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The purpose of this paper is to present an overview of revenue management (RM) studies that focus on information technology (IT) and consumer behavior published between 2008 and 2018.

In total, 112 articles published in 17 journals were identified and analyzed.

This study shows the importance of IT and RM and focuses on the consumer perspective. It also emphasizes that technology is not the enemy of humans: it complements and adds value to their existing jobs.

Book chapters and conference proceedings related to IT and RM were not included in this study. Besides, only journal papers published in English were included in the study. The categorizing of subjects can be seen as subjective.

This study helps researchers discover articles from 2008 to 2018 and helps hospitality executives interested in RM technologies from the demand side to use these findings in their business environment.

Based on the interaction between service providers (hotels) and users (consumers) on IT and RM platforms, the paper identified eight key components that have been relevant over the past decade.

本论文旨在介绍2008年至2018年之间的财务管理（RM）研究中涉及信息技术（IT）和消费者行为的文献综述。

本研究样本为发表在17个期刊的共112篇文章。

研究结果指出了IT和RM的重要性, 以及对消费者方面的重视。此外, 本研究还指出了技术不是人类的敌人– 技术能够弥补以及对人类原有的工作增添价值。

本研究未将涉及IT和RM的书和会议文章纳入样本。此外, 只有英文的期刊文章构成研究样本。对研究样本的主题归类是主观性的。

本论文梳理了2008年至2018年发表的文献, 以及帮助酒店实践者们对RM科技从需求方面更了解其商务环境。

本论文基于服务供应者（酒店）和用户（消费者）在IT和RM交互平台上, 提出了过去十年中相关的八大关键因素。

Many hotels allocate guests to specific rooms immediately after reservation. This happens because individual rooms are sold (and there is no concept of room type) or because the assignment is done by hand at reservation or because of a connection with a channel manager, which is immediately fixing the room number after a reservation request. This early allocation is suboptimal, and it causes the unnecessary rejection of some reservations when the hotel has a high occupancy level. The purpose of this paper is to investigate different room allocation algorithms, including an optimal one (called RoomTetris), aiming at higher occupancy levels and profitability.

The methodology is based on theoretical results and experimentation. The optimality or the proposed RoomTetris algorithm is demonstrated. Experiments are executed in different contexts, including realistic ones, through the adoption of a hotel simulator, to measure the improvements in the occupancy rate of the optimal and heuristic strategies with respect to random or sub-optimal assignments of rooms.

The main results are that smart allocation algorithms can greatly reduce the rejection rate (reservation requests which cannot be fit into the hotel room plan) and improve the occupancy level, the percentage of available rooms or beds sold for the various periods.

This analysis can be extended by considering cancellations and overbookings. A second possibility to add flexibility in room allocation for hotels having more than one type of rooms is that the hotel can upgrade and offer a high-price room to the customer, which given an even large flexibility to fix rooms by shifting customers to other compatible types. In addition, more complex integrations with revenue management can also be considered, for cases in which the cost of a room depends on the number of guests.

Given that the difference in occupancy rate of the optimal algorithm is particularly large in high season and high-request periods, periods which are usually associated to higher rates and higher volumes, the proposed algorithm will improve the main financial performance indicators such as revenue per available room by an even bigger multiplier, depending on the hotel pricing policy. Because the room allocation process can be completely automated, the adoption of appropriate smart allocation algorithms represents a low-hanging fruit to be picked by efficient hotel managers.

To the best of the knowledge this is the first proposal of an optimal algorithm (with proof of optimality) for the considered problem.

很多酒店, 特别是私人、家庭经营型、或者精品酒店, 在客人预定后立刻分派指定的房间给客人。这往往是因为独立房间售卖（没有特殊房型概念）或者因为客人在预定时, 工作人员手动指派房间, 亦或者是因为预订系统与渠道管理系统链接, 直接在预定后指派房间号。这种早期的分派程序是不优化的, 往往在酒店住房率高的时候, 会造成一些不必要的房间预定失败, 继而带来的利润损失。本论文旨在研究不同房间指派参数配置, 包括最优系统（RoomTetris）, 使得酒店达到更高住房率的同时产生高利润。

本论文采用理论讨论和实验等研究方法, 并展示了提出的RoomTetris参数的最优性。本论文还将其参数放在不同的情景中做实验, 以显示其提高酒店针对随机或者次优化分派的最佳启发式策略中的住房率。

研究结果表明智能型分派参数能够大大降低预定失败率（预定需求不能符合酒店房型供给）, 并且提高住房率和利润。住房时间并不是必须的参数, 极具个性化服务, 比如让客人选房间号, 可能导致利润损失（因为最优房间分派无法实现）, 房型的设计也应该参与到最优房间分派的效果中来。

预定取消和超额预定的情况也应该加入到分析中来。第二种对于拥有不止一种房型的酒店来说, 可能增加房间分派的情况在于为客人升级房型, 这样可以将客人转到其他适合房型以解决房间分派问题。此外, 更复杂系统兼容财务管理系统应该被考量, 有的时候, 房间的成本取决于客人的数量。

由于最优算法的住房率区别在于旺季和高预定时段, 也就是高房间价格和高预定量, 本论文提出的最佳算法将提高主要财务指标, 比如RevPAR（平均客房收益）。由于房间分配系统可以完全实现自动化, 那么采用智能分派系统无疑是有效酒店管理中的优质选择。

据作者所知, 此文章是首篇关于此类话题的研究优质算法（且被证实其最佳）。