基于卖高行为的航班舱位优化

基于历史定座数据的卖高率估算算法存在高估高价舱需求的风险,为此提出一种基于旅客选择行为的卖高率估算模型,将旅客选择概率引入模型参数,比较符合销售现状。利用期望边际收益方法对原有卖高率模型和所提模型进行收益优化分析,分析结果表明,两者在航班收益上无显著差别,但所提模型会提高航班客座率,这一结果,和模型设计初衷吻合。     

Airline revenue management games with simultaneous price and quantity competition

This paper treats airline revenue management under simultaneous price and quantity competition in a network. Within this setting, a competitor's demand is a function of his price as well as the competitor's price and booking limits. We present a model to optimize a competitor's behavior in a network revenue management game. To the best of our knowledge, our model is the first of this type. We also present an approach to compute a possibly approximate Nash equilibrium in the game assuming that the competitors make decisions based on our model. Since the model is non-linear, we compute equilibrium prices and booking limits separately. The procedure's performance is shown in a computational study. When both price competition and quantity competition are taken into account, prices as well as demands tend to increase leading to higher revenues compared to monopolistic prices.     

基于蚁群算法的航班网络座位优化研究

座位优化是航空公司增加收益的有效方法,航班网络座位优化是目前主要的研究方向。针对起始地 目的地 舱位票价（ODF）和座位数组合的复杂性,传统的优化模型由于决策变量数多,难以用于实际计算;改进的线性规划方法在一定程度上改善了模型的实用性,但在求解大规模的网络问题时,计算时间长,复杂度高。采用蚁群算法求解网络座位优化问题能克服以上不足。实验结果表明,蚁群算法能快速得到令人满意的解;同时,蚁群算法简化了问题复杂度,思想简单,易于实现。     

Hybrid nesting control strategy for passenger railway with one-seat-one-ticket restriction

Nesting control is one of the most prevalent quantity-based controls for the revenue management problem. A popular nesting control strategy for multi-resource problem is the virtual nesting control, which sets nested booking limits on each resource. However, this control was originally developed for the airline and cannot be directly used in the passenger railway with one-seat-one-ticket restriction. Therefore, this paper develops a new nesting control that is applicable to the railway. The proposed control can nest the capacity over different fare classes and origin-destination pairs, which overcomes the shortcomings of existing railway booking-limit controls. Numerical experiments are conducted in various scenarios to evaluate the performance. The results show that the hybrid nesting control outperforms the others in all situations. In addition, the revenue improvement increases with the randomness of demand and discount percentage.     

竞争环境下任务分配方法的研究

以博弈论和纳什均衡理论为基础,介绍了一种基于博弈论的竞争环境下的任务分配方法,提出了竞争环境下任务的分配模型框架,引入了Agent能力的概念,并给出了竞争环境下详细的任务竞争算法和资源竞买算法,最后通过实例获得了满意的结果,同时该实例也表明了本模型的合理性和算法的有效性.     

Oligopolistic spectrum allocation game via market competition under spectrum broker

For future dynamic spectrum access (DSA) systems, new spectrum management methodologies will be adopted such that the licensed primary spectrum holders can reallocate part of their available bandwidth to the unlicensed secondary service providers for profits, based on market-driven mechanisms. This is known as spectrum market or spectrum trading. This paper aims to propose a dynamic spectrum market model where each spectrum holder has a limited amount of spectrum and is allowed to enter a portion of its available bandwidth into the market managed by a spectrum broker, besides its primary services. To model the price dynamics, a continuous-time price adjustment process governed by a differential equation is considered. Subsequently, we show that the problem is a dynamic N-player oligopoly differential game, subject to the bandwidth constraint. We analyze the feedback Nash equilibrium (NE) solutions for the general game and provide a complete, closed-form solution for the special symmetric case. The solution can be characterized into three distinct regions and transitions between these regions may occur as time evolves, which will be thoroughly investigated. In addition, we propose a discrete-time price adjustment implemented at the spectrum broker. Extensive numerical studies are provided to investigate various aspects of the proposed competition.     

双渠道二级供应链库存分配与控制问题研究

针对由仓库和多个零售商组成的二级供应链问题,考虑仓库面临零售商和网络销售两种渠道以及不同零售商的优先级不同的情况,建立解析模型。以最小化供应链总成本为目标,设计一种改进型遗传算法,并提出一种两阶段分配策略求解模型。以实际数据为例,对模型在供应链库存分配与控制问题中的应用进行实例验证,结果表明,所提出策略所得解优于改进的先到先服务（FCFS）策略,并且与Cplex所得精确解的GAP小于10%。数值试验表明所提出的策略能够有效地分配库存,所提出的算法能够有效地求得库存策略的各个参数。     

EMSR在航空收益管理系统中的应用

利用EMSR(期望边际收入)模型，根据收益管理系统中多航段座位分配的特点，建立了分离航班的多航段座位优化分配模型。该方法解决了目前国内航空订座系统无法实现的多航段优化控制问题。     

基于Stackelberg博弈的协作网络功率分配策略

针对协作网络中的功率分配问题,提出基于Stackelberg博弈的分配策略。首先建立博弈模型,源节点根据中继节点分配的功率给出价格;中继节点根据自身资源情况、信道状态、位置信息以及源节点提出的价格,进行协作传输功率的分配,从而构建用户效用函数;接着证明了该效用函数满足凹函数的条件,且存在均衡点,因此参与决策的用户可以通过求解协作功率和价格的Stackelberg均衡解(SE)最大化自己的效用;最后,通过仿真实验验证了均衡点的存在,并对源节点位置不同情况下节点的价格、功率和效用进行了分析,实验中离中继更近的源节点的协作功率和效用分别是距离较远用户的1.29倍和1.37倍。理论分析与实验结果证明了策略的有效性,而且该策略能适用于协作网络及其他分布式网络。     

A population game approach for dynamic resource allocation problems

We consider a water distribution system as an example of resource allocation, and investigate the use of a population game for its control. We use a game-theoretic approach based on two evolutionary dynamics, the Brown–von Neumann–Nash and the Smith dynamics. We show that the closed-loop feedback interconnection of the water distribution system and the game-theoretic-based controller has a Nash equilibrium as an asymptotically stable equilibrium point. The stability analysis is performed based on passivity concepts and the Lyapunov stability theorem. An additional control subsystem is considered for disturbance rejection. We verify the effectiveness of the method by simulations under different scenarios.     

Pareto-optimal Nash equilibrium in capacity allocation game for self-managed networks

In this paper we introduce a capacity allocation game which models the problem of maximizing network utility from the perspective of distributed noncooperative agents. Motivated by the idea of self-managed networks, in the developed framework the decision-making entities are associated with individual transmission links, deciding on the way they split capacity among concurrent flows. An efficient decentralized algorithm is given for computing a strongly Pareto-optimal strategies, constituting a pure Nash equilibrium. Subsequently, we discuss the properties of the introduced game related to the Price of Anarchy and Price of Stability. The paper is concluded with an experimental study.     

网格资源分配博弈的随机动态分析*

针对网格资源分配的优化问题,提出利用随机动态来研究有限网格群体博弈的分析方法。通过建立网格使用者策略选择的随机模型来分析有限网格群体的博弈,并利用期望效用生成选择过程的量化指标来判断使用者在反复博弈中策略选择的变化方向及其稳定性。最后通过仿真实例的研究结果表明,在效用矩阵不变的情况下,群体规模是影响网格使用者策略选择方案的一个重要因素。     

认知无线电中基于博弈论的频谱分配算法*

利用博弈论分析了认知无线电网络中动态频谱分配问题,构建了基于博弈论的认知无线电频谱分配问题模型,提出了基于潜在博弈论的分布式频谱分配算法,并得到了相应博弈过程的纳什均衡。仿真结果表明,该算法能在较短时间内收敛到稳定状态,潜在函数取值达到最大值、系统总干扰水平降到最小、用户的SIR水平得到明显改善,达到了潜在博弈下信道分配的纳什均衡,实现了提高频谱利用率的目的。     

异构网络中基于博弈的多宿主用户带宽分配算法

针对异构无线网络中的带宽分配问题,将网络间的带宽分配模型转化为非合作博弈模型,提出一种基于非合作博弈论的动态带宽分配（ NCRA）算法。首先,根据用户的带宽需求,并充分考虑当前各种不同网络的负载因素,设计一种基于网络能力动态为用户分配带宽的效用函数;然后,通过证明效用函数为凹函数来验证网络间非合作博弈纳什均衡的存在性,并获得网络间的最佳带宽分配策略;最后,通过仿真实验,验证所提出算法的性能。     

基于效益博弈的云计算资源动态可协调分配策略研究

在对用户的任务进行计算资源分配时,为了有效提高计算资源的利用效率,减少任务执行所需要的成本,提出了一种基于效益博弈的云计算资源动态可协调分配机制。该机制采用时间矩阵和费用矩阵作为任务效益的衡量指标,提出效益博弈模型,通过该模型的效益计算方程来得到最好的资源分配策略。为了使得计算资源能够合理地按需进行分配,提出了动态可协调分配机制,在合理地分配资源,满足所有任务正常执行时所需资源的同时,最大化任务的执行效益。实验仿真及对比结果表明,在任务完成时间、任务执行的平均成本、任务完成成功率上,本文算法都取得了较好的效果。     

An Inventory Management Problem

Inventory management is a key problem in several industries, (car renting, storehouse space renting, etc.). It consists of managing a given fleet of equipment in order to satisfy requests to use it. When requests exceed the stock of available equipment, a decision has to be made, either to subcontract some requests to another provider or to purchase new pieces of equipment. The main difficulty lies in the fact that a subcontracted request must be subcontracted for all the duration of the request. For example, if a subcontracted car is rented to a given customer, this customer will keep the subcontracted car for all the duration of the rental. In this paper, we propose a set of benchmark problem instances, derived from real-world inventory management problems.     

Subcarrier and power allocation for OFDMA-based multicast cellular networks using a coalitional game

The subcarrier and power allocation problem for orthogonal frequency-division multiple access (OFDMA)-based multicast cellular networks is investigated in this paper. Due to the intrinsic heterogeneity of the channel gains experienced by different multicast users, the conventional multicast schemes are highly conservative and spectrally inefficient. To address this issue, a novel multicast formulation is proposed where multiuser diversity inherent in OFDMA can be adaptively exploited by clustering users within a multicast group into smaller subgroups based on their channel gains. Subcarriers and power are then dynamically allocated to these subgroups to maximize the total multicast rate of the system. Coalitional game theory is adopted to model the group formation in which users can autonomously form coalitions with other users to compete for network resources. A low-complexity algorithm is proposed for the multicast coalitional game to reach multicoalitional equilibrium in which a sub-optimal performance can be obtained. Simulation results demonstrate that the proposed scheme outperforms the conventional unicast and multicast schemes while achieving a sub-optimal performance comparable to the exhaustive search scheme.     

弹性效用价格竞争博弈的纯策略寡占均衡

讨论了具有弹性效用的并行路径网络的多个服务提供商之间的价格竞争博弈问题。如果每个拥有链路的服务提供商的目的是设定使自己利润最大化的价格,而用户依据Wardrop原理选择自己的传输流量和传输路径。当每个用户的效用函数为凹函数且其一阶导数也是凹函数,网络链路的延迟函数为线性函数时,则证明了这种多个服务提供商之间的价格竞争博弈存在纯策略寡占均衡。     

A supplier selection and order allocation problem with stochastic demands

We consider a system comprising a retailer and a set of candidate suppliers that operates within a finite planning horizon of multiple periods. The retailer replenishes its inventory from the suppliers and satisfies stochastic customer demands. At the beginning of each period, the retailer makes decisions on the replenishment quantity, supplier selection and order allocation among the selected suppliers. An optimisation problem is formulated to minimise the total expected system cost, which includes an outer level stochastic dynamic program for the optimal replenishment quantity and an inner level integer program for supplier selection and order allocation with a given replenishment quantity. For the inner level subproblem, we develop a polynomial algorithm to obtain optimal decisions. For the outer level subproblem, we propose an efficient heuristic for the system with integer-valued inventory, based on the structural properties of the system with real-valued inventory. We investigate the efficiency of the proposed solution approach, as well as the impact of parameters on the optimal replenishment decision with numerical experiments.