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ABSTRACTIn aircraft concept selection, uncertainties, resulting from economic noise variables such as fuel price, load factor, so on always exist in the decision making process. These uncertainties are often addressed using Taguchi loss functions for robustness analysis. The study presented in this paper is to utilize multi‐criteria decision making (MCDM) methods to solve an aircraft concept selection problem for a hypothetical airline when considering the robustness of the decision. Three MCDM methods were used to conduct the concept selection, including ELimination and Choice Expressing REality, simple additive weighting and technique for order preference by similarity to ideal solution. In this study, robustness is introduced as an additional decision criterion in the aircraft selection process. The impact with and without robustness consideration is compared in order to demonstrate how the proposed method makes trade‐offs between nominal performance and address uncertainty in the decision‐making process. In addition, sensitivity analysis is performed to investigate the influence of the decision criteria, especially robustness, on the final ranking of alternatives. The implementation of three MCDM methods on the aircraft selection problem also demonstrates the potential application of MCDM methods in the assessment of air transportation systems, while balancing economical, ecological, and technical constraints. Copyright © 2011 John Wiley & Sons, Ltd.

AbstractWe consider the problem of scheduling a set of jobs on a single machine subject to random breakdowns. We focus on the preemptive‐repeat model, which addresses the situation where, if a machine breaks down during the processing of a job, the work done on the job prior to the breakdown is lost and the job will have to be started from the beginning again when the machine resumes its work. We allow that (i) the uptimes and downtimes of the machine follow general probability distributions, (ii) the breakdown process of the machine depends upon the job being processed, (iii) the processing times of the jobs are random variables following arbitrary distributions, and (iv) after a breakdown, the processing time of a job may either remain a same but unknown amount, or be resampled according to its probability distribution. We first derive the optimal policy for a class of problems under the criterion to maximize the expected discounted reward earned from completing all jobs. The result is then applied to further obtain the optimal policies for other due date‐related criteria. We also discuss a method to compute the moments and probability distributions of job completion times by using their Laplace transforms, which can convert a general stochastic scheduling problem to its deterministic equivalent. The weighted squared flowtime problem and the maintenance checkup and repair problem are analyzed as applications. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004

This article belongs to the Section Aerospace Science and Engineering. ; Air transport delays are a major source of direct and opportunity costs in modern societies, being this problem is especially important in the case of China. In spite of this, our knowledge on delay generation is mostly based on intuition, and the scientific community has hitherto devoted little attention to this topic. We here present the first data-driven systemic study of air transport delays in China, of their evolution and causes, based on 11 million flights between 2016 and 2018. A significant fraction of the delays can be explained by a few variables, e.g., weather conditions and traffic levels, the most important factors being the presence of thunderstorms and the season of the year. Remaining delays can often be explained by en-route weather phenomena or by reactionary delays. This study contributes towards a better understanding of delays and their prediction through a data-driven methodology, leveraging on statistics and data mining concepts. ; This study is supported by the Research Fund from National Natural Science Foundation of China (Grants No. 61861136005, No. 61851110763, No. 71731001). This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 851255). Financial support has been received from the Agencia Estatal de Investigación (AEI, MCI, Spain) and Fondo Europeo de Desarrollo Regional (FEDER, UE), under the Maria de Maeztu Program for units of Excellence in R&D (MDM-2017-0711). ; Peer reviewed