酒店收益管理系统(文献翻译)
酒店收益管理
酒店Ade问题在于:预订部、前台、销售部都没有在意第二天de客房供给.酒店A内上至经理下至员工几乎没有人意识到客房出租率预测de 重要性,进而导致预订部与前台在不知晓客房供给de情况下,持续不断地接纳订单,以致于多订了一个团,使其自己de酒店进入一种尴尬de 困境.酒店B则处理得很好,在保证自己持续稳定运营de基础上,不但帮助了别人,得到了别人de感激,同时自己也获得了收益 由此案例可以看出:如果预测意识不强或者对房间de控制不是很好会给酒店带来很大de损失.任何一家酒店都应该以预测为基础,将预测 作为一个非常重要de数据
特别关注
有些酒店接旅游团队或会议团 队是因为酒店de需求不足或者 在淡季或者该时间段房间销售 不出去,所以接一些旅游团队 或会议团队来充出租率,最后 可以提高酒店de收益
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一、客源市场de类型—散客市场细分
门市价散客 Rack
门市价散客de出租率不是 很高,但是收入很高,一间 房可能卖到团体de两间房 de价格
其他散客 Individual Others
其他散客即不属于以上市场类别de散客. 这是因为有些酒店在客户de开发上还不 是很完全,因而把这些归入其他散客.然 后再从这些散客中进行归类、分析,有目 de性地将其拉回为自己de顾客
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一、客源市场de类型—散客市场细分
协议公司会议团体 Corporate Meetings
客房需求量 ＜ 客房供给量 待售客房de价值消失
客房需求量 ＞ 客房供给量 无法满足顾客需求,潜在de收益消失 平衡供给和需求之间de矛盾一直是酒店业研究de 重要课题,因此,准确估计需求和合理分配资源de 迫切引发了收益管理de出现
收益管理de重要性
对于管理de价值 只有了解了收益目标,才能根据收益目标采取措施 来提高管理de水平和服务de水准
酒店行业酒店收益管理系统方案
酒店行业酒店收益管理系统方案第一章酒店收益管理概述 (2)1.1 收益管理的定义与重要性 (2)1.1.1 定义 (2)1.1.2 重要性 (2)1.2 收益管理的发展历程 (3)1.2.1 起源 (3)1.2.2 发展 (3)1.2.3 现状 (3)1.3 酒店收益管理的目标与原则 (3)1.3.1 目标 (3)1.3.2 原则 (3)第二章市场分析与预测 (4)2.1 市场需求分析 (4)2.2 市场竞争分析 (4)2.3 市场趋势预测 (4)第三章数据收集与处理 (5)3.1 数据收集途径与方法 (5)3.1.1 数据收集途径 (5)3.1.2 数据收集方法 (5)3.2 数据清洗与整理 (5)3.2.1 数据清洗 (5)3.2.2 数据整理 (6)3.3 数据分析与挖掘 (6)3.3.1 数据分析 (6)3.3.2 数据挖掘 (6)第四章收益管理策略制定 (6)4.1 价格策略 (6)4.2 促销策略 (7)4.3 产能策略 (7)第五章预订与渠道管理 (8)5.1 预订系统优化 (8)5.2 在线旅行社（OTA）合作管理 (8)5.3 预订渠道分析与优化 (9)第六章收益管理实施与监控 (9)6.1 收益管理方案实施 (9)6.1.1 实施准备 (9)6.1.2 实施步骤 (9)6.2 收益管理效果评估 (10)6.2.1 评估指标 (10)6.2.2 评估方法 (10)6.3 收益管理风险控制 (10)6.3.1 风险识别 (10)6.3.2 风险防范措施 (10)第七章人力资源管理 (10)7.1 员工培训与激励 (11)7.1.1 员工培训 (11)7.1.2 员工激励 (11)7.2 团队协作与沟通 (11)7.2.1 团队协作 (11)7.2.2 沟通机制 (12)7.3 人力资源优化配置 (12)第八章技术支持与创新 (12)8.1 收益管理技术发展 (12)8.2 人工智能在收益管理中的应用 (13)8.3 技术创新与行业变革 (13)第九章酒店收益管理案例解析 (14)9.1 成功案例分享 (14)9.1.1 项目背景 (14)9.1.2 收益管理策略实施 (14)9.1.3 成果展示 (14)9.2 失败案例剖析 (14)9.2.1 项目背景 (14)9.2.2 收益管理策略实施 (14)9.2.3 原因分析 (15)9.3 案例总结与启示 (15)第十章酒店收益管理未来发展趋势 (15)10.1 行业发展趋势 (15)10.2 收益管理新理念 (15)10.3 酒店业发展机遇与挑战 (16)第一章酒店收益管理概述1.1 收益管理的定义与重要性1.1.1 定义收益管理（Revenue Management），作为一种旨在最大化企业收益和利润的经营管理策略，通过科学的方法对产品或服务的价格、库存、需求等因素进行优化配置。
酒店收益管理系统(文献翻译)
为酒店群体顾客而设的技术收益管理系统Pablo Cortés, Luis Onieva, Jesús Muñuzuri 西班牙塞维利亚大学（作者承认由Ministerio de Educación y Ciencia，Spain.所给的财务支持）文章信息文章历史：2008年4月1日投稿2009年3月1日收到修订版2009年4月1日接受发表关键词：收益管理顾客群体酒店摘要这篇文章讨论了收益管理：一种关注在决策环节使每个销售单位利润最大化的技术。
新生的技术管理在收益管理的技术发展中承担着一个主要的角色。
在技术管理方面，每个新的改进能带来更加有效的商业收益能力。
现今，决策支持的收益管理系统和技术管理是在服务性行业取得成功的重要因素。
在此文章中例举了酒店的群客群体的一些特殊案例。
也介绍了一种能为酒店设定收益最大化标准的新的决策支持系统。
上述的系统包括了一系列的顾客需求预测方法，同时讨论了单个顾客以及顾客群体的一般情况。
收益管理系统也包含了确定性以及猜测性的数学模型来采取最优决策。
实际上的收益取决于酒店采用的预约系统。
收益管理系统运用一个仿真工具做了一个不同的房间库存管理比较，结果包含了性能指标，例如入住率、实用率以及销售量；通过比较结果，选择其中一个方法。
通过实际数据的测试，收益管理系统证明了自身的适用性，从而成为一个创新和有效的酒店预订系统管理工具。
©2009 Elsevier Inc。
版权所有1.介绍在不适合自身的情况下，许多公司仍然试图利用收益管理技术。
由于公司想运用收益管理技术从越来越多的业务流程中获取利润，研究人员对这些需求作出了回应。
在过去，不同行业最常运用以这种技术为基础的特点。
脆弱的产业，比如面包师、杂货商、水果供应商和剧院经理，通过在不同的时间段设定不同的价格限定了顾客需求。
跟随着美国航空公司在1978年违反规定的行为，现今各个航空公司能够采用它们自身选择的任意航线任意时间的任意价格，Smith, Leimkuhler 和Darrow (1992)。
RevenueManagement酒店收益管理
RevenueManagement酒店收益管理Introduction to Revenue Management收益管理简介What is Revenue Management?什么是收益管理？Components ofRevenue Management收益管理的组成部分Necessary Conditions必要条件Relatively fixed Capacity相对固定的库存量Time-perishable Inventory非耐久性产品Time-variable Demand季节性需求量Appropriate Cost Structure合理的成本结构Segmented Market市场细分化AdvanceBooking预订What is Revenue Management?什么是收益管理？Selling the right PRODUCT for the right PRICE to the right PEOPLE at the right TIME through the right DISTRIBUTION CHANNEL in order to MAXIMIZE REVENUE for the hotel在适当的时间、通过适当的分销渠道、以适当的价格、向适当的客户销售适当的产品，以此最大化酒店的收益。
PRODCUT: RoomTypes, Room Nights, F&B, Meeting Space 产品：房型，房晚, 餐饮, 会议设施PRICE:Group,Transient, Wholesale, etc价格：团队价、散客价、批发价及其他PEOPLE:Corporate, Leisure, etc客户：商务客、休闲客及其他TIME:Seasonality, Weekday vs. Weekend, LOS时间: 季节性，周中 vs. 周末，住客时间长短DISTRIBUTION CHANNEL分销渠道Call Center电话预订中心On property酒店直接定房GDS (Global Distribution System)全球分销系统 品牌网站3rd Party Channels (Ctrip, Elong, Expedia)第三方渠道（携程、易龙、Hotwire）Components of Revenue Management收益管理的组成1. CompetitiveAnalysis 竞争分析2. Forecasting 预测3. Pricing 定价4. InventoryControls 客房控制5. PerformanceMeasurement 考核指数RM Component—Competitive Analysis竞争分析Helps Determine the“Competitive Set”帮助确定“竞争对手组”A hotel’s closest competitors in terms of product, geography,and/or type of business在产品、地理位置以及/或客户方面最接近的竞争对手Primary and secondary competitive sets主要竞争对手组以及次要竞争对手组Macro-Level: Determine the value of the hotel in the overallmarketplace based on the product positioning 宏观层面：根据产品确定酒店在市场中的定位及价值Micro-Level: Benchmark performances against competitors 微观层面：衡量与竞争对手表现差异RMComponent—Forecasting预测Why do we forecast?我们为什么要预测？Determine pricing确定价格Based on UNCONSTRAINE DDEMAND基于”无限制的需求”Unconstrained demand = the number of people who would have stayed at the hotel if it had an infinite number of rooms.无限制需求 = 假设酒店无房间数限制情况下的住店客人数Must measure unconstrained demand to determine price sensitivity of the customer.All customers have different price sensitivities based on product, market, and individual needs.必须通过衡量无限制的需求来了解客人的价格敏感度。
收益管理
应用实例
应用实例
著名的万豪酒店集团是世界酒店业第一家引进收益管 理策略的酒店公司，也是世界第一家投资订做收益管理 电脑系统的酒店公司。该公司董事长、首席执行官比尔 · 马里奥特称，在收益管理实施的头三年，该公司平均每 年有约 1 亿美元的收入直接来源于收益管理。收益管理的 实施为万豪提高利润率起到很大作用。随后，美国的希 尔顿、喜达屋、凯悦等酒店巨头以及法国的雅高、英国 的洲际等集团公司也实施了收益管理。
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Confidentiel 2014
主要作用
(1) 顾客分类及需求预测：不同的顾客对酒店的要求 往往不同。尽管每一酒店有其自己的市场定位，但 顾客的性质，来源渠道以及消费特点仍有许多不同 之处。收益管理的一个重要功能就是通过科学的方 法对不同的顾客进行分类，并得出各种行为模式的 统计特性，然后再对每一类顾客的未来需求进行精 确的预测，包括预订的迟早，入住 的长短，实际入住和预订的差异，提前离店和推迟 离店的概率等等。有了这些精确的预测，再根据各 种客人对价格的敏感度等，酒店就能很好地控制资 源，提高收益。
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Confidentiel 2014
主要作用
(6) 团体和销售代理管理：团体销售几乎是每 一酒店都有的业务，且多数情况下有一定的 折扣。但如何定量地对这项业务进行分析并 有效地控制折扣程度，则是收益管理的很重 要部分。相应地，对代理销售及批发代理等 ，也都可通过抽象的模式来进行优化控制。
Smart Prices.
酒店收益管理
Titreof Contents Table
Sections 内容简介 酒店价格与收益管理主要功能 行业专家介绍 收益管理在中国的发展状况
酒店管理系统 外文文献 外文翻译 中英翻译
Hotel Management System Integration Services1.IntroductionIt is generally accepted that the role of the web services in businesses is undoubtedly important. More and more commercial software systems extend their capability and power by using web services technology. Today the e-commerce is not merely using internet to transfer business data or supporting people to interact with dynamic web page, but are fundamentally changed by web services. The World Wide Web Consortium's Xtensible Markup Language (XML) and the Xtensible Stylesheet Language (XSL) are standards defined in the interest of multi-purpose publishing and content reuse and are increasingly being deployed in the construction of web services. Since XML is looked as the canonical message format, it could tie together thousands of systems programmed by hundreds of programming languages. Any program can be mapped into web service, while any web service can also be mapped into program. In this paper, we present a next generation commercial system in hotel industry that fully integrates the hotel Front Office system, Property Management System, Customer Relationship Management System, Quality Management system, Back Office system and Central Reservations System distributed in different locations. And we found that this system greatly improves both the hotel customer and hot el officer’s experiences in the hotel business work flow. Because current technologies are quite mature, it seems no difficulty to integrate the existing system and the new coming systems (for example, web-based applications or mobile applications). However, currently in hotel industry there are few truly integrated systems used because there are so many heterogeneous systems already exist and scalability, maintenance, price, security issues then become huge to be overcome. From our study on Group Hotel Integration Reservation System (GHIRS), there are still challenges to integrate Enterprise Information System (EIS), Enterprise Information Portal system (EIP), Customer Relationship Management system (CRM) and Supply Chain Management system (SCM) together because of standardization, security and scalability problems, although GHIRS is one of few integration solutions to add or expand hotel softwaresystem in any size of hotel chains environment.We developed this system to integrate the business flow of hotel management by using web services and software integration technologies. In this paper, firstly we describe a scenario of hotel reservation and discuss the interaction between GHIRS and human. Secondly we analyze details of design and implementation of this system. The result and implications of the studies on the development of GHIRS are shown in the later part. Finally we discuss some problems still need to be improved and possible future directions of development.2. Hotel Reservation: A Business Case StudyOur initial thinking to develop GHIRS is to minimize the human interaction with the system. Since GHIRS is flexible and automated, it offers clear benefits for both hotel customers and hotel staff, especially for group hotel customers and group hotel companies. Group hotel companies usually have lots of hotels, restaurants, resorts, theme parks or casinos in different locations. For example, Shangri-La group has hundreds of hotels in different countries all over the world. These groups have certain customers who prefer to consume in hotels belong to the same group because they are membership of the group and can have individual services.The first step of a scenario of hotel reservation is that the consumer plans and looks for a hotel according the location, price or whatever his criteria and then decides the hotel. Then he makes a reservation by telephone, fax, internet, or mail, or just through his travel agent. When hotel staff receives the request, they first look if they can provide available services. If there is enough resource in the hotel, they prepare the room, catering and transportation for the request and send back acknowledgement. At last the guest arrives and checks in. The business flow is quite simple; however, to accomplish all these tasks is burdensome for both the consumer side and the hotel side without an efficient and integrated hotel management system.Telephone may be a good way to make a reservation because it is beyond the limit of time and space. Guests can call hotels at any time and any place. However, itcosts much when the hotel is far away from the city where guest lives; especially the hotel locates in a different country. Moreover, if there is a group of four or five people to make reservation together, it would take a long time for hotel staff to record all the information they need. Making reservation by travel agent saves consumers’ time and cost, but there is still millions of work for agent to do. They gather the requirements from consumers, then distribute to proper destination hotels. Because these hotels don’t use a same system (these thousands of hotels may use hun dreds of management systems), someone, agent or hotel staff, must face the problem how to handle information from different sources with different hotel management systems to different destinations.Web service becomes the tool to solve these problems. Our web services integrate the web server and hotel management system together, and everyone gets benefit. Booking a room easily anywhere and anytime becomes possible by using GHIRS. Consumer browses websites and finds hotel using his PC, PDA or mobile phone (WAP supported), after his identity is accepted, he can book a reservation. Two minutes later he can get the acknowledgement from the hotel by mobile phone text message or multimedia message, or email sent to his email account or just acknowledgement on the dynamic web page, if he hasn’t leave the website. The response time may take a little longer because when the hotel receives the quest, in some circumstance, hotel staff should check if there is clean and vacant room left. The web service is a standard interface that all travel agents can handle, gather and distribute the reservation information easily through internet. When the reservation request is acknowledged, hotel staff prepares the room, catering, and transportation for guests. Since the information already stored in the database, every part in the hotel chains can share it and work together properly. For example, staff in front office and housekeeping department can prepare room for guests according to the data, staff in back office can stock material for catering purpose and hotel manager can check business report in Enterprise Information Portal integrated with GHIRS by his browser. Then room rent-ratio reports, room status reports, daily income reports and other real time business reports are generated. Managers of the group can access anyreport of any hotel by the system. In the later part of this paper, we will show how consumers, agents, and hotel staff can efficiently work together by GHIRS.GHIRS is scalable for small-to-large hotel chains and management companies, especially good for hotel group. It truly soars with seamless connectivity to global distribution systems thereby offering worldwide reservation access. It also delivers real-time, on line reservations via the Internet.3. Integration of Hotel Management System3.1 Existed SystemGHIRS is developed on the base of an existed hotel management system called FoxhisTM. FoxhisTM shares the largest part of software market in hotel industry in China. FoxhisTM version 5 has distributed Client/Server architecture that the server runs SCO-UNIX and client runs Microsoft Windows and it use Sybase database on UNIX. The system includes Front Office system, Property Management system, Quality Management system, Human Resource Management system, Enterprise Information Portal system (EIP), Customer Relationship Management system (CRM) and Supply Chain Management system (SCM).This system is largely based on intranet environment. Most of the work is done in a single hotel by the hotel staff. It’s no customer self-service. If a consumer wants to book a room, hotel staff in local hotel must help the guest to record his request, although FoxhisTM system already done lots of automatic job.When the systems are deployed in different hotels that are parts of a group, sharing data becomes a problem. Just as an example, if the group has ten hotels, there would be at least ten local databases to store the consumers’ data. Because hotels need real time respond of the system, so these ten hotels can’t deploy a central database that does not locate in the same local network. Thus one guest may have different records in different hotels and the information cannot be shared. By web services as an interface, these data can be exchanged easily.3.2 DesignRecall that our initial thinking to deploy GHIRS is to save hotel staff, travel agents and consumers’ labor work the system is to link all the taches of hotel business chains. Figure1 shows how consumers, agents, hotel staff cooperate together efficiently with the system.Consumers could be divided into two categories. One is member of hotel group, who holds different classes of memberships and gains benefits like discount or special offers. These consumers usually contribute a large part of the hotel’s profit then are looked as VIP. The hotel keeps their profiles, preferences and membership account status. The other category is common guest. All these two kinds of guests and travel agents who may trade with many other hotels face the web-based interface that let them to make a reservation. For common guest, the system just requires him to input reservation information such as guest name, contact information, arrival and departure the system. The central processing server then distributes the information to appropriate hotel. Since web services technology is so good for submitting documents to long running business process flows, hotel staff could easily handle this data in and out of database management system and application server. As the membership of hotel, a user just inputs his member id and password, room information, arrival and departure date, then finish the request. Because hotels keep members’ profile, and systems exchange profile across all hotels of the group by web services, hotel staff in different hotels could know the guest’s individual re quirement and provide better services.The agents work for consumers get benefits from GHIRS as well. They may also keep the consumers’ profile and the web services interface is open to them, it is easy to bridge their system to hotel management system. Before GHIRS is deployed, the agents should separate and process the reservation data and distribute them to different hotels, which is an onerous job. But now the agents could just press one button and all the hotel reservation is sent to destination.Hotel staff receives all request from different sources. Some policies are appliedto response the request. For example, some very important guest’s request is passed automatically without confirmation, the guest could get acknowledgement in very short time. The request triggers all chains of the hotel business flow and all the preparation work is done before his arrival. But for the common customer, hotel staff would check on the anticipate date if there is vacant and clean rooms available. Because all the FoxhisTM components are integrated together, staff users needn’t change computer interface to check he room status. If it is a valid request with enough guests’ information and there is enough room left, a confirmation is sent back. If there is not enough vacant room, hotel staff will ask if guest would like to wait a time or transfer to other hotels in the hotel group or alliance hotels. In order to transfer guest’s request, data flows from local database to the central server through local web server, then it is passed to another hotels database by web services interface.3.3 ImplementationToday there are lots of platforms that could provide capabilities to integrate different system and offer other features such as security and work load balancing. The two main commercial products are Java2 Enterprise Edition (J2EE) and . They offer pretty much the same laundry of list of features, albeit in different ways. We choose .NET platform as our programming environment, however, here we don’t advo cate which platform is better or not. Our target is to integrate these decentralized and distributed systems together. In fact, both of these platforms support XML and SOAP to accomplish our task.We use Microsoft Internet Information Services (IIS) as web server and Sybase database server. The firewalls separate the local networks from the public networks. This is very important from the security point of view. Each hotel of the group has a database server, an application server and a web server to deploy this multi-tier system that includes the user interface presentation tier, business presentation tier, business logical tier, and the data access tier. C# is adopted as the programming language for the core executable part. XML is the data exchange standard format.酒店管理系统集成服务1.简介人们普遍认为，网络服务角色在企业中无疑是重要的。
浅谈收益管理(Yield Management)在中国饭店中的应用
浅谈收益管理（Yield Management）在中国饭店中的应用一、引言随着物质生活水平的提高和旅游业的发展，人们对于饮食住宿需求的多样化和个性化逐渐增加，饭店经营环境也越来越复杂和变化多端，所面临的竞争压力也越来越大。
饭店通过不断改进管理模式、提高服务质量和拓展业务范围等途径，企图获得更多的市场份额和利润水平。
其中，收益管理（Yield Management）不仅是饭店营销管理的重要手段，也是提高饭店经营效益的关键因素之一。
本文结合国内饭店实际情况，探讨饭店收益管理在中国应用的现状及面临的挑战，并提出几点对策建议。
二、收益管理的概念和目的收益管理（Yield Management），是指在保证服务质量和客户满意度的情况下，通过策略性的定价、商品组合、容量控制等方法，以达到最大的收益利润（Rosenthal, 2003）。
收益管理的目的在于优化饭店的收益利润，使饭店能够在综合经营效益方面达到最佳的利润水平。
其基本思路是将低价格的销售品预留给需求不高的客户群体或较早预订的客户，而将高价销售品留给需求大或较晚预订的客户群体。
因此，饭店的收益管理旨在利用时间差、需求差和容量差等因素，通过灵活调整价格、销售对象和供应容量来控制市场的需求，达到最大的经济效益。
三、收益管理在中国饭店中的应用现状收益管理这种先进的酒店管理方法，在我国饭店管理中应用不够广泛，也未完全形成系统化的理论体系。
造成这种现象的原因是多方面的，如行业环境的复杂性、饭店管理者的收益管理理念还不够成熟和饭店信息化技术的应用还存在着问题。
1. 行业环境的复杂性饭店经营的环境，尤其在中国这个餐饮人口大国，饭店经营管理面临的竞争环境要比其他国家的饭店经营竞争更为激烈和复杂。
其次，还有饭店客户的偏好、需求以及消费水平的不稳定性等问题，都给饭店收益管理带来了挑战。
2. 管理者的收益管理理念不够成熟还有一部分饭店管理者所采取的收益管理方式，往往只是定价，很难全面维护饭店的盈利能力。
收益管理系统是饭店获取最佳收益的法宝
收益管理一种可以提高汽车租赁经营效益的新管理技术效益管理的用途和效果任何一个租赁公司，都不会僵化的执行一成不变的价格标准，好比车辆状况相近的桑塔纳，对客户甲租金是4500元/月，对客户乙租金是4000元/月，对客户丙租金可能是2500元/月。
这种对同一车型实行不同，甚至差别较大的定价办法，十分普遍。
但如何做到“鱼与熊掌兼得”，即保住某些肯付高租金的客户，又可以尽可能的以低租金对某些客户的诱惑，提高出租率增加租金收入，通过不同租金的组合，获得最佳收益哪？这就是收益管理要解决的问题。
上世纪八十年代，收益管理(Revenue Management 或Yield Management) 这种谋求收入最大化的新经营管理技术出现了。
收益管理是一种用于制订最佳定价方针的手段，而最佳定价方针能够使销售或服务产生最大利润。
收益管理亦称“效益管理”或“实时定价”，它主要通过建立实时预测模型和对以市场细分为基础的需求行为分析，确定最佳的销售或服务价格。
收益管理实际上是一个很复杂的系统，它包括了多种管理策略。
收益管理开始是由民航开发，目的是以最大赢利方式分配一趟航班的座位，以达到固定能力来匹配各细分市场的潜在需求。
效益管理在航空业取得了辉煌的成绩，据美国航空公司的统计，1989至1991年期间，效益管理系统的运用给该公司增加了14亿美元的收入，同期的税后利润增加了8.92亿美元。
曾名噪一时的美国人民捷运航空公司的前首席执行官唐纳德伯尔在公司1996年破产后总结原因时说：“1981年到1985年期间我们是一个充满活力和盈利的公司。
随后我们开始从顶峰跌入到每月亏损5千万美元的地步。
我们公司没有任何变化，可是美国航空公司却将他们的效益管理渗透到我们的每一个市场。
当美国民航以终结者的面目出现时，我们盈利的日子就彻底结束了。
我们丧失了防卫能力，我们末路到了，因为他们总能够比我们价格或则即将公布的价格要低。
”效益管理是如此重要的保护利益的战术武器，并且十分有效，在目前高度竞争的环境中在商业领域得到了广泛应用。
收益管理系统在酒店业的应用
收益管理系统在酒店业的应用作者：陈文力刘志华来源：《商场现代化》2008年第21期[摘要] 在当今竞争激烈的环境下，收益管理系统为酒店提高收益提供一种先进的管理思想和方法。
本文对酒店收益管理的概念进行了探讨，论述了收益管理在酒店业中应用。
[关键词] 收益管理酒店业团队散客当今酒店之间的同业竞争日益加剧，尤其是后奥运时代的北京地区酒店之间的竞争将更加激烈。
酒店能否在激烈的市场竞争中处于不败之地，其中一个重要因素就是是否应用了收益管理的方法来进行酒店的经营管理。
若能较好地利用收益管理系统，可以有效地提高酒店的竞争能力。
一、酒店收益管理的概念收益管理是在20世纪80年代开始应用于酒店业。
因为收益的高低取决于房价的高低和与之相应的房间数，故酒店的收益管理就是在考虑需求预测的基础上通过对客房入住率的调整来达到客房收益的最大化。
其基本要素是价格和房间，即对价格和房间的管理。
旺季尽量提高房价，淡季尽量提高出租率。
二、收益管理在酒店业的应用酒店的收益管理是对酒店经营的决策过程。
酒店将可卖房以合适的价格，在合适的时间，出售给合适的旅客是收益管理的关键。
酒店在开展收益管理时，以下几个方面应值得注意。
1.平衡供求关系时，注重价格而非成本酒店收益增加时，成本并不是成比例地增加。
因此，实行收益管理的酒店常用房价来应对市场的短期波动而较少地考虑其成本。
2.在制定价格时，以市场需求和客人愿意接受的价格为标准制定价格时，采取以需求为导向的差别定价法而不是以成本为导向的定价策略。
定价时主要考虑客人能接受的价格，遵循高需求高价格，低需求低价格的定价原则。
3.在销售时，要着眼于微观市场如果酒店销售房间时，不进行市场细分，只实行单一价格则酒店收益的损失比较大，而进行市场细分后，采用多级价格结构则酒店收益的损失就会大大减少。
4.把可卖房留给愿付最高价的客人传统的销售理念是“先到先得”，这会使有限的可卖房全部低价出售，减少了来自高端顾客的收益。
收益管理在酒店行业的应用-酒店管理论文-管理论文
收益管理在酒店行业的应用-酒店管理论文-管理论文——文章均为WORD文档，下载后可直接编辑使用亦可打印——［摘要］酒店行业并不属于新兴行业，随着市场竞争越来越激烈，传统的服务管理办法可能在酒店行业利润的赚取上不会再占优势，因此必须应运而生新兴的，更加适应酒店市场发展需求的服务与管理理念。
近些年来，收益管理成了酒店行业内较多提及的这样一种管理模式，逐渐被更多的业内人士所应用并引起重视。
本文主要是大概的分析一下收益管理这个理念在酒店行业中的应用以及随之出现的一些问题，共同分享，共同探讨。
［关键词］收益管理;避免资源虚耗;动态定价“收益管理”这个概念，由上个世纪70年代美国人提出，我国引入时间并不是很长。
收益管理主要是针对有存储限制的资产或者是对易耗品的收入管理，是一种为增加收入的定价优化策略。
这个概念自从提出已在诸多行业中已成功应用，并从中获益，逐步应用于服务产业。
其中有很多成功的案例，究其原因，都是因为科学的、合理的应用了收益管理的方法和系统，使得这些企业用最短的时间，创造最大的利益。
这就说明了，收益管理虽说是一门较新的技术，但只要掌握其核心理念，并将其服务管理系统科学应用于生产就是可以帮助企业实现利益最大化的。
应用法则是:需求定价替换成本定价，差别定价替换统一定价。
有价值，有潜力的顾客要做到为其保留产品。
酒店行业一般将收益管理进行如下定义:把酒店的服务或产品，在最恰当的时间，用最满意的价格，最畅通的销售渠道，给最有潜力的客人，以此来实现酒店收益的最大化。
通过“五个最”的组合，以市场预测、机动定价、优化价格、市场分析、控制容量以及超客房预订作为实施法则，尽可能地避免酒店资源虚耗，深度挖掘市场的盈利能力，使得酒店收益最大化目标最终实现。
关于收益管理，经常会有人误解为它只有在酒店客房满房的情况下才能发挥作用，其实不然。
在酒店行业中，收益管理服务系统科学合理应用，无论酒店满房与否，这门技术都可以发挥其作用，实现收益最大化。
酒店收益管理系统操作指南
酒店收益管理系统操作指南第一章概述 (4)1.1 系统简介 (4)1.2 功能特点 (4)1.2.1 数据集成 (4)1.2.2 价格管理 (4)1.2.3 房源管理 (4)1.2.4 预订管理 (4)1.2.5 客户关系管理 (4)1.2.6 报表统计 (4)1.2.7 系统集成 (4)1.2.8 安全保障 (4)1.2.9 用户权限管理 (5)1.2.10 系统维护与升级 (5)第二章系统登录与设置 (5)2.1 登录操作 (5)2.1.1 登录界面 (5)2.1.2 输入用户名与密码 (5)2.1.3 登录按钮 (5)2.1.4 登录成功与失败 (5)2.2 用户权限设置 (5)2.2.1 用户角色管理 (5)2.2.2 用户权限分配 (5)2.2.3 用户角色绑定 (5)2.2.4 用户权限变更 (6)2.3 系统参数配置 (6)2.3.1 系统参数概述 (6)2.3.2 参数配置界面 (6)2.3.3 配置参数 (6)2.3.4 参数生效与重启 (6)2.3.5 参数备份与恢复 (6)第三章预订管理 (6)3.1 预订查询 (6)3.1.1 查询条件设置 (6)3.1.2 查询结果展示 (6)3.1.3 查询结果导出 (7)3.2 预订操作 (7)3.2.1 预订新增 (7)3.2.2 预订确认 (7)3.2.3 预订变更 (7)3.3 预订修改与取消 (7)3.3.1 预订修改 (7)第四章房源管理 (7)4.1 房源信息录入 (7)4.1.1 登录系统 (7)4.1.2 选择房源管理模块 (7)4.1.3 录入房源信息 (7)4.2 房源状态管理 (8)4.2.1 查看房源状态 (8)4.2.2 修改房源状态 (8)4.3 房源调整与优化 (8)4.3.1 房源调整 (8)4.3.2 房源优化 (9)第五章价格管理 (9)5.1 价格策略设置 (9)5.1.1 策略制定 (9)5.1.2 策略实施 (9)5.2 价格调整与审批 (9)5.2.1 价格调整 (9)5.2.2 审批流程 (10)5.3 价格优化分析 (10)5.3.1 数据收集 (10)5.3.2 数据分析 (10)5.3.3 优化建议 (10)第六章收益分析 (10)6.1 收益报表查询 (10)6.1.1 功能概述 (10)6.1.2 操作步骤 (10)6.1.3 注意事项 (11)6.2 收益分析图表 (11)6.2.1 功能概述 (11)6.2.2 操作步骤 (11)6.2.3 注意事项 (11)6.3 收益预测与优化 (11)6.3.1 功能概述 (11)6.3.2 操作步骤 (11)6.3.3 注意事项 (12)第七章客户管理 (12)7.1 客户信息录入 (12)7.1.1 功能概述 (12)7.1.2 操作步骤 (12)7.1.3 注意事项 (12)7.2 客户关系维护 (12)7.2.1 功能概述 (12)7.2.2 操作步骤 (12)7.3 客户消费分析 (13)7.3.1 功能概述 (13)7.3.2 操作步骤 (13)7.3.3 注意事项 (13)第八章营销活动管理 (13)8.1 营销活动策划 (13)8.1.1 策划目的 (13)8.1.2 策划原则 (13)8.1.3 策划流程 (14)8.2 营销活动实施 (14)8.2.1 实施准备 (14)8.2.2 实施过程 (14)8.3 营销活动效果评估 (14)8.3.1 评估指标 (14)8.3.2 评估方法 (14)8.3.3 评估结果运用 (15)第九章数据报表 (15)9.1 日报表 (15)9.1.1 报表概述 (15)9.1.2 报表内容 (15)9.1.3 报表操作流程 (15)9.2 月报表 (15)9.2.1 报表概述 (15)9.2.2 报表内容 (16)9.2.3 报表操作流程 (16)9.3 年报表 (16)9.3.1 报表概述 (16)9.3.2 报表内容 (16)9.3.3 报表操作流程 (17)第十章系统维护与升级 (17)10.1 系统备份 (17)10.1.1 备份目的 (17)10.1.2 备份策略 (17)10.1.3 备份操作步骤 (17)10.2 系统升级 (17)10.2.1 升级目的 (17)10.2.2 升级策略 (17)10.2.3 升级操作步骤 (18)10.3 故障处理与售后服务 (18)10.3.1 故障处理 (18)10.3.2 售后服务 (18)第一章概述1.1 系统简介酒店收益管理系统是一款针对酒店行业特点研发的智能化管理软件，旨在帮助酒店提高收益、优化资源配置、提升客户满意度。
酒店收益管理外文文献翻译中英文2019
外文文献翻译原文及译文标题：酒店收益管理中动态客房分配的解决方法中英文2019文献出处：N. Aydin, S. I. Birbil[J]European Journal of Operational Research, Volume 271, Issue 116 , November 2018, Pages 179-192译文字数：4700 多字原文Decomposition methods for dynamic room allocation in hotel revenue managementN.Aydin，S.I.BirbilAbstractLong-term stays are quite common in the hotel business. Consequently, it is crucial for the hotel managements to consider the allocation of available rooms to a stream of customers requesting to stay multiple days. This requirement leads to the solving of dynamic network revenue management problems that are computationally challenging. A remedy is to apply decomposition approaches so that an approximate solution can be obtained by solving many simpler problems. In this study, we investigate several room allocation policies in hotel revenue management. We work on various decomposition methods to find reservation policies for advance bookings and stay-over customers. We also devise solution algorithms to solve the resulting problems efficiently.Keywords：Revenue management，Hotel，Capacity control，Decomposition methodsIntroductionHistorically, the airline industry played the steering role in revenue management (RM). Today, however, there is a wide range of applications in different industries with volatile demand, requesting fixed and perishable capacity (Kimes, 1989). Although the hotel industry is one of the typical application areas of revenue management, the research in this particular area lags behind the work produced for other service industries. In their recent work, Ivanov and Zhechev (2012) and Ivanov (2014) present a review of the methods proposed in the hotel RM literature and point out the gaps.In general, well-known airline RM techniques, such as booking control and pricing, can be applied to hotel RM problems. However, it is important to consider several constraints that are unique to hotel reservation systems. First, multi-day stays in hotels are quite common. While a flight itinerary includes, on average fewer than three legs, the number of nights a typical customer spends in a hotel can be a week oreven more (Zhang & Weatherford, 2017). Second, the demand process is different. Hotel customers may decide to stay longer and extend their reservation while they are staying in the hotel (Kimes, 1989). Third, airline customers generally make advance bookings but a number of hotel customers consist of walk-ins. Moreover, the early reservations in the booking interval are even allowed to cancel their bookings at no extra cost.In this paper, we focus on the room allocation decisions for a hotel. The optimal policy to accept or reject an arriving customer can be obtained by analyzing the stochastic nature of the customer arrival process. In hotel reservation systems, the customers are classified as the advance bookings, the stay-overs and the walk-ins. While the advance bookings make room reservations before they arrive at the hotel, the walk-ins show up without any reservation. The stay-overs are the customers who ask for an extension for their reservations during their stay in the hotel. Recently, hotel reservation systems have started offering extended stay as an option due to high customer demand (Tepper, 2015). For instance, Priceline (2017) and Hotwire (2017)present “add-a-night” and “add to your stay” options to their existing customers. The arrival process of the advance bookings and walk-ins are similar. The only difference is that the walk-in customers arrive after the reservation period ends. However, the stay-over requests depend on the accepted advance bookings. To simplify our notation, we ignore the walk-in customers and formulate our problem by considering the advance bookings and the stay-overs. Then, we explain how one can easily incorporate the walk-in customers to our proposed models. To the best of our knowledge, the dynamic model of stay-over customers in a network setting has not been previously studied in the literature.The research contributions in this paper come from the application and the analysis of two decomposition approaches. These are the day-based and the period- based decompositions. Our day-based decomposition is similar to the one proposed by Kunnumkal and Topaloglu (2010). We simplify their decomposition method and show that our proposed model provides a lower bound to their model. We set forth a dynamic model for the advance bookings and formulate a linear program for theproblem. The resulting model is then solved with the constraint generation method. We also propose alternate approximate models, which provide upper and lower bounds on the optimal expected revenue of the original model. To manage the stay- over requests, one needs to keep track of the number of reservations in each booking type. A day-based method, however, decomposes the network problem into independent days, and this decomposition approach causes loss of information on the number of customers in each booking type. Our solution to this hindrance is a period- based decomposition method, which is an extension of another approach recently proposed by Birbil, Frenk, Gromicho, and Zhang (2014). First, we focus on the single-day stay-over problem, as the request for an additional night is the most frequently realized stay-over case in real-life (Talya, 2016). Though our model is different than the one set forth by Birbil et al. (2014), we successfully build on their decomposition idea. Second, we consider the multi-day stay-over problem and present a two-period approximation, which combines the pair-based decomposition with the deterministic linear programming approach. In period one, we observe the reservation activity of the advance booking customers. In period two, we take into account the stay-over requests of the customers whose bookings have been accepted. To test the performances of the proposed decomposition approaches, we conduct simulation experiments and compare our results with those obtained by several well-known models from the literature. Our computational study indicates that the proposed decomposition approaches are apt to effective room allocation in hotel RM.Review of related literatureWe begin by reviewing the related work on hotel RM. Then, we summarize the decomposition approaches frequently applied to the network RM problems.Ladany (1976) works on a single-day stay model for a hotel with two types of resources. The aim of the model is to find an allocation policy to maximize the daily expected revenue. He develops a dynamic programming formulation and obtains the decision policy for each resource. Williams (1977) works on the single-day stay model during the peak demand period. In this model, he assumes that demand arrives from three different sources: the stay-overs, the reservations and the walk-ins. Hecomputes the reservation policy for each customer type by comparing the costs of underbooking and overbooking. Bitran and Leong (1989) focus on the multi-day problem by considering the walk-in and stay-over requests. They model the multi-day reservations as a series of independent, single-day reservations. Bitran and Mondschein (1995) develop a dynamic programming model for a single-day stay problem with multiple products. Since the resulting model is computationally intractable for the real size problems, they utilize several heuristics when searching for the optimal allocation policy. Weatherford (1995) focuses on the effect of the length of stay. He proposes a heuristic method based on a static model and compares this method with the other booking policies developed for the single-day stay problems. Bitran and Gilbert (1996) work on a single-day stay and single-room problem. They assume that during the service day, three types of customers show-up: the customers with guaranteed reservations, the customers with reservations and the walk-ins. They develop a dynamic model and propose a heuristic method to obtain the room allocation policy. Baker and Collier (1999) extend the study of Weatherford (1995) as well as the work of Bitran and Mondschein (1995) by allowing cancellations, overbooking and stay-overs. They develop two heuristics that integrate overbooking with the capacity allocation decisions. They compare the performances of these heuristics against the other booking control policies in the literature. Through this comparison, Baker and Collier (1999) discuss the advantages of each policy under different operating environments.Later studies focus on multi-product and multi-day stay problems. Chen (1998)presents a general formulation for a deterministic problem and discusses that it can be transferred to a network flow problem. Moreover, he shows that the optimal solution of the linear program is always integral. Goldman, Freling, Pak, and Piersma (2002) propose deterministic and stochastic linear programming models to find the nested booking limits and the bid prices for the multi-day stay problem. They follow the work of Weatherford (1995) to develop the deterministic model. For the stochastic model, they extend the work of De Boer, Freling, and Piersma (2002)on the airline revenue management problem. However, unlike the models proposed by Weatherford(1995) and De Boer et al. (2002), they use the booking control policies over a rolling horizon of decision periods. Lai and Ng (2005) work on a stochastic programming formulation for a multi-day stay problem. They apply robust optimization techniques to solve the problem on a scenario basis. They also consider the risk aversion of the decision maker and use the mean absolute value to measure the revenue deviation risk. Koide and Ishii (2005) work on the optimal room allocation policies for a single- day stay by considering the early discounts, the cancellations and the overbookings. They examine the properties of the expected revenue function and show that it is unimodal on the number of allocated rooms for early discount and overbooking. As with Lai and Ng (2005), Liu, Lai, and Wang (2008) present revenue optimization models for a multi-day stay problem by considering the revenue risk. They propose a stochastic programming model with semi-absolute deviations to measure the risk. Guadix, Cortes, Onieva, and Munuzuri (2010) present a decision support system for forecasting and room allocation decisions. They work on the deterministic and stochastic programming models by considering group arrivals. The proposed decision support system integrates these models for room allocation and pricing decisions. Nadarajah, Lim, and Ding (2015) study dynamic pricing policy for a single type of room by considering the multiple day stays. Since the resulting model is computationally intractable, they propose pricing heuristics based on fluid approximation and approximate linear programming. They analyze the properties of the pricing policy under the peak demand.The solution approaches considered in this study build on the literature on decomposition methods in network revenue management. The output of a decomposition method is used to construct various capacity controls, such as bid- prices and nested booking limits. Adelman (2007) develops an approximation method to compute the dynamic bid prices. He first formulates the network problem as a dynamic model, which suffers from the curse of dimensionality. Thus, he derives a standard linear program by approximating the dynamic programming value functions. This approach provides an upper bound on the optimal expected revenue. Zhang (2011) proposes a nonlinear, non-separable approximation to the dynamicprogramming model that leads to a tighter upper bound. Topaloglu (2009)focuses on a Lagrangian relaxation method to decompose the network problem into many single capacity problems. Erdelyi and Topaloglu (2009) work on the overbooking problem in an airline network and develop separable approximations to decompose the problem by individual flights (legs). This approach constructs capacity dependent bid prices. However, it becomes quite difficult to compute the value functions for each leg as the size of the problem increases. To reduce the computational burden, Kunnumkal and Topaloglu (2011) develop a stochastic approximation algorithm that provides a set of capacity independent bid prices. In this approach, they formulate the total expected profit as a function of the bid prices and use stochastic gradients to obtain a good bid price policy. Recently, Kunnumkal and Topaloglu (2010) propose a new leg- based decomposition method for the airline revenue management problems that involve the customer choices. In this method, they first allocate the revenue of each itinerary among the legs covered by the itinerary. Then, they define a penalty term to incorporate the network effect. They view the revenue allocations and the penalty terms as decision variables, and use subgradient search to find the optimal solution. Although this solution approach is manageable in small size networks, it can be impractical for the problems of substantial size networks. Hotel network revenue management problems are also tackled with the decomposition methods. Zhang and Weatherford (2017) work on a dynamic pricing problem. They generalize the approximation method of Zhang (2011) and decompose the problem into independent single-day problems by approximating the value functions with nonlinear non- separable functions. They test the proposed approach by using the data from a hotel. Aslani et al. (2013) also propose a decomposition method for a pricing problem in hotel revenue management. They develop an approach to estimate the effective arrival rate for each day by considering the stock-outs and the customer losses due to high price levels. They decompose the network problem into single- day subproblems by using these daily arrival rates. Our study has several distinguishing features compared with the earlier work. To begin with, we focus on the multiple day problem and propose several decomposition methods to attack theproblem. In particular, our aim is to find a dynamic capacity allocation policy that takes into account the advance bookings and the stay-over customers. We first study advance bookings and propose day-based decomposition methods. We work on a fare-allocation strategy where the reservation fares are allocated on day basis depending on the time of the booking. Our method is based on dynamic programming formulations for the single-day revenue management problems, hence it can capture the temporal dynamics of the reservation requests more accurately compared with the static models. We also present alternate solution methods to improve the computational time for the large-scale problems. Later, we study stay-over requests in hotel RM and propose a pair-based dynamic programming method. To the best of our knowledge, the dynamic model of stay-over customers in a network setting has not previously been studied in the literature. We also discuss the applicability of the proposed models to several cases, such as late checkout and overbooking. Finally, our computational experiments demonstrate that the proposed methods can generate significantly higher profits than the well-known benchmarks in the literature. The performance gaps are especially significant when the daily hotel capacity is tight and the stay-over probability is high. In addition, day-based decomposition methods perform significantly better when the hotel controls the fares on a per-day basis and does not offer discount for long-term stays.ConclusionIn this study, we work on the dynamic room allocation problem in hotel revenue management. Due to the complexity of this problem, we concentrate on several approximation methods. We analyze the structural properties of the problem and present day- and pair-based decomposition approaches that can handle the walk-in and the stay-over customers. First, we work on the day-based decomposition methods. Day-based decomposition generates independent subproblems for each day and, hence, it cannot store the number of reserved rooms for each product. Therefore, incorporating the stay-over customers becomes a challenge. In the second part, we work on the stay-over extension. To the best of our knowledge, the dynamic programming model that includes the stay-over customers has not been proposed inthe literature before. We first focus on the single-day stay-over problem. By extending the work of Birbil et al. (2014), we propose a solution method. Second, we consider the multi-day stay-over problem and present a two-period approximation, which combines the pair-based decomposition with the deterministic linear programming. We conduct a thorough computational study and investigate the performances of our proposed models along with some well-known approaches used in the literature. Our computational experiments indicate that the proposed policies perform well. The performance gaps are especially significant when the hotel’s daily capacity is tight and the stay-over probability is high.As we mentioned in Section 5.2, our stay-over models can be extended to several other applications in hotel RM. Recently, hotel reservation systems have started to offer late checkout option to their customers. Late checkout requests can be considered as a special case of stay-over problem where the customers can extend their stay until the allowed time specified by the hotel. Following the same construction as for the stay-over model, we can obtain the reservation policies for late checkouts. Another important issue in hotel revenue management is overbooking. Similarly, the overbooking option can be incorporated in the multi-day stay-over model and it can also be solved in two stages. However, it is important to note that preallocating the hotel capacity to even more pairs and determining the individual overbooking limit for each pair may poorly affect the control of hotel capacity network-wide. Incorporation of the overbooking option is a potential topic for future research.译文酒店收益管理中动态客房分配的解决方法摘要长期住宿在酒店行业中很常见。
酒店收益管理外文翻译中英文2019
酒店收益管理中动态客房分配的解决方法中英文2019原文Decomposition methods for dynamic room allocation in hotel revenue managementN.Aydin，S.I.BirbilAbstractLong-term stays are quite common in the hotel business. Consequently, it is crucial for the hotel managements to consider the allocation of available rooms to a stream of customers requesting to stay multiple days. This requirement leads to the solving of dynamic network revenue management problems that are computationally challenging. A remedy is to apply decomposition approaches so that an approximate solution can be obtained by solving many simpler problems. In this study, we investigate several room allocation policies in hotel revenue management. We work on various decomposition methods to find reservation policies for advance bookings and stay-over customers. We also devise solution algorithms to solve the resulting problems efficiently.Keywords：Revenue management，Hotel，Capacity control，Decomposition methodsIntroductionHistorically, the airline industry played the steering role in revenue management (RM). Today, however, there is a wide range of applications in different industries with volatile demand, requesting fixed and perishable capacity (Kimes, 1989). Although the hotel industry is one of the typical application areas of revenue management, the research in this particular area lags behind the work produced for other service industries. In their recent work, Ivanov and Zhechev (2012) and Ivanov (2014) present a review of the methods proposed in the hotel RM literature and point out the gaps.In general, well-known airline RM techniques, such as booking control and pricing, can be applied to hotel RM problems. However, it is important to consider several constraints that are unique to hotel reservation systems. First, multi-day staysin hotels are quite common. While a flight itinerary includes, on average fewer than three legs, the number of nights a typical customer spends in a hotel can be a week or even more (Zhang & Weatherford, 2017). Second, the demand process is different. Hotel customers may decide to stay longer and extend their reservation while they are staying in the hotel (Kimes, 1989). Third, airline customers generally make advance bookings but a number of hotel customers consist of walk-ins. Moreover, the early reservations in the booking interval are even allowed to cancel their bookings at no extra cost.In this paper, we focus on the room allocation decisions for a hotel. The optimal policy to accept or reject an arriving customer can be obtained by analyzing the stochastic nature of the customer arrival process. In hotel reservation systems, the customers are classified as the advance bookings, the stay-overs and the walk-ins. While the advance bookings make room reservations before they arrive at the hotel, the walk-ins show up without any reservation. The stay-overs are the customers who ask for an extension for their reservations during their stay in the hotel. Recently, hotel reservation systems have started offering extended stay as an option due to high customer demand (Tepper, 2015). For instance, Priceline (2017) and Hotwire (2017)present “add-a-night” and “add to your stay” options to their existing customers. The arrival process of the advance bookings and walk-ins are similar. The only difference is that the walk-in customers arrive after the reservation period ends. However, the stay-over requests depend on the accepted advance bookings. To simplify our notation, we ignore the walk-in customers and formulate our problem by considering the advance bookings and the stay-overs. Then, we explain how one can easily incorporate the walk-in customers to our proposed models. To the best of our knowledge, the dynamic model of stay-over customers in a network setting has not been previously studied in the literature.The research contributions in this paper come from the application and the analysis of two decomposition approaches. These are the day-based and the period-based decompositions. Our day-based decomposition is similar to the one proposed by Kunnumkal and Topaloglu (2010). We simplify their decompositionmethod and show that our proposed model provides a lower bound to their model. We set forth a dynamic model for the advance bookings and formulate a linear program for the problem. The resulting model is then solved with the constraint generation method. We also propose alternate approximate models, which provide upper and lower bounds on the optimal expected revenue of the original model. To manage the stay-over requests, one needs to keep track of the number of reservations in each booking type. A day-based method, however, decomposes the network problem into independent days, and this decomposition approach causes loss of information on the number of customers in each booking type. Our solution to this hindrance is a period-based decomposition method, which is an extension of another approach recently proposed by Birbil, Frenk, Gromicho, and Zhang (2014). First, we focus on the single-day stay-over problem, as the request for an additional night is the most frequently realized stay-over case in real-life (Talya, 2016). Though our model is different than the one set forth by Birbil et al. (2014), we successfully build on their decomposition idea. Second, we consider the multi-day stay-over problem and present a two-period approximation, which combines the pair-based decomposition with the deterministic linear programming approach. In period one, we observe the reservation activity of the advance booking customers. In period two, we take into account the stay-over requests of the customers whose bookings have been accepted. To test the performances of the proposed decomposition approaches, we conduct simulation experiments and compare our results with those obtained by several well-known models from the literature. Our computational study indicates that the proposed decomposition approaches are apt to effective room allocation in hotel RM.Review of related literatureWe begin by reviewing the related work on hotel RM. Then, we summarize the decomposition approaches frequently applied to the network RM problems.Ladany (1976) works on a single-day stay model for a hotel with two types of resources. The aim of the model is to find an allocation policy to maximize the daily expected revenue. He develops a dynamic programming formulation and obtains the decision policy for each resource. Williams (1977) works on the single-day staymodel during the peak demand period. In this model, he assumes that demand arrives from three different sources: the stay-overs, the reservations and the walk-ins. He computes the reservation policy for each customer type by comparing the costs of underbooking and overbooking. Bitran and Leong (1989) focus on the multi-day problem by considering the walk-in and stay-over requests. They model the multi-day reservations as a series of independent, single-day reservations. Bitran and Mondschein (1995) develop a dynamic programming model for a single-day stay problem with multiple products. Since the resulting model is computationally intractable for the real size problems, they utilize several heuristics when searching for the optimal allocation policy. Weatherford (1995) focuses on the effect of the length of stay. He proposes a heuristic method based on a static model and compares this method with the other booking policies developed for the single-day stay problems. Bitran and Gilbert (1996) work on a single-day stay and single-room problem. They assume that during the service day, three types of customers show-up: the customers with guaranteed reservations, the customers with reservations and the walk-ins. They develop a dynamic model and propose a heuristic method to obtain the room allocation policy. Baker and Collier (1999) extend the study of Weatherford (1995) as well as the work of Bitran and Mondschein (1995) by allowing cancellations, overbooking and stay-overs. They develop two heuristics that integrate overbooking with the capacity allocation decisions. They compare the performances of these heuristics against the other booking control policies in the literature. Through this comparison, Baker and Collier (1999) discuss the advantages of each policy under different operating environments.Later studies focus on multi-product and multi-day stay problems. Chen (1998)presents a general formulation for a deterministic problem and discusses that it can be transferred to a network flow problem. Moreover, he shows that the optimal solution of the linear program is always integral. Goldman, Freling, Pak, and Piersma (2002) propose deterministic and stochastic linear programming models to find the nested booking limits and the bid prices for the multi-day stay problem. They follow the work of Weatherford (1995) to develop the deterministic model. For the stochasticmodel, they extend the work of De Boer, Freling, and Piersma (2002)on the airline revenue management problem. However, unlike the models proposed by Weatherford (1995) and De Boer et al. (2002), they use the booking control policies over a rolling horizon of decision periods. Lai and Ng (2005) work on a stochastic programming formulation for a multi-day stay problem. They apply robust optimization techniques to solve the problem on a scenario basis. They also consider the risk aversion of the decision maker and use the mean absolute value to measure the revenue deviation risk. Koide and Ishii (2005) work on the optimal room allocation policies for a single-day stay by considering the early discounts, the cancellations and the overbookings. They examine the properties of the expected revenue function and show that it is unimodal on the number of allocated rooms for early discount and overbooking. As with Lai and Ng (2005), Liu, Lai, and Wang (2008) present revenue optimization models for a multi-day stay problem by considering the revenue risk. They propose a stochastic programming model with semi-absolute deviations to measure the risk. Guadix, Cortes, Onieva, and Munuzuri (2010) present a decision support system for forecasting and room allocation decisions. They work on the deterministic and stochastic programming models by considering group arrivals. The proposed decision support system integrates these models for room allocation and pricing decisions. Nadarajah, Lim, and Ding (2015) study dynamic pricing policy for a single type of room by considering the multiple day stays. Since the resulting model is computationally intractable, they propose pricing heuristics based on fluid approximation and approximate linear programming. They analyze the properties of the pricing policy under the peak demand.The solution approaches considered in this study build on the literature on decomposition methods in network revenue management. The output of a decomposition method is used to construct various capacity controls, such as bid-prices and nested booking limits. Adelman (2007) develops an approximation method to compute the dynamic bid prices. He first formulates the network problem as a dynamic model, which suffers from the curse of dimensionality. Thus, he derives a standard linear program by approximating the dynamic programming valuefunctions. This approach provides an upper bound on the optimal expected revenue. Zhang (2011) proposes a nonlinear, non-separable approximation to the dynamic programming model that leads to a tighter upper bound. Topaloglu (2009)focuses on a Lagrangian relaxation method to decompose the network problem into many single capacity problems. Erdelyi and Topaloglu (2009) work on the overbooking problem in an airline network and develop separable approximations to decompose the problem by individual flights (legs). This approach constructs capacity dependent bid prices. However, it becomes quite difficult to compute the value functions for each leg as the size of the problem increases. To reduce the computational burden, Kunnumkal and Topaloglu (2011) develop a stochastic approximation algorithm that provides a set of capacity independent bid prices. In this approach, they formulate the total expected profit as a function of the bid prices and use stochastic gradients to obtain a good bid price policy. Recently, Kunnumkal and Topaloglu (2010) propose a new leg-based decomposition method for the airline revenue management problems that involve the customer choices. In this method, they first allocate the revenue of each itinerary among the legs covered by the itinerary. Then, they define a penalty term to incorporate the network effect. They view the revenue allocations and the penalty terms as decision variables, and use subgradient search to find the optimal solution. Although this solution approach is manageable in small size networks, it can be impractical for the problems of substantial size networks. Hotel network revenue management problems are also tackled with the decomposition methods. Zhang and Weatherford (2017) work on a dynamic pricing problem. They generalize the approximation method of Zhang (2011) and decompose the problem into independent single-day problems by approximating the value functions with nonlinear non-separable functions. They test the proposed approach by using the data from a hotel. Aslani et al. (2013) also propose a decomposition method for a pricing problem in hotel revenue management. They develop an approach to estimate the effective arrival rate for each day by considering the stock-outs and the customer losses due to high price levels. They decompose the network problem into single-day subproblems by using thesedaily arrival rates. Our study has several distinguishing features compared with the earlier work. To begin with, we focus on the multiple day problem and propose several decomposition methods to attack the problem. In particular, our aim is to find a dynamic capacity allocation policy that takes into account the advance bookings and the stay-over customers. We first study advance bookings and propose day-based decomposition methods. We work on a fare-allocation strategy where the reservation fares are allocated on day basis depending on the time of the booking. Our method is based on dynamic programming formulations for the single-day revenue management problems, hence it can capture the temporal dynamics of the reservation requests more accurately compared with the static models. We also present alternate solution methods to improve the computational time for the large-scale problems. Later, we study stay-over requests in hotel RM and propose a pair-based dynamic programming method. To the best of our knowledge, the dynamic model of stay-over customers in a network setting has not previously been studied in the literature. We also discuss the applicability of the proposed models to several cases, such as late checkout and overbooking. Finally, our computational experiments demonstrate that the proposed methods can generate significantly higher profits than the well-known benchmarks in the literature. The performance gaps are especially significant when the daily hotel capacity is tight and the stay-over probability is high. In addition, day-based decomposition methods perform significantly better when the hotel controls the fares on a per-day basis and does not offer discount for long-term stays.ConclusionIn this study, we work on the dynamic room allocation problem in hotel revenue management. Due to the complexity of this problem, we concentrate on several approximation methods. We analyze the structural properties of the problem and present day- and pair-based decomposition approaches that can handle the walk-in and the stay-over customers. First, we work on the day-based decomposition methods. Day-based decomposition generates independent subproblems for each day and, hence, it cannot store the number of reserved rooms for each product. Therefore, incorporating the stay-over customers becomes a challenge. In the second part, wework on the stay-over extension. To the best of our knowledge, the dynamic programming model that includes the stay-over customers has not been proposed in the literature before. We first focus on the single-day stay-over problem. By extending the work of Birbil et al. (2014), we propose a solution method. Second, we consider the multi-day stay-over problem and present a two-period approximation, which combines the pair-based decomposition with the deterministic linear programming. We conduct a thorough computational study and investigate the performances of our proposed models along with some well-known approaches used in the literature. Our computational experiments indicate that the proposed policies perform well. The performance gaps are especially significant when the hotel’s daily capacity is t ight and the stay-over probability is high.As we mentioned in Section 5.2, our stay-over models can be extended to several other applications in hotel RM. Recently, hotel reservation systems have started to offer late checkout option to their customers. Late checkout requests can be considered as a special case of stay-over problem where the customers can extend their stay until the allowed time specified by the hotel. Following the same construction as for the stay-over model, we can obtain the reservation policies for late checkouts. Another important issue in hotel revenue management is overbooking. Similarly, the overbooking option can be incorporated in the multi-day stay-over model and it can also be solved in two stages. However, it is important to note that preallocating the hotel capacity to even more pairs and determining the individual overbooking limit for each pair may poorly affect the control of hotel capacity network-wide. Incorporation of the overbooking option is a potential topic for future research.译文酒店收益管理中动态客房分配的解决方法摘要长期住宿在酒店行业中很常见。
酒店业收益管理(1)
要点：1.什么是收益管理；2.举例说明收益管理；3.实行收益管理的前提；4.饭店业收益管理入门。
1.什么是收益管理《华尔街日报》曾多次报导有关收益管理系统在多行业成功应用的文章，并将它誉之为21世纪最重要的和投资回报率最高的边缘产业之一。
收益管理系统首先是由美洲航空公司开发的，美洲航空公司仅由于使用收益管理系统1997年增加的额外收益就达10亿美元。
饭店业最先开发使用收益管理系统的是万豪，开始于把周末房价降至平时一半的优惠来吸引当地的顾客到旅馆度周末，万豪的董事长兼首席执行官比尔-玛丽奥特曾说：“收益管理不仅为我们增加了数百万美元的收益，同时也教育了我们如何更有效地管理。
”希尔顿、凯悦、喜达屋等饭店集团先后开发了各自的收益管理系统后，凯悦摄政俱乐部客房的预订率上升了20%，希尔顿创造了空前收入的记录，凯悦和希尔顿都声称销售和预订之间的沟通有了显著的加强。
收益管理系统(Revenue Management System)是根据收益管理原理设计开发的一种计算机辅助决策管理系统。
收益管理是指导企业在合适的时间、以合适的价格、把合适的产品卖给合适的顾客的科学管理方法。
大家都比较熟悉航空公司的变动价格制度,并且接受这样的变动价格,利用同样原理,将变动价格原理推行到酒店行业,顾客同样是接受的。
酒店集团和在线预订公司对多天住宿顾客的预订都采用收益管理方法报价，收益管理是保证酒店收益最大化和保证顾客满意指数最大化的一个平衡杠杆。
2.举例说明收益管理以一名顾客预订酒店3天的住宿为例,按照酒店没有实行收益管理的前提,酒店会给出3个晚上一个同样的平均房价；如果酒店实行了收益管理的方法,会根据3天不同的房源情况,每天给出不同的房价。
顾客对于酒店的房价往往存在一个期望和现实的差异，按照传统办法，酒店平均1200元的销售价,3天合计房价为3600元；酒店根据最合适的可售房价的原理,前两天因为客房宽裕房价为1 000元,第3天因客房紧张房价为1500元,3天合计房价为3500元。
浅谈酒店收益管理方法.txt
三、结语 Байду номын сангаас
收益管理通过对客源市场的细分，对消费者行为模式的分析，对市场供求关系变化的预测，不断优化产品和服务，最大限度地提高产品和服务的销售总量以及单位销售价格，以获取最大收益。这种保持固定成本投入不变而创收的过程本身就是直接创收的过程。然收益创收的直接性并不意味着收益管理本身的简单化。收益管理是与运筹学、经济学、统计学等学科密切相关的复合型学科。高收益的实现不仅仅需要“四个合适”的统一协调作业，更需要收益管理系统的辅助和高素质专业人才的管理辅助。另外，酒店产品的多元使得酒店收益管理的发展方向将不仅仅局限于独立的客房和餐饮销售，而是更有可能在客房和餐饮的“捆绑”式经营销售上得到广泛应用。
二、收益管理的应用
作为一项动态化的管理过程，收益管理的直接创收并不局限于大而化之的创收层面，可以将之分解细化为独个的单元进行解刨分析。人们可以从Kimes给出的“四个合适”概念出发进行细分，当然，这“四个合适”彼此之间并没有明确界限，而是相互作用于收益管理这一大系统。人们在此基础上可以最大限度地细分顾客、价格、销售渠道，以达到缩小市场空白区、获取最大收益的目的。（一）合适的顾客。对酒店而言，顾客可较明确地被分类，且分类的标准并不单一，出行目的、预定渠道、消费特征等都可作为分类标准。而分类的目的无非就是对不同类型的顾客采取不同的价格，以极大限度地满足顾客需求，从而获取最高收益。顾客的细分实际上就是市场的细分，市场的细分主要有两种思路，一种是单一市场的细分，如表1所列的国际饭店集团通用的一般市场细分方式；其二就是市场的细分组合，这也是大多数酒店目前所采取的市场细分方法，即以每一细分市场的客源量和收益额为标准，确定各细分的市场的比例，以实现每房收益的最大额。（二）合适的价格。价格类别以顾客类别为基础，这里列举最普遍存在的4种顾客类型：零售散客、公司协议散客、会议团体和旅游团体。对于零售散客来讲，其价格的确定需考虑以下要素：价格背后的所包含的产品和服务有哪些，这些产品和服务的质量怎样，同水平竞争对手的给价如何，需求到底有多旺盛。对于公司协议散客来讲，定价的考虑因素则包括：与零售散客卖价的差价为多少才合适，该类顾客是否会创造餐饮、会议等综合贡献消费，淡旺季的分布量是什么，高给价公司和低给价公司的比例该如何协调。而会议团体与旅游团体整体定价考虑因素大同小异。只是在上述两类顾客定价影响因素的基础上，还需考虑某一时间段（比如一年内）的占房率和替代收入的情况。价格不是一成不变的，基于客观市场环境的变动，相关操作主体应对价格进行较短间隔期的动态调整，本着恰当定价比和最高收益额相符的原则进行调整，其实也就是前文所提到的边际成本与边际收益的调节问题。（三）合适的时间。季节性问题分为两种，一种源于自然时间的变动，比如，夏天的青岛，游客数量显然多于其他季节，那在该时节的酒店客房需求量也必然较大；另一种是出于非自然时间的异同，比如，某一时间重大会议或者重大赛事等的举行。旺季多销，淡季薄销；旺季提价，淡季降价，时间性是收益管理最需要考虑的因素。（四）合适的地点。传统意义上的合适地点是指酒店的区位选择，而区位选择本身又涉及到区位所在地土地的成本问题，地租、客流量乃至政策导向，涉及面广而巨，因此，不将区位的选择纳入收益管理的考虑范围，而是将地点选择引申义为渠道的选择。目前来看，酒店行业所涉及到的销售渠道主要是OTA销售（如携程、艺龙等），但OTA销售会涉及到佣金问题，由于涉及多方利益相关者，因此，具体数值分析此处不作展开。此外，旅行分销商、团购、微店、营销公众号和打包促销等都是酒店产品的销售渠道。选择不同销售渠道究其根本是价格的平衡性问题，所以需要经由成本比较，选择出能实现最大销售量的单一销售渠道或渠道组合。
酒店的收益管理
收益管理目录收益管理概述原理收益管理前提和工作内容收益管理在实践中需注意的问题展开收益管理概述收益管理(Revenue Management 或Yield Management)是一种谋求收入最大化的新经营管理技术。
它诞生于上世纪八十年代，最早由民航开发。
收益管理,又称产出管理、价格弹性管理；亦称“效益管理”或“实时定价”,它主要通过建立实时预测模型和对以市场细分为基础的需求行为分析，确定最佳的销售或服务价格。
其核心是价格细分亦称价格歧视(price discrimination)，就是根据客户不同的需求特征和价格弹性向客户执行不同的价格标准。
这种价格细分采用了一种客户划分标准，这些标准是一些合理的原则和限制性条件。
这种划分标准的重要作用在于:通过价格藩篱将那些愿意并且能够消费得起的客户和为了使价格低一点而愿意改变自己消费方式的客户区分开，最大限度地开发市场潜在需求，提高效益。
收益管理是一种用来增加收入的方法，即根据市场供需和竞争程度的动态变化去制定价格，从而达到增加收入的目的。
从根本上讲，当需求强劲并趋向大于供给时，则应提高价格。
反之，当需求趋于下降，从而供给超过需求时，则应降低价格。
其目的是在给定供需状况下，力争实现最大收益。
收益测定方法将实际实现的收入同理论上的潜在总收入进行比较。
对于潜在总收入的解释及其计算方法，各饭店或各饭店连锁公司往往多有不同。
我们可以根据提前一小时、一天、一周乃至数月或数年确定的需求动向，去应用收益管理的原理。
例如，对于未来需求预计会变得强劲的时期，我们可以调高价格。
在需求疲弱时期，我们则可以调低价格，以便通过占有市场份额去争取客房收入。
收益管理所关注的不是测定出租率或房价，而是以每间客房的收入为焦点，如同在汽车租赁业中是以每租出一辆车的所得收入为焦点或者在航空运输业中是以每一乘客所带来的收入为关注焦点一样。
虽然“收益管理”这一术语源于航空运输业，但饭店和度假地对收益管理原理的应用远远早于航空公司。
关于收益管理在酒店业里的应用研究
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一．绪论
在信息产业技术革命大潮和航空运输业发展需求的共同推动下，收益管理 理论和实践取得了大量成果，通过逐步深化运用，国外航空运输业和酒店业获 得了丰厚利润。国内航空公司和酒店业虽然也对收益管理逐步重视，但多停留 在理论，实践应用不多，效果有限。如何让收益管理更贴近中国民航的实际需 要，将收益管理的先进理念和方法贯彻到公司经营活动的各个领域，是这些公 司目前急切要解决的一个问题。 国内少数航空和酒店公司开始使用国外收益管理系统，绝大部分公司没有 建立系统，收益管理还处在相对初级、原始的状态，绝大多数具体工作还是通 过人工进行处理和调整，虽然也在运用收益管理的某些手段，但还无法达到依 靠系统数据辅助经营管理，量化经营决策。管理的准确性、及时性、全面性不 足。未建立起收益管理系统的主要原因在于缺乏有效的工具和及时稳定的数据 来源。不过，随着我国信息服务水平的逐步提升，一些国内公司已经准备建立 收益管理系统，将现行的经验管理改进为建立在历史数据基础上的自动处理为 主、人为修正为辅的管理模式。1
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李文丽，陆亚刚.收益管理在酒店业的应用策略. 《社会科学家》.2006,(11):140
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25％，税前利润上升 250％，从 30 亿升为 75 亿，增加的 45 亿中，有 30 亿来自 于应用了收益管理方法和系统。1 收益管理是相对较新的技术，从它首次被提出至今也就 20 年左右。目前它 已经成为服务产业广泛应用的一项创新。 收益管理广泛适用于，航空公司、酒店、旅游度假地等不可储存资产的管 理，以及易腐商品的管理的场合。甚至广播公司也运用收益管理技术，管理其 广告时段，以决定有多大比例预留给高端客户，多大比例留给散户。对于制造 能力不可储存的情况，制造商也可以运用收益管理技术对其进行管理。 航空公司在 20 世纪 70 年代末解禁之后首次实行收益管理。它们对一些时 段的价格进行了限制，潜力在乘客要么按所给价格预订机票，那么选择其他的 交通工具。这种大胆的市场政策碰到了一些问题，但是却建立了飞机票价的经 济结构。
基于经济学基础的酒店收益管理研究
基于经济学基础的酒店收益管理研究酒店收益管理（Revenue Management）在全球范围内已经成为了酒店运营中的重要环节，其目的在于通过对消费者的行为及市场需求的分析，提高酒店的收益。
酒店收益管理是一种以应用经济学原理为基础的策略，涉及到房价定价、销售渠道管理、应用优惠券等众多方面。
本文将基于经济学基础对酒店收益管理进行深入地研究。
一、酒店收益管理的起源在20世纪60年代初，航空公司首先开始实行收益管理技术，通过优化机务表现、航线规划和房间出售时间的营销策划，来实现营业收入最大化。
这些技术逐渐被酒店、汽车租赁行业和其他服务业采用。
时间下加上网络技术的突飞猛进，整个市场的形态发生了变化。
酒店业在20世纪80年代也引入了收益管理理念，但是开始步履蹒跚。
90年代則是大潮的时代，许多领先转型的集团纷纷参与到饮食服务业中。
这些公司开始使用技术工具，以优化价格和投影入住量，最终确定利润最大化策略。
酒店业如今是收益管理的得益者之一，其技术能力和工具的基础学科开始迅速发展。
二、酒店收益管理的基础——需求曲线理论理解需求曲线是酒店收益管理的关键。
需求曲线是指一组数字表示特定服务或产品的需求量随着价格的变化而产生的相应变化。
这个理论的背后是消费者购买商品或服务的决策过程，因此需求曲线是实现酒店收益管理的核心构架之一。
Rajan和Chandy分别在2009和2011年的文章中提出了有关需求曲线主要构造元素的两个扩展理论。
第一个元素是价格对客户的消费利益的度量，这意味着酒店管理者需要对客户所需的与价格相关的价值偏好进行了解；第二个元素是市场需求数据的预测，主要包括酒店客房销售时间和价格。
通过这些扩展，酒店收益管理者可以更准确地描绘需求曲线，并确定相关的房价和市场运营策略。
三、酒店收益管理的策略——动态定价酒店收益管理的一个最关键的策略就是动态定价（Dynamic Pricing）。
这是指有时候酒店在不同的渠道上向同一客户提供不同的房价。
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为酒店群体顾客而设的技术收益管理系统Pablo Cortés, Luis Onieva, Jesús Muñuzuri 西班牙塞维利亚大学（作者承认由Ministerio de Educación y Ciencia，Spain.所给的财务支持）文章信息文章历史：2008年4月1日投稿2009年3月1日收到修订版2009年4月1日接受发表关键词：收益管理顾客群体酒店摘要这篇文章讨论了收益管理：一种关注在决策环节使每个销售单位利润最大化的技术。
新生的技术管理在收益管理的技术发展中承担着一个主要的角色。
在技术管理方面，每个新的改进能带来更加有效的商业收益能力。
现今，决策支持的收益管理系统和技术管理是在服务性行业取得成功的重要因素。
在此文章中例举了酒店的群客群体的一些特殊案例。
也介绍了一种能为酒店设定收益最大化标准的新的决策支持系统。
上述的系统包括了一系列的顾客需求预测方法，同时讨论了单个顾客以及顾客群体的一般情况。
收益管理系统也包含了确定性以及猜测性的数学模型来采取最优决策。
实际上的收益取决于酒店采用的预约系统。
收益管理系统运用一个仿真工具做了一个不同的房间库存管理比较，结果包含了性能指标，例如入住率、实用率以及销售量；通过比较结果，选择其中一个方法。
通过实际数据的测试，收益管理系统证明了自身的适用性，从而成为一个创新和有效的酒店预订系统管理工具。
©2009 Elsevier Inc。
版权所有1.介绍在不适合自身的情况下，许多公司仍然试图利用收益管理技术。
由于公司想运用收益管理技术从越来越多的业务流程中获取利润，研究人员对这些需求作出了回应。
在过去，不同行业最常运用以这种技术为基础的特点。
脆弱的产业，比如面包师、杂货商、水果供应商和剧院经理，通过在不同的时间段设定不同的价格限定了顾客需求。
跟随着美国航空公司在1978年违反规定的行为，现今各个航空公司能够采用它们自身选择的任意航线任意时间的任意价格，Smith, Leimkuhler 和Darrow (1992)。
这导致科学家们研发了一种称为“收益管理”的新的管理方法。
在最初，收益管理假设乘客们都选择一个特定的航班价格，并且没有选择一个能够得到的更低价格。
在不放弃现有的从全票顾客赚取的利润基础上，公司都采用不同的价格来争夺对价格敏感的顾客。
后来这些技术发拓展允许不同级别票价需求弹性的存在。
Bodily 和Weatherford (1995)也考虑了过度预订的情况，以及提出了允许乘客进行调整的建议。
Belobaba 和Weatherford (1996)完成了一项不同决策制定合并乘客调整规则的比较。
通过这样，他们把收益管理定义为把正确的单位货品在正确的时间销售给正确的顾客。
他们集中研究使用这类系统来决定可获得的不同价格的房间数量的酒店的收益管理方面。
Rothsein (1971)进行了酒店预订系统过量预订的最早应用。
Liberman 和Yechiali (1978)考虑了顾客在24小时之内的取消预订。
Orkin (1988)为酒店提出了一些收益管理之外的意见并提供了不同类型计算的例子。
Bitran 和Mondschein (1995)设计出酒店预订模型，其中包括了停留天数。
Bitran 和Gilbert（1996）拓宽了先前的模型并合并加入不确定顾客。
当满足以下的5个条件时，收益管理适用于服务性行业，特别是酒店业。
1.有限的容量。
收益管理的设计把容量有限的公司作业目标。
每一个酒店单位在一段时期在确定的容量基础上，用售出的房间数量来衡量。
2.市场分割。
服务性行业把市场细分，这样能够选择不同类型的顾客。
服务性行业不允许任意定价，所以服务有不同的区别特色，并且通过同样的单位来传达不同类型的服务。
酒店通常使用限制购买和退款要求的方法来细分享受性和商务性顾客。
3.未来需求不确定。
收益管理应该具有预测未来需求变化的能力，这样能使管理者在高需求时期内提高价格，在低需求时期内降低价格。
在知道将有多少商务房间会售出之前，酒店必须为商务顾客留出房间，保护他们免受更低价的正常房间影响。
4.脆弱的产品。
产品的性质区别服务业与制造业。
在服务业，未售出的产品在特定时间后便会被浪费，因为服务是不能储存的。
这个特点导致了服务业倾向于提前出售。
酒店不能储存今天的房间为明天的顾客服务。
5.适当的成本以及价格体系。
许多服务公司都具有不能迅速调整的服务能力和顾客需求。
同样地，服务一个新的顾客所增加的额外成本是很低的。
这篇文章研究了酒店在团体接待方面的收益管理。
酒店的顾客群体具有独特的特点，因此需要战术性的手段和典型的方法来服务每个顾客。
因此，本次研究的模型把顾客分为单个顾客和顾客群体两种类型。
以确定性和随机性的规划技术为基础，测试了一系列不同的房间最优化安排。
这个调查是为了测试技术性收益管理系统在酒店部门中的应用，以及鉴定出不同的顾客类型对系统的影响因素。
酒店业需要采用技术性收益管理来谋求生存发展，以下的几个研究更用证据说明了其必要性。
Donaghy、McMahon-Beattie和McDowel (1997) 提出了一个分为10个步骤、强调技术管理在市场细分以及每个细分市场的特的应用的模型。
Emeksiz、Gursoy 和Icoz (2006)展示了一个分5步比较运用技术管理与没有运用技术管理的酒店的模型。
在长期发展的角度，为顾客创造有利的条件也是必须的。
因此，Noone、Kimes 和Renaghan (2003)提出，必须应用CRM系统来实现收益管理，在将来的发展中保证服务质量以及顾客忠诚度。
然而，对于那些以不同的价格出售相同的服务给顾客的企业，这个更要谨慎地做。
一个发生在2000年的事例很好说明了这一点，（由Enos在2000年提出）亚马逊网站（）出售不同价格的DVD，并依照条例在不同的顾客购买地区提供20%到40%的折扣。
使用ICTs 和英特网的顾客可以查到同一部电影（DVD）的不同价格。
这一次尝试对亚马逊公司造成了负面的影响。
在其他领域，例如航空业或酒店业，价格变换的情况比较常见，但在目前为止却没有产生过任何负面影响。
这是因为航空业和酒店业出售的不同价格的服务很容易根据其特点区分，具有可接触性，所以顾客能够接收到提供的不同产品和服务。
西班牙的6间酒店成为了决策支持系统的测试区，运用TRM系统。
这些酒店是4星级酒店产业的一部分，平均每间酒店具有160间客，并座落在西班牙的南海岸，南海岸是一个具备国际水平的旅游产业集散地和目的地。
（Guzman、Moreno 和Tejada (2008)提出）TRM 系统研究重点以马贝拉城市的酒店为对象，以上这些酒店都是常年开放的，其组织者在马贝拉城市内都拥有另一间同样的酒店。
有需要的时候，顾客可以从其中一间搬到另一间。
这种链接为酒店获得了很高的顾客满意度，而且也是服务业其中一个必需的因素。
（Fullard (2007)提出）在另一篇论文中，Lindenmeier 和Tscheulin (2008)以航空业为例，展示了相同的原理。
本文由剩下的几部分组成。
第2部分提出了一种服务业用于处理问题的新方法论。
第3部分展示了用于航空业的需求预测模型及其在酒店业领域的应用。
第4部分讨论了最佳房间分布问题，一种以问题为基础的新模型。
第5部分描述了一种为房间库存管理服务的自拟模型，会在3种不同的角度定义到达的顾客。
第6部分分析结算的结果并进行比较，包括指标、入住率、实用率和收益的比较。
最后，第7部分是结论。
2.方法论Jones 和Lockwood （1998）指出，TRM系统由3个管理层级组成：战略层面关注长期以及主要的管理层。
TRM系统数据设立了市场细分标准、长期性定价策略和结构性决策。
战术层面主要处理到执行层面的中间环节。
TRM系统数据确立了在中期的不同市场占有率的目标。
执行层面关注自身短期的操作系统管理，例如销售部或者前台。
在服务业，人力资本构成了一个操作层面的决定性因素。
（Arribas 和Vila (2007)提出）TRM系统数据确定了短期内的价格以及预订量。
根据这个架构，我们可以应用一个原始方法来为TRM系统作一个合适的总括描述，这方法将在下表提到。
图1介绍了主要组成部分和给出了一个总体的信息、决定和设计、还有测试的阶段。
与在酒店所使用的价格改变策略不同，Shoemaker (2003)认为应该在“战略管理”加入“战术水平”。
接着的部分会详细描述TRM系统的每个模快。
TRM系统主要有4步：1.需求预测必须根据历史数据。
根据历史数据中的房间占用率，酒店可以预测未来一段短时期内的需求。
需求预测的准确性是特别重要的，因为其是TRM系统有效使用的条件。
频繁的历史数据更新能使预测模型的准确性提高。
2.最佳的房间分配。
TRM系统使用输入到模型中的预测数据，所以预测的数量会分布在酒店每日房间销量的不同类别中。
最佳房间分配模型在不同的价格水平上设定了房间预订量的限制。
3.房间库存控制。
这一步强调两个不同的方面：到达产生的顾客和预订系统。
首先，模型会产生顾客的到达过程，这些数据在房间库存控制的过程中有助于设立到达产生的顾客群体模块。
相反地，之前所陈述的最佳房间分配过程，会与到达产生的顾客群体模块一起输入到预订系统模块中。
房间库存控制声明了房间的销售模式和预订系统。
销售经理必须根据设立好的标准来决定接受或拒绝一个到达的顾客的订房需求。
4.现实的任务。
作为最后一步，销售处向单个的顾客提供房间价格，同时也提供价格折扣给旅行社和旅游代理带来的顾客群体。
图1Vinod (2004)提出一个适用于酒店业的收益管理系统，并强调了其组成模块的技术需要。
依据同样的看法，Chiang, Chen 和Xu (2007)强调了技术管理在收益管理领域中的重要性。
历史数据通过销售和预订的数据合并自动更新。
同样地，数据更新也因为英特网和技术管理，它们在收益管理和价格管理中都发挥着重要的作用。
现今，顾客更容易比较不同竞争酒店的价格，酒店也能更快获取到顾客行为信息。
3.需求预测收益管理很大程度上依赖一个准确的预测，准确的预测能使预订系统更具效率性，并能够作为数据参考为现实中的最优分配模型服务。
这是很多学者对预测模型的看法，包括McGill 和Van Ryzin (1999), Talluri 和Van Ryzin (2004), Pai 和Hong (2005) or Fernández-Morales 和Mayorga-Toledano (2008).TRM系统使用输入的顾客需求预测数据来获得一个房间的最优配置。
TRM系统通常通过考虑历史性的信息（包括顾客停留时间和房间类别）来计算预测的需求量。