Application of learning algorithms to traffic management in integrated services networks.

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Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Analytic approximations of queues with lightly- and heavily-correlated autoregressive service times

We consider a single-server queueing system. The arrival process is modelled as a Poisson process while the service times of the consecutive customers constitute a sequence of autoregressive random variables. Our interest into autoregressive service times comes from the need to capture temporal correlation of the channel conditions on wireless network links. If these fluctuations are slow in comparison with the transmission times of the packets, transmission times of consecutive packets are correlated. Such correlation needs to be taken into account for an accurate performance assessment. By means of a transform approach, we obtain a functional equation for the joint transform of the queue content and the current service time at departure epochs in steady state. To the best of our knowledge, this functional equation cannot be solved by exact mathematical techniques, despite its simplicity. However, by means of a Taylor series expansion in the parameter of the autoregressive process, a "light-correlation" approximation is obtained for performance measures such as moments of the queue content and packet delay. We illustrate our approach by some numerical examples, thereby assessing the accuracy of our approximations by simulation. For the heavy correlation case, we give differential equation approximations based on the time-scale separation technique, and present numerical examples in support of this approximation

Multi-fidelity modelling approach for airline disruption management using simulation

Disruption to airline schedules is a key issue for the industry. There are various causes for disruption, ranging from weather events through to technical problems grounding aircraft. Delays can quickly propagate through a schedule, leading to high financial and reputational costs. Mitigating the impact of a disruption by adjusting the schedule is a high priority for the airlines. The problem involves rearranging aircraft, crew and passengers, often with large fleets and many uncertain elements. The multiple objectives, cost, delay and minimising schedule alterations, create a trade-off. In addition, the new schedule should be achievable without over-promising. This thesis considers the rescheduling of aircraft, the Aircraft Recovery Problem. The Aircraft Recovery Problem is well studied, though the literature mostly focusses on deterministic approaches, capable of modelling the complexity of the industry but with limited ability to capture the inherent uncertainty. Simulation offers a natural modelling framework, handling both the complexity and variability. However, the combinatorial aircraft allocation constraints are difficult for many simulation optimisation approaches, suggesting that a more tailored approach is required. This thesis proposes a two-stage multi-fidelity modelling approach, combining a low-fidelity Integer Program and a simulation. The deterministic Integer Program allocates aircraft to flights and gives an initial estimate of the delay of each flight. By solving in a multi-objective manner, it can quickly produce a set of promising solutions representing different trade-offs between disruption costs, total delay and the number of schedule alterations. The simulation is used to evaluate the candidate solutions and look for further local improvement. The aircraft allocation is fixed whilst a local search is performed over the flight delays, a continuous valued problem, aiming reduce costs. This is done by developing an adapted version of STRONG, a stochastic trust-region approach. The extension incorporates experimental design principles and projected gradient steps into STRONG to enable it to handle bound constraints. This method is demonstrated and evaluated with computational experiments on a set of disruptions with different fleet sizes and different numbers of disrupted aircraft. The results suggest that this multi-fidelity combination can produce good solutions to the Aircraft Recovery Problem. A more theoretical treatment of the extended trust-region simulation optimisation is also presented. The conditions under which a guarantee of the algorithm's asymptotic performance may be possible and a framework for proving these guarantees is presented. Some of the work towards this is discussed and we highlight where further work is required. This multi-fidelity approach could be used to implement a simulation-based decision support system for real-time disruption handling. The use of simulation for operational decisions raises the issue of how to evaluate a simulation-based tool and its predictions. It is argued that this is not a straightforward question of the real-world result being good or bad, as natural system variability can mask the results. This problem is formalised and a method is proposed for detecting systematic errors that could lead to poor decision making. The method is based on the Probability Integral Transformation using the simulation Empirical Cumulative Distribution Function and goodness of fit hypothesis tests for uniformity. This method is tested by applying it to the airline disruption problem previously discussed. Another simulation acts as a proxy real world, which deviates from the simulation in the runway service times. The results suggest that the method has high power when the deviations have a high impact on the performance measure of interest (more than 20%), but low power when the impact is less than 5%

Review and evaluation of national airspace system models

Cover titleOctober 1979Includes bibliographical references (p. B1-B32)Abstract from Technical Report Documentation Page: This report is intended to serve as a guide to the availability and capability of state-of-the-art analytical and simulation models of the National Airspace System (NAS). An extensive literature search produced a listing of 230 reports potentially containing technical descriptions of models developed during the last decade. These reports are classified into primary categories based on applicability of the model to various aspects of the NAS. Capacity/delay models are classified as capacity-oriented runway, delay-oriented runway, complete airport, terminal airspace. air route traffic (including communications), controller workload and performance, and models of major segments of the NAS. Reports describing models primarily concerned with safety-related measures and noise-related measures are categorized separately. Reports were initially screened to eliminate those known to have been superseded by a subsequent report, and those containing inadequate or inconsequential technical information concerning models. The remaining reports (approximately 180) were subjected to a detailed review. The results of this review are documented for each of the 50 distinct models described by the selected reports. Information contained in each model review includes report ID, abstract, input/output parameters, computer-related characteristics, assumptions, quality of documentation, extent of validation, and an evaluation of the model's usefulness and limitations. Another part of the report contains a comparative evaluation of models in the same primary category. These evaluations present an overview of the models contained in each category, summarize the main features of the best models, and document the conclusions and recommendations regarding the models best suited for specific applications.Prepared for U.S. Dept. of Transportation, Federal Aviation Administration, Office of Systems Engineering Managemen