Optimisation de la gestion des avions dans un aéroport : affectation aux points de stationnement, routage au sol et ordonnancement à la piste.

In this thesis, we address the optimization of aircraft ground operations at airports, focusing on three main optimization problems: the stand allocation, the ground routing between stands and runways, and the sequencing of take-offs and landings.These works result from a close collaboration with Amadeus. Our approaches have been tested and validated with real data from European airports.The stand allocation problem is formulated as a Mixed Integer Program (MIP). We show that finding an allocation plan respecting operational requirements is NP-Complete and we strengthen our model in several directions. We obtain better solutions than the literature withing reasonable computation times for an industrial application.The ground routing problem is modeled by a MIP formulation adapted from the literature. We show that the main indicators of the industry are in contradiction with the objective of reducing taxi times and therefore air pollution. We propose new indicators based on take-off times instead of push back times.Lastly, we focus on the integration of the runway sequencing with the ground routing. We highlight that a better integration allows to reduce taxi times while improving the management of the runway. We propose a sequential heuristic based on an innovative MIP formulation of the runway sequencing problem. This heuristic is shown to provide high quality solutions in reasonable computation times, unlike the exact approach from the literature.Le cadre de cette thèse est l'optimisation des opérations aéroportuaires. Nous nous intéressons à trois problèmes de gestion des avions dans un aéroport : l'affectation aux points de stationnement, le routage au sol entre les pistes et les points de stationnement, et l'ordonnancement des décollages et des atterrissages.Ce travail a été réalisée en collaboration étroite avec la société Amadeus. Nos approches ont été testées et validées avec des données réelles provenant d'aéroports européens.Nous proposons une formulation en Programme Linéaire en Nombres Entiers (PLNE) du problème d'affectation aux points de stationnement. Nous montrons que trouver une affectation réalisable est un problème NP-Complet et nous proposons diverses améliorations visant à réduire le temps de résolution de notre modèle. Nous obtenons ainsi des solutions de meilleure qualité que celles de la littérature, tout en conservant un temps de calcul raisonnable.Le problème de routage au sol est modélisé en adaptant un PLNE de la littérature. Nous montrons que les indicateurs de l'industrie sont en contradiction avec l'objectif de réduction du temps de roulage, et donc des émissions de pollutions. Nous proposons de nouveaux indicateurs basés sur l'heure de décollage, et non sur l'heure de départ du point de stationnement.Enfin, nous nous intéressons à l'intégration de l'ordonnancement à la piste avec le routage au sol. Nous montrons qu'une meilleure intégration permet de réduire le temps de roulage et d'améliorer la gestion de la piste. Nous proposons une heuristique séquentielle basée sur une modélisation en PLNE innovante du problème d'ordonnancement à la piste. Nous montrons que cette heuristique fournit des solutions de bonne qualité en temps raisonnable, contrairement à l'approche exacte de la littérature

Large-scale mixed integer optimization approaches for scheduling airline operations under irregularity

Perhaps no single industry has benefited more from advancements in computation, analytics, and optimization than the airline industry. Operations Research (OR) is now ubiquitous in the way airlines develop their schedules, price their itineraries, manage their fleet, route their aircraft, and schedule their crew. These problems, among others, are well-known to industry practitioners and academics alike and arise within the context of the planning environment which takes place well in advance of the date of departure. One salient feature of the planning environment is that decisions are made in a frictionless environment that do not consider perturbations to an existing schedule. Airline operations are rife with disruptions caused by factors such as convective weather, aircraft failure, air traffic control restrictions, network effects, among other irregularities. Substantially less work in the OR community has been examined within the context of the real-time operational environment. While problems in the planning and operational environments are similar from a mathematical perspective, the complexity of the operational environment is exacerbated by two factors. First, decisions need to be made in as close to real-time as possible. Unlike the planning phase, decision-makers do not have hours of time to return a decision. Secondly, there are a host of operational considerations in which complex rules mandated by regulatory agencies like the Federal Administration Association (FAA), airline requirements, or union rules. Such restrictions often make finding even a feasible set of re-scheduling decisions an arduous task, let alone the global optimum. The goals and objectives of this thesis are found in Chapter 1. Chapter 2 provides an overview airline operations and the current practices of disruption management employed at most airlines. Both the causes and the costs associated with irregular operations are surveyed. The role of airline Operations Control Center (OCC) is discussed in which serves as the real-time decision making environment that is important to understand for the body of this work. Chapter 3 introduces an optimization-based approach to solve the Airline Integrated Recovery (AIR) problem that simultaneously solves re-scheduling decisions for the operating schedule, aircraft routings, crew assignments, and passenger itineraries. The methodology is validated by using real-world industrial data from a U.S. hub-and-spoke regional carrier and we show how the incumbent approach can dominate the incumbent sequential approach in way that is amenable to the operational constraints imposed by a decision-making environment. Computational effort is central to the efficacy of any algorithm present in a real-time decision making environment such as an OCC. The latter two chapters illustrate various methods that are shown to expedite more traditional large-scale optimization methods that are applicable a wide family of optimization problems, including the AIR problem. Chapter 4 shows how delayed constraint generation and column generation may be used simultaneously through use of alternate polyhedra that verify whether or not a given cut that has been generated from a subset of variables remains globally valid. While Benders' decomposition is a well-known algorithm to solve problems exhibiting a block structure, one possible drawback is slow convergence. Expediting Benders' decomposition has been explored in the literature through model reformulation, improving bounds, and cut selection strategies, but little has been studied how to strengthen a standard cut. Chapter 5 examines four methods for the convergence may be accelerated through an affine transformation into the interior of the feasible set, generating a split cut induced by a standard Benders' inequality, sequential lifting, and superadditive lifting over a relaxation of a multi-row system. It is shown that the first two methods yield the most promising results within the context of an AIR model.PhDCommittee Co-Chair: Clarke, John-Paul; Committee Co-Chair: Johnson, Ellis; Committee Member: Ahmed, Shabbir; Committee Member: Clarke, Michael; Committee Member: Nemhauser, Georg

New Solution Approaches for Scheduling Problems in Production and Logistics

The current cumulative PhD thesis consists of six papers published in/submitted to scientific journals. The focus of the thesis is to develop new solution approaches for scheduling problems encountering in manufacturing as well as in logistics. The thesis is divided into two parts: “ma-chine scheduling in production” and “scheduling problems in logistics” each of them consisting three papers. To have most comprehensive overview of the topic of machine scheduling, the first part of the thesis starts with two systematic review papers, which were conducted on tertiary level (i.e., re-viewing literature reviews). Both of these papers analyze a sample of around 130 literature re-views on machine scheduling problems. The first paper use a subjective quantitative approach to evaluate the sample, while the second papers uses content analysis which is an objective quanti-tative approach to extract meaningful information from massive data. Based on the analysis, main attributes of scheduling problems in production are identified and are classified into sever-al categories. Although the focus of both these papers are set to review scheduling problems in manufacturing, the results are not restricted to machine scheduling problem and the results can be extended to the second part of the thesis. General drawbacks of literature reviews are identi-fied and several suggestions for future researches are also provided in both papers. The third paper in the first part of the thesis presents the results of 105 new heuristic algorithms developed to minimize total flow time of a set of jobs in a flowshop manufacturing environ-ment. The computational experiments confirm that the best heuristic proposed in this paper im-proves the average error of best existing algorithm by around 25 percent. The first paper in second part is focused on minimizing number of electric tow-trains responsi-ble to deliver spare parts from warehouse to the production lines. Together with minimizing number of these electric vehicles the paper is also focused to maximize the work load balance among the drivers of the vehicles. For this problem, after analyzing the complexity of the prob-lem, an opening heuristic, a mixed integer linear programing (MILP) model and a taboo-search neighborhood search approach are proposed. Several managerial insights, such as the effect of battery capacity on the number of required vehicles, are also discussed. The second paper of the second part addresses the problem of preparing unit loaded devices (ULDs) at air cargos to be loaded latter on in planes. The objective of this problem is to mini-mize number of workers required in a way that all existing flight departure times are met and number of available places for building ULDs is not violated. For this problem, first, a MILP model is proposed and then it is boosted with a couple of heuristics which enabled the model to find near optimum solutions in a matter of 10 seconds. The paper also investigates the inherent tradeoff between labor and space utilization as well as the uncertainty about the volume of cargo to be processed. The last paper of the second part proposes an integrated model to improve both ergonomic and economic performance of manual order picking process by rotating pallets in the warehouse. For the problem under consideration in this paper, we first present and MILP model and then pro-pose a neighborhood search based on simulated annealing. The results of numerical experiment indicate that selectively rotating pallets may reduce both order picking time as well as the load on order picker, which leads to a quicker and less risky order picking process

Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

Inclusion of Geometrically Nonlinear Aeroelastic Effects into Gradient-Based Aircraft Optimization

While aircraft have largely featured flexible wings for decades, more recently, aircraft structures have rapidly become more flexible. The pursuit of longer ranges and higher efficiency through higher aspect ratio wings, as well as the introduction of modern, light-weight materials has yielded moderately and very flexible aircraft configurations. Past accidents, such as the loss of the Helios High Altitude Long Endurance (HALE) aircraft have highlighted the limitations of linear analysis methods and demonstrated the peril of neglecting nonlinear effects when designing such aircraft. In particular, accounting for geometrical nonlinearities in flutter analyses become necessary in aircraft optimization, including transport aircraft, or future aircraft may require costly modifications late in the design process to fulfill certification requirements. As a result, there is a need to account for geometrical nonlinearities earlier in the design process and integrate these analyses directly into the multi-disciplinary design optimization (MDO) problems. This thesis investigates geometrically nonlinear flutter problems and how these should be integrated into aircraft MDO problems. First, flutter problems with and without geometrical nonlinearities are discussed and a unifying interpretation is presented. Furthermore, methods for interpreting nonlinear flutter problems are proposed and differences between linear and nonlinear flutter problem interpretation are discussed. Next, a flutter constraint formulation which accounts for geometrically nonlinear effects using beam-based analyses is presented. The resulting constraint uses a Kreisselmeiser-Steinhauser aggregation function to yield a scalar constraint from flight envelope flutter damping values. While the constraint enforces feasibility over the entire flight envelope, how the flight envelope is sampled largely determines the flutter constraint’s accuracy. To this end, a constrained Maximin approach, which is applicable for non-hypercube spaces, is used to sample the flight envelope and obtain a low-discrepancy sample set. The flutter constraint is then implemented using a beam-based geometrically nonlinear aeroelastic simulation code, UM/NAST. As gradient-based optimization methods are used in MDO due to the large number of design variables in aircraft design problems, the flutter constraint requires the recovery of flutter damping sensitivities. These are obtained by applying algorithmic differentiation (AD) to the UM/NAST code base. This enables the recovery of gradients for any solution type (static, modal, dynamic, and flutter/stability) with respect to any local design variable available within UM/NAST. The performance of the gradient prediction is studied and a hybrid primal-AD scheme is developed to obtain the coupled nonlinear aeroelastic sensitivities. After verifying the accuracy and performance of the gradient evaluation, the flutter constraint was implemented in a sample optimization problem. Finally, a roadmap for including the beam-based flutter constraint within an aircraft design problem is presented using analyses of varying fidelity. To this end, analyses of appropriate fidelity are used depending on the output of interest. While a shell-based FEM model can recover stress distributions, and is therefore well-suited for strength constraints, they are ill-suited for geometrically nonlinear flutter constraints due to their computational cost. Analyses are presented for a high aspect ratio transport aircraft configuration to illustrate the proposed approach and highlight the necessity for the inclusion of a geometrically nonlinear flutter constraint.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163259/1/clupp_1.pd

Aeronautical engineering: A continuing bibliography with indexes (supplement 272)

This bibliography lists 719 reports, articles, and other documents introduced into the NASA scientific and technical information system in November, 1991. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Disruption Response Support For Inland Waterway Transportation

Motivated by the critical role of the inland waterways in the United States\u27 transportation system, this dissertation research focuses on pre- and post- disruption response support when the inland waterway navigation system is disrupted by a natural or manmade event. Following a comprehensive literature review, four research contributions are achieved. The first research contribution formulates and solves a cargo prioritization and terminal allocation problem (CPTAP) that minimizes total value loss of the disrupted barge cargoes on the inland waterway transportation system. It is tailored for maritime transportation stakeholders whose disaster response plans seek to mitigate negative economic and societal impacts. A genetic algorithm (GA)-based heuristic is developed and tested to solve realistically-sized instances of CPTAP. The second research contribution develops and examines a tabu search (TS) heuristic as an improved solution approach to CPTAP. Different from GA\u27s population search approach, the TS heuristic uses the local search to find improved solutions to CPTAP in less computation time. The third research contribution assesses cargo value decreasing rates (CVDRs) through a Value-focused Thinking based methodology. The CVDR is a vital parameter to the general cargo prioritization modeling as well as specifically for the CPTAP model for inland waterways developed here. The fourth research contribution develops a multi-attribute decision model based on the Analytic Hierarchy Process that integrates tangible and intangible factors in prioritizing cargo after an inland waterway disruption. This contribution allows for consideration of subjective, qualitative attributes in addition to the pure quantitative CPTAP approach explored in the first two research contributions

The 1991 3rd NASA Symposium on VLSI Design

Papers from the symposium are presented from the following sessions: (1) featured presentations 1; (2) very large scale integration (VLSI) circuit design; (3) VLSI architecture 1; (4) featured presentations 2; (5) neural networks; (6) VLSI architectures 2; (7) featured presentations 3; (8) verification 1; (9) analog design; (10) verification 2; (11) design innovations 1; (12) asynchronous design; and (13) design innovations 2