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This study aims to analyze how different room-committing practices affect the occupancy and profitability of hotels and it critically reviews the role of minimum-length-of-stay (MLOS) requirements given these findings.

The approach uses statistical analysis of simplified contexts to develop understanding, and simulations of more complex situations to confirm the relevance in realistic contexts.

The study demonstrates that proper solutions of the room-committing problem improve occupancy and profitability, in particular, for hotels working in high-season and high-occupancy situations. Smart committing algorithms diminish the role of MLOS requirements. More demand can be accepted without sacrificing late-arriving long reservations.

To the best of the authors’ knowledge, this work, building upon a previous one cited in this paper, is the first to rigorously study the room-committing problem and to demonstrate its relevance in practical situations and its implications on MLOS rules.

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）。

DONCASTER'S new Central Library was formally opened on 29th December 1969 on precisely the 100th anniversary of the opening of the first public library in Doncaster. Conforming to tradition, the Library was opened by the Mayor of Doncaster, Councillor Marcus Outwin. The President of the Library Association, Mr. Wilfred Ashworth, addressed the assembled guests, his last official appointment before relinquishing the office.

A number of anti-transgender bills were proposed in the United States during the first half of 2021, with several becoming laws. The passing of these bills has made it increasingly difficult for trans people to gain access to lifesaving, gender-affirming healthcare and to participate in sport based on their gender identity rather than their sex assignment at birth. In light of these new laws, sport organizations such as the NCAA have made statements promising to support trans athletes. While these statements may promote awareness, in order for trans athletes to compete safely and openly throughout their collegiate careers, the NCAA must create policies and implement effective practices centred around trans inclusion. In this chapter, I (Estel, they/he) share my journey as a transgender student-athlete at an NCAA Division I university during the Spring 2021 season. During my collegiate athletic career, I felt both grateful for the chance to row and fear and frustration due to discrimination. Through memory work and participant case analysis, my co-author and I outline the ways that transphobic myths may have influenced my interactions with administrators, coaches and athletes throughout my collegiate athletic career and negatively impacted my wellbeing and performance. We also identify specific ways in which NCAA policies on trans inclusion are outdated and do not align with their expressed commitment to the well-being of trans student-athletes; we provide recommendations for changes to these policies. Further, we recommend trans-inclusive practices NCAA member institutions can implement to create an inclusive and empowering sports environment.

Voyager Inn International (Bethesda) is negotiating a master contract with TourAmerica, an international tour operator, for hotel rooms during the 1995 tourist season. Issues under consideration include number of rooms during peak, mid-, and off-periods; room rates; breakfast prices; and the cost of ancillary services. The hotel manager is evaluated on the basis of several criteria, including adjusted daily rates, occupancy rates, and food and beverage profitability. To facilitate trade-offs among the various criteria, the manager articulates a utility scheme. This case is a role-play exercise, and must be used in conjunction with “TourAmerica” (UVA-QA-0464). Reporting forms for the evaluation of alternative contracts are provided with each case.

The purpose of this paper is to inform an international audience of the difficulties prosecutors in the USA have encountered in light of a decision of the US Supreme Court limiting the application of the federal anti‐money laundering statute to cases where a criminal enterprise generated profits.

The paper summarizes the law in the USA regarding money laundering prosecutions before the decision in the United States v. Santos, outlines the decision of the Supreme Court, and organizes the post‐Santos case law into categories setting forth the divergent views of what the decision means and how it is to be applied.

The case law in the USA regarding money laundering prosecutions is now quite unsettled. Courts have taken different views as to whether the government must now prove that the funds being laundered by or on behalf of a criminal represent the profits of the criminal enterprise as opposed to its gross receipts.

The case law on this issue continues to develop at a rapid pace. It is necessary to cut off the research on this issue to complete the paper, but the reader should be aware that new cases are being issued in rapid order.

Prosecutors in the USA now face several obstacles in bringing money launderers to justice. Decisions in closed cases may be reopened as defendants argue that their convictions are obtained under an incorrect view of the law. Going forward, prosecutors are uncertain whether the government must prove that a criminal enterprise is profitable before they can obtain a conviction for money laundering.

Prior to Santos, it is assumed that it is an offense to launder the gross receipts of a crime or criminal scheme. Santos cases grave doubt on that assumption, holding that in at least some cases, the laundering offense will apply only where the financial transaction involves the net profits of an offense. This is an object lesson in the confusion that can result from inartful legislative drafting. It also provides a guide to the current state of the law and suggests how prosecutors in the USA are dealing with the problem pending any legislative correction.

This study aims to provide a critical reflection of the application of image recognition techniques in visual information mining in hospitality and tourism.

This study begins by reviewing the progress of image recognition and advantages of convolutional neural network-based image recognition models. Next, this study explains and exemplifies the mechanisms and functions of two relevant image recognition applications: object recognition and facial recognition. This study concludes by providing theoretical and practical implications and potential directions for future research.

After this study presents different potential applications and compares the use of image recognition with traditional manual methods, the main findings of this critical reflection revolve around the feasibility of the described techniques.

Knowledge on how to extract valuable visual information from large-scale user-generated photos to infer the online behavior of consumers and service providers and its influence on purchase decisions and firm performance is crucial to business practices in hospitality and tourism.

Visual information plays a crucial role in online travel agencies and peer-to-peer accommodation platforms from the side of sellers and buyers. However, extant studies relied heavily on traditional manual identification with small samples and subjective judgment. With the development of deep learning and computer vision techniques, current studies were able to extract various types of visual information from large-scale datasets with high accuracy and efficiency. To the best of the authors’ knowledge, this study is the first to offer an outlook of image recognition techniques for mining visual information in hospitality and tourism.