无线充电传感器网络能量效率优化研究

本文将SWIPT技术应用到无线充电传感器网络中,提出了一种基于SWIPT的协作传输协议（简称CTS）,利用无线信息和能量的协作传输来提高无线能量传输的效率。在此基础上,设计了一个以最大化网络效用为目的的资源分配策略优化问题,并提出RAPOA算法来求解问题。仿真实验表明CTS最大化了网络效用,提高了无线充电传感器网络的能量传输效率。     

无线mesh网络中效用与链路强度联合优化的覆盖多播

支持多播通信是无线mesh网络(WMNs)的一个重要应用.采用基于效用的定价机制,通过拉格朗日(Lagrange)对偶分解法获得一个基于价格的分布式算法.以该分布式算法为核心,作为mesh网络中数据流速率和链路强度调整的优化策略,每条链路根据自己的拥塞状况合理地定价,通过价格机制来调节链路的强度和数据流的速率,使网络净效用最大化.实验结果表明该算法是有效可行的.     

6G重叠区域中基于博弈论的任务卸载策略

为实现6G网络基站服务范围重叠区域内复杂任务的高效计算，对重叠区域的任务卸载问题展开研究。在综合考虑任务时延约束、系统能耗、社会效应以及经济激励的基础上，构建多基站多物联网设备的多接入边缘计算网络模型，联合优化基站定价策略、物联网设备基站选择策略和任务卸载策略，实现基站利润和物联网设备效用的最大化。为解决重叠区域中物联网设备基站选择的问题，构建了多对一匹配博弈模型，提出基于交换匹配的基站选择算法优化物联网设备的基站选择策略。引入斯坦伯格博弈理论建立基站与物联网设备间定价和任务卸载交互的两阶段博弈模型，通过反向归纳法证明斯坦伯格均衡的存在性和唯一性。提出了基于博弈论的最优价格最佳响应算法(Optimal pricing and Best response algorithm based on Game Theory, OBGT),以获得基站和物联网设备的均衡策略。仿真实验和对比实验表明，OBGT算法可以在短时间内达到收敛，有效提高基站利润和物联网设备效用。     

面向5G网络的Stackelberg博弈缓存优化策略

为提高5G网络中的内容缓存效率并降低网络能耗,提出一种基于Stackelberg博弈的缓存优化算法。将网络服务商和内容提供商建模为一个多主多从的Stackelberg博弈模型,内容提供商从网络服务商处购买基站存储空间,以缓存流行和热门内容。构建博弈双方的策略空间和利润函数,并证明给定一组网络服务商的基站租用价格时内容提供商之间存在纳什均衡点。在此基础上,利用分布式迭代算法对博弈模型进行求解,得到网络服务商的基站最优定价和内容提供商的基站最优租用比例。仿真结果表明,与用户QoS优先算法、果蝇算法和全局最优算法相比,该算法能够提高缓存命中率和网络收益,降低网络能耗。     

ICN中免费内容的缓存定价机制

在信息中心网络(Information-Centric Networking, ICN)中, 为了激励网络服务提供商部署网络内缓存, 我们需要制定一种合适的定价机制. 目前ICN的定价机制仅考虑收费内容, 但实际的情况是, 网络中免费内容远多于收费内容. 为此, 本文区别于ICN中传统订阅(收费内容)模型, 首次提出了ICN中一种具有广告商参与的基于免费内容的定价策略. 并利用博弈论对模型进行求解. 文章分析表明, 网络中的参与者存在最佳的广告收费策略、内容缓存策略以及最佳收益点. 仿真结果可以看出, 该模型可以使参与者在竞争过程中实现利润最大化, 从而达到激励ISP部署网络内缓存的目的. 与ICN中已有的方案相比, 我们的方案更加贴合实际, 也能获得更优的收益.     

编码分组网络的效用最大化研究

对基于网络编码方案的分组网络(即编码分组网络)的效用最大化问题进行研究。利用网络编码和网络流的对应关系以及组播树分解方法提出单通话编码分组网络效用最大化模型。基于对偶分解理论推导出解决单通话编码分组网络效用最大化问题的分布式次梯度投影算法,找到一个有效的Lipschiz常数从而得到算法收敛的充分条件。通过仿真验证了该算法的正确性。     

基于Stackerlberg和议价博弈的Femtocell网络功率控制

针对Femtocell网络中存在的干扰问题,提出一种基于Stackerlberg博弈和议价博弈的功率控制策略.首先,给出既可以使Femtocell网络系统容量最大化,又可以体现效用公平的效用函数;然后,在保证宏小区用户效用最大化的条件下,考虑到Femtocell家庭用户的收益,给出Stackelberg博弈模型;接着,考虑到Femtocell家庭用户之间的效用公平和最小服务质量要求,给出基于议价博弈的Kalai-Smorodinsky议价解,并证明议价解的存在性和唯一性.使用拉格朗日乘子法给出宏小区的最优价格,保证宏小区的效用最大化.仿真结果表明,该策略在满足宏蜂窝网络效用最大化的情况下,不但能够满足Femtocell家庭用户最小服务质量需求和公平性,而且Femtocell网络的收益也能得到较好的保证.     

网上零售业多产品配送服务定价研究

随着越来越多的网上零售商开始实施有条件的免运费策略, 如何确定免运费的条件和运费成为电商企业面临的重要问题。该问题抽象成为一个两阶段的博弈模型：首先消费者根据效用最大化的原则确定购买决策, 然后零售商在考虑消费者购买决策的基础上依据利润最大化原则设定物流定价策略。通过算法设计和算例分析, 得到免运费阈值设定在产品价格组合边界时, 零售商利润会发生跳跃。     

网上零售业多产品配送服务定价研究

随着越来越多的网上零售商开始实施有条件的免运费策略,如何确定免运费的条件和运费成为电商企业面临的重要问题。该问题抽象成为一个两阶段的博弈模型:首先消费者根据效用最大化的原则确定购买决策,然后零售商在考虑消费者购买决策的基础上依据利润最大化原则设定物流定价策略。通过算法设计和算例分析,得到免运费阈值设定在产品价格组合边界时,零售商利润会发生跳跃。     

5G移动通信网缓存与计算关键技术研究

由于5G核心网络内容缓存资源的分配不均衡,导致网络切片性能不佳的问题,提出一种高效的缓存资源分配机制。对此,将5G通信缓存资源分配问题转化为网络提供商的收益最大化问题,并构建目标函数,然后采用基于化学反应优化算法CRO对目标函数进行求解。仿真结果表明,相较于其他求解策略,本研究提出的基于CRO的缓存资源分配策略能量消耗更低,且网络切片性能更好。     

Multi-period optimization with loss-averse customer behavior: Joint pricing and inventory decisions with stochastic demand

To maximize a firm's profit over a finite planning horizon, we develop a dynamic optimization model by considering loss aversion when making pricing and inventory decisions. We estimate customer demand through a choice model, which incorporates reference price, utility function and customer loss aversion. Our model forms the core of the expert system for decision support. Through a sequence of Bellman equations, we find that the firm's profit is a concave function of price and inventory, and we solve the model optimally. The profit is positively correlated with the reference price, and the price and inventory decisions are non-monotonic functions of loss aversion intensity. Our results shed new light on pricing and inventory management with customer behavior in a multi-period system. Through various theorem developments, we are able to identify the optimal inventory level and the corresponding price. Numerical examples are provided to illustrate and validate the model and to derive managerial insights. To show the potential significance, we demonstrate how a dynamic programming model yields good results with customer loss aversion under realistic customer behavior assumptions. Our system can improve the efficiency of decision making and provide better customer service.     

OFDMA认知无线电网络中面向功率控制的频谱定价与分配

针对OFDMA认知无线电网络,提出一种基于Stackelberg博弈的频谱定价和分配模型.对于次基站控制次网络传输功率来保护主网络通信的场景,主基站可通过该模型获得最优的频谱定价方案.从功率控制的角度,重新设计次用户的效用函数,运用Stackelberg博弈对单个主基站和多个次用户在频谱租赁市场中的交易行为进行建模.通过逆向归纳法,求解市场均衡下的最优频谱定价,使得主基站在考虑主网络QoS降级的同时获得最大收益.此外,对于主基站只能获取本地信息的情形,提出了基于动态Stackelberg博弈的分布式频谱定价和分配模型.仿真实验表明,该模型能够在控制次网络传输功率的基础上,提供最优频谱定价和频谱分配方案.     

A novel dynamic pricing scheme for contributing peers in the VoD system

In this paper we have proposed a dynamic pricing scheme for the contributing peers in the Video on Demand (VoD) system. The scheme provides an effective mechanism to maximize the profit through the residual resources of the contributing peers. A utilization function is executed for each contributing peer to estimate the utility factor based on the parameters such as initial setup cost, holding cost, chaining cost and salvage cost. In this paper, we urge an effective dynamic pricing algorithm that efficiently utilizes a range of parameters with a varying degree of complexity. The key findings of the algorithm are (i) each contributing peers are benefitted by the monetary based on its resource contributions to the VoD system and (ii) a high degree of social optimum is established by proficiently aggregating the contributing peer’s resources with the overall resources of the VoD system. We validate our claim by simulating the proposed dynamic pricing scheme with other standard pricing schemes such as altruism, cost model and game theory perspective. The result of our dynamic pricing scheme shows the best utility factor than other standard pricing schemes.     

On pricing algorithms for batched content delivery systems

Businesses offering video-on-demand (VoD) and downloadable-CD sales are growing in the Internet. Batching of requests coupled with a one-to-many delivery mechanism such as multicast can increase scalability and efficiency. There is very little insight into pricing such services in a manner that utilizes network and system resources efficiently while also maximizing the expectation of revenue. In this paper, we investigate simple, yet effective mechanisms to price content in a batching context. We observe that if customer behavior is well understood and temporally invariant, a fixed pricing scheme can maximize expectation of revenue if there are infinite resources. However, with constrained resources and potentially unknown customer behavior, only a dynamic pricing algorithm can maximize expectation of revenue. We formulate the problem of pricing as a constrained optimization problem and show that maximizing the expectation of revenue can be intractable even when the customer behavior is well known. Since customer behavior is unlikely to be well known in an Internet setting, we develop a model to understand customer behavior online and a pricing algorithm based on this model. Using simulations, we characterize the performance of this algorithm and other simple and deployable pricing schemes under different customer behavior and system load profiles. Based on our work, we propose a pricing scheme that combines the best features of the different pricing schemes and analyze its performance.     

面对损失厌恶顾客的零售商订货定价策略及激励问题研究

考虑顾客存在损失厌恶, 研究零售商的订货和定价策略, 以及顾客损失厌恶程度对零售商决策行为的影响, 同时探讨零售商通过补偿契约对损失厌恶型顾客的激励问题, 得到如下主要结论: 顾客损失厌恶导致零售商利润和零售价格下降, 订货量和需求量增加; 零售商采取补偿契约能够提高零售商利润、零售价格、订货量和需求量, 有效弱化了顾客损失厌恶给企业带来的负面影响, 并且, 只要补偿额不超过一定限度, 补偿契约也会增加顾客的效用, 实现企业与顾客双赢.     

A profit maximizing mathematical model for pricing and selecting optimal product line

Variety among products is manifested in terms of different attributes or features present in a product. Each attribute in its turn, may be built-in in the product at different levels, giving rise to an increased choice for the customer. However, higher level of an attribute, yielding higher utility for the customers, typically requires higher costs and commands higher price. Increasing price results in higher profit margin but lowers the attractiveness of the product to the customers. In this regard, product line optimization is concerned with the offering of a set of product variants to a large customer base, such that, certain objectives like maximization of buyers’ utility, seller’s return can be met. In this paper, a mathematical programming model has been developed, to determine the optimal product combination that fetches the maximum profit from a potential targeted market segment. Contrary to the existing formulations on product line optimization, where the pricing decision is determined exogenously, we consider simultaneous decision on pricing and product line optimization. Price is considered as a decision variable along with other attributes. Another major departure from the past models is the incorporation of costs associated with each level of attributes. These costs, used as input data, are justified, as the manufacturers typically use combinations of different modules to create different levels of the attributes. The resulting model became computationally complex and hence a greedy heuristic is developed for the purpose. An example is provided to illustrate the working of the heuristic. The same example has been solved for optimality, yielding identical solution to the heuristic results. Finally, a proposition has been presented to show the condition under which the heuristic will give the optimal solution.     

An in-process customer utility prediction system for product conceptualisation

Customer involvement in new product development, especially in the early stage of product conceptualisation, plays an important role for a successful product. In this study, a customer utility prediction system (CUPS) is proposed. The system comprises two modules, namely design knowledge acquisition module and customer utility evaluation module. In the design knowledge acquisition module, a knowledge acquisition technique called general sorting is utilised to establish a design knowledge hierarchy (DKH), in which design options can be generated. In the same module, customer voices towards diverse design options called customer-sensitive design criteria are solicited from customer requirements. Subsequently, in the customer utility evaluation module, a measurement for customer desirability, i.e. customer utility index (CUI), is formulated using conjoint analysis (CA) technique. Finally, the rated criteria are also used as inputs to a radial basis function (RBF) neural network for in-process customer utility prediction. A case study on cellular phone design is used to illustrate the proposed approach.     

Bandwidth trading under misaligned objectives: Decentralized measurement-based control

This paper studies the interplay between a profit-maximizing network and a number of users competing for the finite bandwidth on each link. In our setting, the objectives of the network and the users are ‘misaligned’, in that the prices that optimize the network’s profit do not maximize the aggregate utility of the users. The links set the prices for bandwidth and the users react to them by revealing their preferred amounts of bandwidth.A first contribution of this work is an iterative procedure for a single-link model. In this provably convergent scheme, the link adapts the price to achieve profit maximization, and each individual user adapts its demand for bandwidth so as to maximize its ‘compensated utility’, where utility is a function of its allocated bandwidth. Importantly, the scheme relies on communication between the link and the individual users, but not between users.In practice, the utility of the users (ISPs) strongly depends on the level of satisfaction experienced by their clients (the end-users). We show how the iteration scheme can be adapted to the more natural situation of utility being a function of the loss probability, rather than a function of the bandwidth. Since the end-users’ supply of traffic is not fully known to the ISPs, we develop a Bayesian approach for estimating the loss probability from measurements; we do so in the practically relevant context of Gaussian input traffic. The resulting estimator proves to be particularly useful for risk-averse ISPs.     

Learning approaches for developing successful seller strategies in dynamic supply chain management

Variable, dynamic pricing is a key characteristic of the modern electronic trading environments, allowing for prices that change or fluctuate due to uncertainty and different conditions and context. Being able to manage dynamic pricing strategies is vital for companies wishing to succeed in the world of modern business. The ability to accurately predict selling prices at a given time can help organizations to maximize their profit. This paper addresses the problem of predicting customer order prices and choosing the selling strategy which can lead to a greater profit in the context of supply chain management (SCM). The potential of the Neural Networks (NN) and Genetic Programming (GP) learning techniques is explored for making price forecasts. In particular, different parameter settings and methods for preprocessing input data are investigated in the paper. Although, both techniques showed the potential for dealing with the problem of dynamic pricing in SCM, NN models outperform GP models in the context under consideration in terms of accuracy of prediction, complexity of implementation, and execution time.     

基于零售商创新投入的双渠道供应链协调策略

针对制造商在传统实体渠道的基础上积极开辟网络渠道的双渠道供应链,从消费者效用理论角度构建单一渠道和双渠道供应链Stackelberg博弈模型,在零售商创新投入和消费者渠道偏好扰动因素下,探讨制造商开辟网络渠道形成的渠道竞争效应,寻求供应链系统的组合优化策略,通过一个双重协调机制改善供应链运作效率.研究表明:批发价格、零售价格及创新投入水平的最优组合策略可实现供应链优化;制造商和零售商利润均与创新投入水平呈特定函数关系;网络渠道的引进可降低零售商定价和利润,减少实体渠道市场份额,但总需求增加;双重协调机制可缓解渠道冲突.最后通过数值仿真结果验证了协调机制的可行性.