Tight bounds on the competitive ratio on accomodating sequences for the seat reservation problem

The unit price seat reservation problem is investigated. The seat reservation problem is the problem of assigning seat numbers on-line to requests for reservations in a train traveling through $k$ stations. We are considering the version where all tickets have the same price and where requests are treated fairly, i.e., a request which can be fulfilled must be granted. For fair deterministic algorithms, we provide an asymptotically matching upper bound to the existing lower bound which states that all fair algorithms for this problem are frac{1{2-competitive on accommodating sequences, when there are at least three seats. Additionally, we give an asymptotic upper bound of frac{7{9 for fair randomized algorithms against oblivious adversaries. We also examine concrete on-line algorithms, First-Fit and Random, for the special case of two seats. Tight analyses of their performance are given

Online Algorithms for Dynamic Resource Allocation Problems

Dynamic resource allocation problems are everywhere. Airlines reserve flight seats for those who purchase flight tickets. Healthcare facilities reserve appointment slots for patients who request them. Freight carriers such as motor carriers, railroad companies, and shipping companies pack containers with loads from specific origins to destinations. We focus on optimizing such allocation problems where resources need to be assigned to customers in real time. These problems are particularly difficult to solve because they depend on random external information that unfolds gradually over time, and the number of potential solutions is overwhelming to search through by conventional methods. In this dissertation, we propose viable allocation algorithms for industrial use, by fully leveraging data and technology to produce gains in efficiency, productivity, and usability of new systems. The first chapter presents a summary of major methodologies used in modeling and algorithm design, and how the methodologies are driven by the size of accessible data. Chapters 2 to 5 present genuine research results of resource allocation problems that are based on Wang and Truong (2017); Wang et al. (2015); Stein et al. (2017); Wang et al. (2016). The algorithms and models cover problems in multiple industries, from a small clinic that aims to better utilize its expensive medical devices, to a technology giant that needs a cost-effective, distributed resource-allocation algorithm in order to maintain the relevance of its advertisements to hundreds of millions of consumers

The concept and impact analysis of a flexible mobility on demand system

This paper introduces an innovative transportation concept called Flexible Mobility on Demand (FMOD), which provides personalized services to passengers. FMOD is a demand responsive system in which a list of travel options is provided in real-time to each passen- ger request. The system provides passengers with flexibility to choose from a menu that is optimized in an assortment optimization framework. For operators, there is flexibility in terms of vehicle allocation to different service types: taxi, shared-taxi and mini-bus. The allocation of the available fleet to these three services is carried out dynamically so that vehicles can change roles during the day. The FMOD system is built based on a choice model and consumer surplus is taken into account in order to improve passenger satisfac- tion. Furthermore, profits of the operators are expected to increase since the system adapts to changing demand patterns. In this paper, we introduce the concept of FMOD and present preliminary simulation results. It is shown that the dynamic allocation of the vehicles to different services provides significant benefits over static allocation. Furthermore, it is observed that the trade-off between consumer surplus and operator’s profit is critical. The optimization model is adapted in order to take into account this trade-off by control- ling the level of passenger satisfaction. It is shown that with such control mechanisms FMOD provides improved results in terms of both profit and consumer surplus

Airline overbooking performance measurement

Cover titleMay 1995Submitted to the Department of Aeronautics and Astronautics on May 5, 1995 in partial fulfillment of the requirements for the Degree of Diplom Ingenieur in Luft- und Raumfahrttechnik"--P. 1Includes bibliographical references (p. 115-117)Since the "product" of an airline cannot be stored, the value of every seat which is left empty upon departure is lost forever or "spoiled". In order to compensate for the economic effects of passengers holding a confirmed reservation who fail to show-up, airlines overbook, i.e. accept more reservations than physical seats are available under the assumption that sufficient no-shows will occur. Even though airlines have overbooked their flights intentionally for decades, very few efforts have been made to measure the economic success of overbooking. As revenue maximization becomes more critical to the profitability of an airline, it is even more important to review the balanced tradeoff between denied boardings and spoilage. This thesis outlines the major philosophies of the currently applied overbooking models and illustrates further the common overbooking performance measurement approaches. As all of these models demonstrate significant shortcomings, a new model, the Revenue Achievement Model, is introduced. This new approach is based on a purely economics driven philosophy. Along with the Revenue Achievement Model, the different definitions of spoilage, oversales and other key values for the overbooking performance evaluation are reviewed and defined anew in an attempt to standardize the terminology. It is shown that the Revenue Achievement Model is more consistent with today's overbooking models than other overbooking performance measurement models. It matches the economic objectives of the airlines and shows superior qualities in comparing flights on a single flight level as well as evaluating the aggregate performance for large samples. The proposed methodology enables also to obtain a target performance index which allows a quantification of the objectives of overbooking. Finally, the impact of system overrides by revenue management analysts is analyzed and methods are suggested to evaluate their actions

Multicriteria mapping manual: version 1.0

This Manual offers basic advice on how to do multicriteria mapping (MCM). It suggests how to: go about designing and building a typical MCM project; engage with participants and analyse results – and get the most out of the online MCM tool. Key terms are shown in bold italics and defined and explained in a final Annex. The online MCM software tool provides its own operational help. So this Manual is more focused on the general approach. There are no rigid rules. MCM is structured, but very flexible. It allows many more detailed features than can be covered here. MCM users are encouraged to think for themselves and be responsible and creative