimal channel cooperation strategy under asymmetric information and analyse the equilibrium results under the optimal strategy to shed light on channel cooperation and information strategy for supply chain members. We find that under asymmetric information competition prompts the offline showroom to conceal information which generates signal cost (negative information effect) and to increase experience service level which creates value (positive service effect). The optimal channel cooperation strategy depends on the trade-off between these effects. We also find that competition increases the existing online retailer's profit in some conditions. Besides, in some conditions information asymmetry harms all supply chain members, which suggests the offline showroom to share information with the online retailers; in some conditions, information asymmetry harms the offline showroom but benefits the online retailers.

Strategic Cooperation with Differential Suppliers’ Ability under Downstream Competition in Complex Products Systems

This paper investigates the strategic cooperation of two competitive suppliers with different abilities and a weak main manufacturer in Complex Products Systems (CoPS), where a main manufacturer signs a revenue-sharing contract based on a relationship-specific investment with the stronger supplier. The stronger supplier provides one key element to the main manufacturer and encroaches on the downstream market by producing substitutable final products simultaneously. We consider multi-period decisions, by building different models based on centralized, decentralized, and cooperative decisions. The equilibrium strategies are characterized under downstream competition, and optimal cooperation strategies are derived by building multi-period game models. The results show that strong cooperation can enhance the economic performance of each individual as well as the whole supply chain. The weak main manufacturer would face the risk of strong suppliers’ supply interruptions when the competitive degree of suppliers and downstream competition are fierce enough under decentralized decisions. Additionally, the gap in the abilities of the two competitive suppliers reduces the main manufacturer’s profitability. However, the revenue-sharing contract based on a relationship-specific investment can motivate the strong supplier to establish cooperation relationship and improve both stakeholders’ profitability. Moreover, strategic cooperation is efficient to prevent the strong supplier encroaching on downstream and has a positive impact on boosting the weak main manufacturer’s market share. Meanwhile, nurturing a domestic supplier is an effective measure for improving competitiveness and indigenous technological capability of the main manufacturer in CoPS. Finally, some useful management sights on cooperation strategy and optimal decisions are derived.

Online cooperation mechanism: game analysis between a restaurant and a third-party website

Increasing numbers of restaurants pay a commission to seek a business relationship with a third-party website as an alternative distribution channel for traditional offline selling. Whether to build the cooperation and how to set the online marketing strategy are the major concern of these restaurants. In this paper, a Stackelberg game is established to study online pricing strategies of a restaurant, as well as the optimal service effort level of a third-party website. Two optimal cooperative strategies in different scenarios are obtained, which are comprehensively influenced by the restaurant’s retail price and the website’s user base.

An optimization design of vehicle axle system based on multiobjective cooperative optimization algorithm

ABSTRACTTo address multiobjective, multi constraint and time-consuming structural optimization problems in a vehicle axle system, a multiobjective cooperative optimization model of a vehicle axle structure is established. In light of the difficulty in the nondominated sorting of the NSGA-II algorithm caused by inconsistent effects of the uniformity objective function and physical objective function, this paper combines a multiobjective genetic algorithm with cooperative optimization and presents a strategy for handling the optimization of a vehicle axle structure. The uniformity objective function of the sub discipline is transformed to its self-constraint. Taking the multiobjective optimization of a vehicle axle system as an example, a multiobjective cooperative optimization design for the system is carried out in ISIGHT. The results show that the multiobjective cooperative optimization strategy can simplify the complexity of optimization problems and that the multiobjective cooperative optimization method based on an approximate model is favorable for accuracy and efficiency, thereby providing a theoretical basis for the optimization of similar complex structures in practical engineering.

Cooperative Guidance Law for Target Pair to Lure Two Pursuers into Collision

A novel cooperative defensive guidance law is presented for a two-on-two engagement. Instead of classical strategies in which evasive maneuvers are performed or additional agents (e.g., defender missiles) are deployed, the target pair lures the pursuing missiles into collision. The optimal cooperative strategy is solved using state-dependent Riccati equation method. Linearized kinematics and arbitrary-order linear adversaries’ dynamics are assumed in the guidance law derivation. Imperfect information is assumed on the relative states and the guidance laws employed by the missile pair. Guidance strategies that the pursuing missiles may employ are assumed to belong to a finite set of linear guidance laws. In addition to the proposed cooperative defensive strategy, a decentralized estimation scheme based on the multiple-model adaptive estimation approach is also presented. Guidance law and estimation performance are demonstrated using nonlinear simulations. Simulation results show the viability of the proposed guidance law and highlight the sensitivity of the guidance law to range measurement accuracy.

Topology‐based distributed optimization for multi‐UAV cooperative wildfire monitoring

SummaryCooperative small‐sized unmanned aerial vehicles (UAVs) can contribute to wildfire monitoring missions. By distributing UAVs into groups to cover different segments of the fire‐front contour, the mission efficiency can be improved. In this paper, according to the requirements of a wildfire mission, we formulate the multi‐UAV cooperation problem in a distributed network. A consensus‐based method is proposed to allow the group‐level cooperation among UAVs. The optimal UAV cooperation strategy is obtained through mathematical analysis. The effectiveness of the methodology is verified in an independent fire simulation environment.

Selection and optimization of cooperative advertising strategies in supply chain based on stackelberg game method

With the intensification of market competition, the cost of advertising enterprises is increasing, so eventually, advertising may unable to increase the profits of enterprises and bring huge consumptions to enterprises. In this case, cooperative advertising came into being. With the Stackelberg game method, the selection and optimization of cooperative advertising strategies in supply chain were studied. Through the comparative analysis, the related concepts of supply chain competition, the level of cooperative advertising and Stackelberg game were determined; from the structures as two chains that are the non-integrated decision-making and the integrated decision-making, and two chains that are the integrated decision-making and the non-integrated decision-making, the existing problems were modeled and analyzed with the method of Stackelberg game theory; through numerical analysis, the optimal cooperative advertising strategy under the level of horizontal cooperation advertisement for each enterprise in the supply chain was obtained. Theoretical and empirical analysis can provide theoretical supports and model references for the research on strategy selection and optimization of cooperative advertising in other supply chains.

An optimal one-way cooperative strategy for two defenders against an attacking missile

This paper investigates a cooperative strategy for protecting an aerial target. The problem is solved as a game among four players (a target, two defenders, and a missile). In this scenario, the target launches two defenders (defender-1 and defender-2) simultaneously, to establish a one-way cooperation system (OCS) against an attacking missile. A new optimal evasion strategy for the target is also derived. During the engagement, the target takes into account the reaction of the attacking missile, and guides defender-1 to the interception point by receiving information from defender-1. Depending on the control effort of the target, defender-2 can choose appropriate launch conditions and use very limited maneuvering capability to intercept the missile. For adversaries with first-order dynamics, simulation results show that the OCS allows two defenders to intercept the missile. During the engagement, even if one defender or communication channel is broken, the OCS still allows an interception to be made, thus increasing the target’s survivability.

The Gaussian Diamond-Wiretap Channel With Rate-Limited Relay Cooperation

In this letter, we establish the secure degrees-of-freedom of the Gaussian diamond-wiretap channel with rate-limited relay cooperation, where the eavesdropper not only observes the relay transmission through another multiple access channel but also wiretaps some of communication links among relays. The legitimate parties do not know the location of wiretapped relay links nor the eavesdropper’s channel state information. As an optimal relay cooperation strategy, a noise-forwarding scheme that does not incorporate secure network coding is adopted. Furthermore, we briefly outline how the scheme can be extended to incorporate the case when the source-relay links are also wiretapped.

Threshold-Based Cooperative Communication of Energy Harvesting Active Networked Tags

Using cooperative transmission allows energy-harvesting active networked tags (EnHANTs) to cooperate regarding information transmission and reception, achieving higher throughput compared with non-cooperative counterparts. In this paper, a battery-threshold-based optimal cooperative transmission strategy is proposed for EnHANTs, allowing EnHANT-equipped objects to communicate with the destination (i.e., the reader) either directly or by cooperating with neighbouring objects. A strategy adapted to request states and available energy resources is considered, and an optimal transmission policy for EnHANTs is developed to maximise the long-term average throughput by formulating a Markov decision process (MDP) model. The simulated results indicate that the proposed strategy yields superior performance compared with single-link transmission.

Cooperative Search Advertising

Channel coordination in search advertising is an important but complicated managerial decision for both manufacturers and retailers. A manufacturer can sponsor retailers to advertise his products while at the same time compete with them in position auctions. We model a manufacturer and retailers' cooperation and intra-brand competition in advertising, as well as inter-brand competition with other advertisers. We consider a simple coordination mechanism where a manufacturer shares a fixed percentage of a retailer's advertising cost. Our model prescribes the optimal cooperative advertising strategies from the manufacturer's perspective. We find that it can be optimal for a manufacturer not to cooperate with all his retailers even if they are ex ante the same. This reflects the manufacturer's trade-off between a higher demand versus a higher bidding cost resulting from more competition. We also find that an advertiser's position rank in equilibrium is entirely determined by his channel profit per click, no matter he is a manufacturer or retailer. Consequently, when determining whether to advertise directly to consumers or via retailers, a manufacturer should compare his profit per click from direct sales with the retailer's total channel profit per click; similarly, when choosing which retailer to sponsor, a manufacturer should compare their total channel profits per click. We also investigate how a manufacturer uses both wholesaling and advertising contracts to coordinate channels with endogenous retail prices.

Dynamic stochastic cooperative reinsurance strategy in a\\ continuous time model

The problem of the expected utility maximization in a reinsurance market for a single period model is well understood under a general setting. This paper studies the problem in a continuous time model. For reciprocal type of reinsurance strategies and exponential utility functions, with the concept of the Pareto optimality in game theory, we obtain the characterization of the core of cooperative reinsurance games and show that the core, which contains all of the Pareto optimal cooperative reinsurance strategies, is non-empty. Examples are provided to illustrate that the core is non-empty under the given situations and the Pareto optimal cooperative reinsurance strategy is shown to be a proportional reinsurance strategy when there are only two insurance companies considered.

Bidding and Cooperation Strategies for Electricity Buyers in Power Markets

Deregulation of electric power industries in recent years has opened many opportunities for electricity buyers. However, the strong influence of network physical constraints may result in economic decisions that adversely affect the interests of the consumers. Compared with the monopolistic economy of yesteryears, electricity buyers may actually be able to influence the market by cooperating with other buyers in the network. This paper presents a coevolutionary approach to investigate individual and cooperative strategies of buyers in a power market, taking fully into account the physical network constraints. First, the study focuses on deterministic cases, where buyers choose their bidding strategies to maximize the profits in different scenarios of playing individually or cooperatively. It is found that, by evolutionary learning, buyers can benefit from cooperation. After that, the uncertain nature of the market is modeled, where buyers find optimal cooperation strategies to hedge against the risk of low payoffs. The payoff distribution problem in cooperative game theory was linked with the optimal coalition generation problem by proving a theorem. The statistically consistent simulation results show that our approach is able to discover interesting cooperation strategies and can be easily extended to practical networks with a large number of buyers.

To Relay or Not to Relay: Learning Device-to-Device Relaying Strategies in Cellular Networks

We consider a cellular network where mobile transceiver devices that are owned by self-interested users are incentivized to cooperate with each other using tokens, which they exchange electronically to “buy” and “sell” downlink relay services, thereby increasing the network's capacity compared to a network that only supports base station-to-device (B2D) communications. We investigate how an individual device in the network can learn its optimal cooperation policy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> , which it uses to decide whether or not to provide downlink relay services for other devices in exchange for tokens. We propose a supervised learning algorithm that devices can deploy to learn their optimal cooperation strategies online given their experienced network environment. We then systematically evaluate the learning algorithm in various deployment scenarios. Our simulation results suggest that devices have the greatest incentive to cooperate when the network contains (i) many devices with high energy budgets for relaying, (ii) many highly mobile users (e.g., users in motor vehicles), and (iii) neither too few nor too many tokens. Additionally, within the token system, self-interested devices can effectively learn to cooperate online, and achieve up to 20 percent throughput gains on average compared to B2D communications alone, all while selfishly maximizing their own utilities.

Decode-to-cooperate: a sequential alamouti-coded cooperation strategy in dual-hop wireless relay networks

An optimal cooperation strategy, decode-to-cooperate, is proposed and investigated for performance improvements in dual-hop wireless relay networks. Based on decode-and-forward (DF) strategy with multiple relay selection, we design a novel scheme such that the source node keeps transmitting sequentially and the selected relays cooperate by transmitting the decoded signal using distributed Alamouti coding. We exploit the multipath propagation effect of the wireless channel to achieve lower probability of error and introduce optimum power allocation and relay positioning. We analyze the scenario when the source to destination direct link is not available and derive a closed form expression for symbol error rate (SER), its upper bound and an asymptotically tight approximation to exploit the performance gain by selecting the optimum relays in a multiple-relay cooperation scheme. Moreover, asymptotic optimum power allocation (based on the SER approximation) and optimal relay positioning are also considered to further improve the SER. The proposed relay selection scheme outperforms cooperative (DF) and non-cooperative schemes by more than 2 dB.

An Optimal Cache Partition Based Algorithm For Content Distribution And Replication

Generally, users can cast up two sorts of requests, such as elastic requests that contain no delay constraints, and inelastic requests that take an inflexible delay constraint. The distribution of WSN's in multiple areas like, target tracking in battle fields, environmental control needs an optimization for communication among the detectors to serve information in shorter latency and with minimal energy consumption. Cooperative data caching emerged as a productive technique to accomplish these ends simultaneously. The execution of protocols for that network depends mainly on the selection of the sensors which will call for special roles in accordance to the procedure of caching and take forwarding decisions. A perception of Wireless content distribution was shown in which there are numerous cellular base stations, each of which encompass a cache for storing of content. Content is typically partitioned into two disjoint sets of inelastic as well as elastic content. Cooperative caching is shown to be capable to reduce content provisioning cost which heavily depends on service and pricing dependencies among several stakeholders including content providers, web service providers, and end consumers. Hither, a practical network, service, and economic pricing models which are then utilized for making an optimal cooperative caching strategy based on social community abstraction in wireless nets are broken. Inelastic requests are provided by means of broadcast transmissions and here we develop algorithms in support of content spread by means of elastic and inelastic requests. The developed framework includes optimal caching algorithms, analytical models, for evaluating the operation of the suggested scheme. The primary donations are: i) formulation of economic cost-reward flow models among the WNET stakeholders, ii) developing optimal distributed cooperative caching algorithms, iii) characterizing the impacts of network, user and object dynamics, and finally iv) investigating the impacts of user noncooperation,

Synergy effect of cooperative investment

Cooperative investment consists of two problems: finding an optimal cooperative investment strategy and fairly dividing investment outcome among participating agents. In general, the two problems cannot be solved separately. It is known that when agents’ preferences are represented by mean-deviation functionals, sharing of optimal portfolio creates instruments that, on the one hand, satisfy individual risk preferences but, on the other hand, are not replicable on an incomplete market, so that each agent is strictly better off in participating in cooperative investment than investing alone. This synergy effect is shown to hold when agents’ acceptance sets are represented by cash-invariant utility functions in the case of multiperiod investment with an arbitrary feasible investment set. In this case, a set of all Pareto-optimal allocations is characterized, and an equilibrium-based method for selecting a “fair” Pareto-optimal allocation is suggested. It is also shown that if exists, the “fair” allocation belongs to the core of the corresponding cooperative game. The equilibrium-based method is then extended to the case of arbitrary utility functions. The obtained results are demonstrated in a multiperiod cooperative investment problem with investors imposing drawdown constraints on investment strategies.

Resource Allocation for Cognitive Small Cell Networks: A Cooperative Bargaining Game Theoretic Approach

Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks.

Optimal Cooperation Strategy in Cognitive Radio Systems with Energy Harvesting

In recent years, the excessive energy consumption in wireless communication systems has been increasingly critical, and environmental and financial considerations have motivated a trend in wireless communication technologies to resort to renewable energy sources. Energy harvesting is considered as a promising solution to alleviate such issues and has received extensive attentions. In this paper, we consider a cognitive radio system with one primary user (PU) and one secondary user (SU) and both of their transmitters operate in time-slotted mode. The SU, which harvests energy exclusively from ambient radio signal, follows a save-then-transmit protocol. In such a scenario, we investigate the SU's optimal cooperation strategy, namely, the optimal decision (to cooperate with the PU or not) and the optimal action (to spend how much time on energy harvesting and to allocate how much power for cooperative relay). We separately investigate the optimal action in non-cooperation and cooperation modes to maximize the SU's achievable throughout and derive the optimal closed-form solutions. Based on the analytical results of the optimal solutions, we propose the optimal cooperation protocol (OCP) to make the optimal decision, which simply involves a two-level test. Simulation results show that the proposed OCP outperforms the other two protocols (non-cooperation protocol and stochastic cooperation protocol) and the optimal underlay (OU) transmission mode.