Over-Booking Approach for Dynamic Spectrum Management

An over-booking based dynamic spectrum management (DSM) scheme is conceived for improving the attainable spectral efficiency. All secondary users (SU) will be categorized into different classes and they borrow spectral resources from the primary users (PU) before data transmission. Under the risk-based policy model, the effects of both booking cancellations and ’no-show’ reservations are analyzed. Assuming that the booking demands obey an inhomogeneous Poisson process, we derive the optimal number of excess reservations, while minimizing the total compensation costs. Algorithms are developed for determining the capacity allocation dedicated to each SU class, whilst denying those resource allocations, which would lead to congested bookings

Requirements of time management tools for outpatient physiotherapy practice

The effects of electronic appointment booking systems on the time management activities of health professionals have received little attention to date. We report on time management practices in three outpatient physiotherapy departments with different paper and electronic systems. The study has identified a set of time management activities and associated social behaviours common to physiotherapy departments. The convenience, flexibility and expressive nature of paper diary systems is of significant value to users, whilst the clarity and superior database functionality of electronic systems are valued by staff using this medium. The study highlights several potential barriers to the effective deployment of electronic booking systems in physiotherapy departments, including poor resource and training provision, concerns regarding restrictive diary control measures, the continued reliance on burdensome duplication procedures and the need to coordinate multiple information artefacts, which need to be addressed if such technology is to be successfully designed and deployed. Copyright © 2005 SAGE Publications (London, Thousand Oaks, CA and New Delhi)

Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed

A Stochastic Dynamic Programming Approach to Revenue Management in a Make-to-Stock Production System

In this paper, we consider a make-to-stock production system with known exogenous replenishments and multiple customer classes. The objective is to maximize profit over the planning horizon by deciding whether to accept or reject a given order, in anticipation of more profitable future orders. What distinguishes this setup from classical airline revenue management problems is the explicit consideration of past and future replenishments and the integration of inventory holding and backlogging costs. If stock is on-hand, orders can be fulfilled immediately, backlogged or rejected. In shortage situations, orders can be either rejected or backlogged to be fulfilled from future arriving supply. The described decision problem occurs in many practical settings, notably in make-to-stock production systems, in which production planning is performed on a mid-term level, based on aggregated demand forecasts. In the short term, acceptance decisions about incoming orders are then made according to stock on-hand and scheduled production quantities. We model this problem as a stochastic dynamic program and characterize its optimal policy. It turns out that the optimal fulfillment policy has a relatively simple structure and is easy to implement. We evaluate this policy numerically and find that it systematically outperforms common current fulfillment policies, such as first-come-first-served and deterministic optimization.revenue management;advanced planning systems;make-to-stock production;order fulfillment

An examination of ongoing trends in airline ancillary revenues

The airline industry seems permanently embedded in producing thin margins and continuously combatting downward pressure on yields. To perpetuate the problem, the industry remains eclipsed with high cost structures and low barriers to entry. However, a new sizzling concept continues to counterbalance these effects in the form of ancillary revenues. Globally, these revenues have increased by 121% from 2010 to 2014 – and the trend is set to continue as carriers are quickly implementing structural changes to accommodate these revenues streams. This paper examines the performance of the two core classifications of ancillary revenues, which are unbundled products and commission based income. It also investigates the willingness of passengers to pay for these services together with what type of ancillary items are acceptable at a particular price point. The study found that passengers value a narrow range of perceived ‘necessity’ products and services such as food and drink, checked baggage and seat assignment as opposed to perceived ‘optional’ unbundled or commission based products/services. It also found significant differences in WTP for specific ancillary services based on carrier type (FSC/LCC/Charter), length of flight (long and short haul) and journey purpose (business, leisure, VFR)

A Review of Trip Planning Systems.

This report reviews current information provision in all modes of transport and assesses the needs for and benefits of trip planning systems. The feasibility of trip planning systems is discussed given the current state of technology and information availability and supply. The review was stimulated by technological developments in telecommunications and information technology which are providing the possibility of a greatly enhanced quality of information to aid trip planning decisions. Amongst the conclusions reached were the following: Current information provision is considered deficient in many respects. Travellers are often unaware of alternative routes or services and many are unable to acquire adequate information from one source especially for multi-modal journeys. In addition, there is a lack of providing real time information where it is required (bus stops and train stations) and of effective interaction of static and real time information. Most of the projects, which integrate static and dynamic data, are single mode systems. Therefore there is a need for an integrated trip planning system which can inform and guide on all aspects of transport. Trip planning systems can provide assistance in trip planning (before and during the journey) using one or a number of modes of travel, taking into account travellers preferences and constraints, and effectively integrating static and dynamic data. Trip planning systems could adversely affect traffic demand as people who become aware of new opportunities might be encouraged to make more journeys. It could also affect travellers choice as a result of over-saturation of information, over-reaction to predictive information, and concentration on the same 'best' routes. However, it can be argued, based on existing evidence, that such a system can benefit travellers, and transport operators as well as the public sector responsible for executing transport policies. Travellers can benefit by obtaining adequate information to help them in making optimal decisions and reducing uncertainty and stress associated with travel. Public transport operators can benefit by making their services known to customers, leading to increased patronage. Public transport authorities can use the supply of information to execute their transport policies and exercise more control over traffic management

Unveiling Venice’s hotels competition networks from dynamic pricing digital market

We study the dynamic price competition of hotels in Venice using publicly available data scraped from an online travel agency. This study poses two main challenges. First, the time series of prices recorded for each hotel encompasses a twofold time frame. For every single asking price for an overnight stay on a specific day, there is a corresponding time series of asking prices along the booking window on the online platforms. Second, the competition relations between different hoteliers is clearly unknown and needs to be discovered using a suitable methodology. We address these problems by proposing a novel Network Autoregressive model which is able to handle the peculiar threefold data structure of the data set with time-varying coefficients over the booking window. This approach allows us to uncover the competition network of the market players by employing a quick data-driven algorithm. Independent, mixed, and leader–follower relationships are detected, revealing the competitive dynamics of the destination, useful to managers and stakeholders

An Agent-Based Decision Support Model for the Development of E-Services in the Tourist Sector

This paper regards cultural heritage as a strategic development tool for urban tourist policy. It highlights the use of e-services as a central instrument in a competitive tourist sector. The appropriate choice of e-services - and packages thereof - depends on the various strategic considerations of urban stakeholders (agents) and may differ for each individual city. The paper offers a systematic analysis framework for supporting these choices and deploys multi-criteria analysis as a systematic evaluation methodology, in particular the Regime method. The evaluation framework is exemplified through an application to three field cases in Europe, viz. the cities of Amsterdam, Genoa and Leipzig. Our analysis concludes that tailor-made packages of e-services that serve the needs of the stakeholders can be made with the help of our evaluation tools.cultural heritage, e-services, city marketing, agent-based decision support model

TOWARDS AN EFFICIENT DECISION POLICY FOR CLOUD SERVICE PROVIDERS

Cloud service providers may face the problem of how to price infrastructure services and how this pricing may impact the resource utilization. One aspect of this problem is how Cloud service providers would decide to accept or reject requests for services when the resources for offering these services become scarce. A decision support policy called Customized Bid-Price Policy (CBPP) is proposed in this paper to decide efficiently, when a large number of services or complex services can be offered over a finite time horizon. This heuristic outperforms well-known policies, if bid prices cannot be updated frequently during incoming requests and an automated update of bid prices is required to achieve more accurate decisions. Since CBPP approximates the revenue offline before the requests occur, it has a low runtime compared to other approaches during the online phase. The performance is examined via simulation and the pre-eminence of CBPP is statistically proven