需求波动下考虑乘运供应能力的网约车平台动态定价

“共享经济”理念下的网约车平台需求具有随机发生和波动变化的特点.针对乘车需求波动导致不同供需状态下的网约车平台定价问题,运用最优控制方法,以平台期望收益最大化为目标,构建乘运供应能力下的平台动态定价模型,并基于庞特里亚金极大值原理及模型推导,求得最优动态价格解以及平台乘运供应率和需求率的变化轨迹.研究表明:网约车平台的最优价格动态影响乘运供应,且随着市场乘车需求的波动而动态变化;尤其是乘车高峰需求时,最优动态价格将显著减少乘车需求订单的延误.最后,通过数值仿真验证模型结论并进一步探讨平台服务质量和市场乘车需求变化系数对最优价格和期望收益的影响.     

排队系统定价控制问题的研究综述

定价作为服务系统价值因素的调控机制,在拥塞控制、收益管理和服务质量控制等领域有重要的应用.服务提供商设计定价策略使得系统性能在诱导的顾客行为下达到最优.排队模型是分析服务系统性能的重要模型.本文介绍了排队系统定价控制的基本要素,包括排队模型、顾客行为、性能指标以及定价策略类型,围绕静态定价策略、动态定价策略和优先级定价策略,详细综述了排队系统定价控制的研究现状,然后简要介绍了作者针对排队网络动态定价问题的最新研究进展,最后总结了该领域的研究现状、尚未解决的问题、当前的研究热点以及未来的发展方向.     

机票定价与舱位控制两阶段决策方法

针对航空收益管理中定价与舱位控制联合决策问题,提出一种两阶段决策方法.以最大化总收益为目标,建立和分析相应的联合决策模型,包括非嵌套模型(确定性模型和随机模型)和嵌套模型.通过对模型的求解和仿真得到:在价格方面,随机模型定价最高,其次是嵌套模型,确定性模型定价最低;在对低票价等级的订座限制方面,随机模型限制最严,其次是确定性模型,嵌套模型限制最宽松;最终总收益方面,嵌套模型的总收益最高,而随机模型与确定性模型总收益的高低视情况而定.为应对求解大规模嵌套模型算例时的复杂性,分别将非嵌套模型计算所得的定价结果作为嵌套模型的输入价格,求得对应的座位分配结果.对所得到的两阶段策略进行仿真得到,随机模型与嵌套模型相结合所得到的两阶段策略表现更好,能够使总收益接近最优水平.     

IaaS云服务定价机制的仿真研究

定价机制是影响云计算用户利益和云服务提供商收益的关键因素。本文以IaaS云服务定价机制为研究对象,首先将IaaS云服务定价机制分为固定定价和动态定价2大类,其中固定定价细分为即用即付费和预订定价2种,从理论上分析2类定价机制对IaaS云服务提供商收益的影响;然后,分别建立即用即付费和预订定价机制下IaaS云服务提供商的收益模型,并用启发式算法构建动态定价机制下IaaS云服务提供商的收益模型;最后,基于Repast Simphony建立多主体仿真模型,比较分析3种不同定价机制下IaaS云服务提供商的收益。研究结果表明,随着服务时长和顾客数量的增加,动态定价机制更能为IaaS云服务提供商带来更多收益,同时IaaS云服务提供商可以通过改变服务等级和资源价格等相关参数,改变顾客行为,使顾客数量增加,从而提高收益。     

聚合模式下网约车市场的定价策略研究

针对聚合模式给网约车市场带来的收益分配和价格竞争问题,引入平台抽成和出行服务商服务差异刻画聚合模式下网约车市场的特征,构建了聚合平台和两服务商的斯坦伯格博弈模型,探讨了在聚合模式下网约车市场的定价均衡。随后引入平台补贴定价策略,分析了补贴策略对市场均衡定价以及聚合平台、出行服务商期望收益的影响。研究表明：聚合模式下出行服务商服务差异是影响平台抽成比例和网约车市场服务定价的重要因素,对聚合平台和出行服务商的期望收益有双向影响;平台对出行者的价格补贴提高了补贴前网约车市场的服务定价,导致平台抽成增加;采用合理的补贴定价策略可以有效提升出行服务商和聚合平台的收益,同时改进社会福利。     

考虑参照价格效应和消费者行为的预售及退货策略

生产商通过预售可以提前获取市场需求信息,但由于消费者产品估值的不确定性,生产商可能面临现货期大量退货的风险,制定合理的预售与退货策略成为生产商必须解决的核心问题.鉴于此,运用报童模型和消费者期望效用理论,考虑参照价格效应对消费者购买行为以及生产商预售期定价策略的影响,构建单一预售、预售退货不再出售、预售退货再出售3种策略下的期望利润模型,求解得到相应的最优生产量以及不同退货策略下的退货补偿价格取值范围,并对模型进行对比分析.研究表明:预售退货不再售与退货再售情形下,高价预售与低价预售策略中均存在一个退货补偿价格临界阈值;知情消费者中具有参照价格效应的消费者其比率决定了生产商在预售期的定价策略,且低价预售策略下,预售价格随退货补偿价格与参照价格系数的提高而降低.     

消费者等待和转移购买行为下的易逝品定价策略

针对消费者等待和转移两种购买行为下的易逝品销售市场,构建两阶段双寡头零售商降价和匹配定价模型,讨论模型的简化运算方式,分析两种行为构成比例对零售商决策的影响,并进一步论证降价和匹配定价策略的有效性.研究表明,消费者异质购买行为下,等待和转移消费者的构成比例是零售商决策的重要因素,对零售商的期望收益具有双向影响;当消费者...     

一种拥塞补偿的网络服务定价机制

采用经济学方法和定价机制控制用户流量是网络资源分配的一种重要思路.针对软实时应用的带宽需求特性,将面向弹性应用的拥塞定价思想,引入到软实时应用的服务协商和速率控制中来,提出一种拥塞补偿的网络服务定价机制.不同于传统的拥塞定价对所有数据流一视同仁的做法,该定价机制在网络拥塞时,对不同的用户数据流区别对待,向造成网络拥塞的用户收取额外的拥塞费用,而向受到拥塞影响的用户给予适当的价格补偿.探讨了拥塞补偿价格的设置原则,提出与之相适应的拥塞补偿价格动态设置算法.针对服务协商过程中可能存在的用户瞒报或虚报需求的行为,利用博弈论方法,提出了激励相容的网络定价方案和计费策略,并通过实验说明了其有效性.     

基于深度强化学习的生鲜产品联合库存控制与动态定价研究

针对生鲜产品的易逝性特征以及复杂多变的现实环境导致生鲜产品的最优订货和定价策略难以获得的问题,提出了基于深度强化学习方法的生鲜产品联合库存控制与动态定价方法,结合生鲜产品特性对问题进行建模并定义为马尔可夫决策过程,然后基于深度强化学习设计了生鲜品联合库存控制和动态定价算法。实验结果表明,基于深度强化学习的联合库存控制和动态定价策略收益表现最佳,因此,基于深度强化学习的联合库存控制和动态定价研究能够提高企业收益,有效促进强化学习在收益管理领域的落地,具有实际应用价值。     

基于旅客保留价格的客运专线票价最优动态调整

从客运专线市场化运营趋势出发,针对如何通过客票价格的动态调整来最大化客运专线期望收益的问题,基于旅客保留价格,结合已知客票价格和订票旅客到达概率,根据Bellman最优化原理建立了动态规划模型,进而探讨了最优动态票价调整策略的结构特征,证明了包含两个区段客运专线网络的最优动态票价调整策略的阈值特性,并通过算例进行了验证.     

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。     

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。     

Pricing and inventory carryover strategy considering cost learning effect and strategic consumers

Anticipating the markdown in the future price due to the cost reduction of the firm, more and more consumers tend to delay their purchasing to the later period. Incorporating the strategic behavior of consumers, this paper establishes a two-period production and selling model under dynamic pricing strategy and price commitment strategy respectively, with considering stochastic learning effect in which the firm may or may not have the inventory carryover option. The results show that the firm may hold inventory under dynamic pricing while no inventory is kept under price commitment. Additionally, consumers' patience level enhances the benefit of inventory carryover. Furthermore, for the firm with relatively high farsighted level and low inventory holding cost, the dynamic pricing equipped with inventory carryover outperforms the price commitment strategy. Finally, numerical examples are conducted to analyze the impacts of important parameters on the firm's choice of pricing strategies.     

Seat inventory control for sequential multiple flights with customer choice behavior

This paper considers a seat inventory control problem in which flights depart sequentially and passengers purchase available seats depending on customer choice behavior. Customer choice behavior can lead to either a horizontal shift or a booking loss when a desired fare class is unavailable. This problem is mathematically challenging and intractable via exact mathematical models. As an alternative heuristic approach, this paper develops a simulation-based greedy grid-search algorithm and illustrates simulation experiments using the newly developed algorithm. This paper obtains encouraging numerical results with the approach proposed here, but additional studies are required for accommodating more general assumptions such as booking arrival patterns, booking control mechanisms (e.g., cancellation and overbooking) and strategic customer behavior.     

航班座位控制模型实现与分析

收益管理是航空公司在航班销售期间合理分配有限的航班座位,以期在竞争环境下获得最大收益的有效经营策略.本文首先介绍了座位分配常用的两种算法;然后给出了算法的实现方法,并特别强调了在不同应用环境下开发算法动态链接库的方法;最后对两种算法的计算结果和计算时间进行了细致的比较分析.结果表明,两种算法在座位分配效果上无明显差异,...     

Multi-objective bionics design method of passive suspension parameters based on hybrid behavior game

In this paper, a linear passive vehicle suspension model with eight DOF, including six vertical and two rotation motions is given. Considering road surface damage, and ride comfort, taking stiffness and damping of seat and suspension system as design variables; taking seat acceleration RMS, tire’s relative dynamic load and suspension’s maximum dynamic stroke as objective functions, it established a tri-objective optimization model; and it also gave a multi-objective method based on game theory, takes three design objectives as three players, by calculating the affecting factors of the design variables to objective functions and fuzzy clustering, the design variables are divided into different strategic spaces owned by each player. Bionic research on three kinds of male side-blotched lizard’s behavior and survival of multiply mechanism, it defines three lizards as opportunism, egoism and collectivism; sets the three behaviors as corresponding player, and takes self-adaptive function as objective as optimal mono-objective its own strategic spaces and obtains the best strategy to deal with the others. All the best strategies are combined as a game strategy set. By multi-gaming, the final solution meeting given convergence of criterion is obtained. Calculation results show the effectiveness and practicability of the proposed method.     

Flight operations recovery: New approaches considering passenger recovery

The sources of disruption to airline schedules are many, including crew absences, mechanical failures, and bad weather. When these unexpected events occur, airlines recover by replanning their operations. In this paper, we present airline schedule recovery models and algorithms that simultaneously develop recovery plans for aircraft, crews, and passengers by determining which flight leg departures to postpone and which to cancel. The objective is to minimize jointly airline operating costs and estimated passenger delay and disruption costs. This objective works to balance these costs, potentially increasing customer retention and loyalty, and improving airline profitability. Using an Airline Operations Control simulator that we have developed, we simulate several days of operations, using passenger and flight information from a major US airline. We demonstrate that our decision models can be applied in a real-time decision-making environment, and that decisions from our models can potentially reduce passenger arrival delays noticeably, without increasing operating costs.     

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.     

Airline planning benchmark problems—Part II: Passenger groups,utility and demand allocation

This paper is the second of two papers entitled “Airline Planning Benchmark Problems”, aimed at developing benchmark data that can be used to stimulate innovation in airline planning, in particular, in flight schedule design and fleet assignment. The former has, to date, been under-represented in the optimisation literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. Revenue models in airline planning optimisation only roughly approximate the passenger decision process. However, there is a growing body of literature giving empirical insights into airline passenger choice. Here we propose a new paradigm for passenger modelling, that enriches our representation of passenger revenue, in a form designed to be useful for optimisation. We divide the market demand into market segments, or passenger groups, according to characteristics that differentiate behaviour in terms of airline product selection. Each passenger group has an origin, destination, size (number of passengers), departure time window, and departure time utility curve, indicating willingness to pay for departure in time sub-windows. Taking as input market demand for each origin–destination pair, we describe a process by which we construct realistic passenger group data, based on the analysis of empirical airline data collected by our industry partner. We give the results of that analysis, and describe 33 benchmark instances produced.