Applications of stochastic modeling in air traffic management:Methods, challenges and opportunities for solving air traffic problems under uncertainty

In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management

Solvability in Discrete, Nonstationary, Infinite Horizon Optimization

For several time-staged operations management problems, the optimal immediate decision is dependent on the choice of problem horizon. When that horizon is very long or indefinite, an appropriate modeling technique is infinite horizon optimization. For problems that have stationary data over time, optimizing system performance over an infinite horizon is generally no more difficult than optimizing over a finite horizon. However, restricting problem data to be stationary can render the models unrealistic, failing to include nonstationary aspects of the real world. The primary difficulty in nonstationary, infinite horizon optimization is that the problem to solve can never be known in its entirety. Thus, solution techniques must rely upon increasingly longer finite horizon problems. Ideally, the optimal immediate decisions to these finite horizon problems converge to an infinite horizon optimum. When finite detection of that optimal decision is possible, we call the underlying infinite horizon problem well-posed. The literature on nonstationary, infinite horizon optimization has generally relied upon either uniqueness of the optimal immediate decision or monotonicity of that decision as a function of horizon length. In this thesis, we require neither of these, instead developing a more general structural condition called coalescence that is equivalent to well-posedness. Chapters 2-4 study infinite horizon variants of three deterministic optimization applications: concave cost production planning, single machine replacement, and capacitated inventory planning. For each problem, we show that coalescence is equivalent to well-posedness. We also give a solution procedure for each application that will uncover an infinite horizon optimal immediate decision for any well-posed problem. In Chapter 5, we generalize the results of these applications to a generic classes of optimization problems expressible as dynamic programs. Under two different sets of assumptions concerning the finiteness of and reachability between states, we show that coalescence and well-posedness are equivalent. We also give solution procedures that solve any well-posed problem under each set of assumptions. Finally, in Chapter 6, we introduce a stochastic application: the infinite horizon asset selling problem, and again show that coalescence and well-posedness are equivalent and give a solution procedure to solve any such well-posed problem.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60810/1/tlortz_1.pd

Detecting demand outliers in transport systems

Optimisation routines used for demand management in transport systems strongly depend on accurate forecasts. Outliers caused by systematic shifts in demand cause erroneous forecasts for both current services and future services whose forecasts are based on historic demand. Transport service providers often rely on analysts to identify outlier demand and make adjustments accordingly. However, previous research on judgemental forecasting shows that such adjustments can be biased and even superfluous. Literature on automated detection and evaluation of outlier demand in this context is scarce. To date, most literature on forecasting and optimisation in transport planning does not account for demand outliers despite the negative impacts it can have. This thesis presents a novel methodology, which combines network clustering with functional data analysis and time series forecasting, to detect outliers in demand for transport systems. This thesis also contributes a simulation framework for evaluating the performance of the proposed outlier detection procedure and for quantifying the effects of outlier demand on different optimisation routines. The use of such a method as a decision support tool for analyst adjusted forecasts, and how the outlier alerts may be best communicated, is also considered. Computational studies highlight the benefits of different adjustments that analysts may take after the identification of outlier demand. Multiple empirical studies will demonstrate how the method can be applied in practice to different types of transport systems, with analyses of Deutsche Bahn railway booking data and Capital Bikeshare usage data

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

Modeling Energy Demand—A Systematic Literature Review

In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.BMBF, 03SFK4T0, Verbundvorhaben ENavi: Energiewende-Navigationssystem zur Erfassung, Analyse und Simulation der systemischen Vernetzungen" - Teilvorhaben T0BMWi, 03ET4040C, Verbundvorhaben: Harmonisierung und Entwicklung von Verfahren zur regional und zeitlich aufgelösten Modellierung von Energienachfragen (DemandRegio) Teilvorhaben: ProfileDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Data Science in Energy and Environmental Systems With Multiple Data Sources

The energy sector in modern society is undergoing a rapid transformation from fuel-based generation to renewable generation. Further, distributed supply and demand, grid-responsive demand management, and other complex technology increasingly rely on environmental data throughout the energy supply chain, from power production to end uses. However, data analysis in energy systems presents major technical challenges, including spatio-temporal heterogeneity, localized characteristics, and disparate data sources. This dissertation study aims to design data science solutions that address some of these challenges and model the dynamics of ambient environmental conditions that are closely tied with energy system operations. Climate conditions are often temporally and spatially correlated and exhibit a non-stationary nature, constantly changing all the time. To fully characterize these characteristics, this study utilizes the rich data available from multiple sources, including data collected at spatially distributed locations and data generated from disparate sources, e.g., field meteorological data and physics-based numerical weather prediction data. This dissertation initiates two major ideas: (a) making use of data collected from multiple spatially dispersed locations; and (b) integrating data generated from physics-based numerical weather prediction models with actual climate measurements through a linkage function. Specifically, the following three research topics are investigated. The first study develops a probabilistic model for assessing wind resources at a target location by utilizing wind data collected at nearby meteorological stations. By quantifying daily and spatially correlated diurnal patterns of the wind speed at multiple locations, the developed integrative approach provides compelling capabilities for evaluating the wind variability at non-observational locations, while quantifying prediction uncertainties. The results will provide rich information for deciding suitable wind farm sites. Next, we make use of temperature projections from physics-based global climate models for the purpose of long-term electricity load predictions. While the physics-based climate models can provide useful climate projections in the long run, they inevitably exhibit systematic discrepancies (also called `bias'), compared to actual climate conditions, because of incomplete characterization of local or regional variations. We calibrate the climate model projections to address possible biases and provide a long-term density prediction of peak electricity load. The results provide useful insights on how the daily peak demand densities would change over time, in response to climate change and other socio-economic factors. Finally, we present another bias correction model that quantifies the spatially and temporally correlated bias from the physics-based numerical model output for urban temperature modeling. By combining both types of data, our approach can successfully characterize localized environmental variations over space and time and greatly improve the prediction accuracy compared to that of the original physics-based numerical model. hl{The proposed approach helps understanding temperature variations over dispersed locations, depending on urbanization intensity. Such results can be useful for predicting electricity demand and effectively managing power system operations such as demand response programs.} The advantages of all proposed approaches are demonstrated with case studies using actual data. The results validate that the proposed approaches successfully address some of the challenges discussed above that arise in energy and environmental systems.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169899/1/mapsossa_1.pd

Mergers and acquisitions transactions strategies in diffusion - type financial systems in highly volatile global capital markets with nonlinearities

The M and A transactions represent a wide range of unique business optimization opportunities in the corporate transformation deals, which are usually characterized by the high level of total risk. The M and A transactions can be successfully implemented by taking to an account the size of investments, purchase price, direction of transaction, type of transaction, and using the modern comparable transactions analysis and the business valuation techniques in the diffusion type financial systems in the finances. We developed the MicroMA software program with the embedded optimized near-real-time artificial intelligence algorithm to create the winning virtuous M and A strategies, using the financial performance characteristics of the involved firms, and to estimate the probability of the M and A transaction completion success. We believe that the fluctuating dependence of M and A transactions number over the certain time period is quasi periodic. We think that there are many factors, which can generate the quasi periodic oscillations of the M and A transactions number in the time domain, for example: the stock market bubble effects. We performed the research of the nonlinearities in the M and A transactions number quasi-periodic oscillations in Matlab, including the ideal, linear, quadratic, and exponential dependences. We discovered that the average of a sum of random numbers in the M and A transactions time series represents a time series with the quasi periodic systematic oscillations, which can be finely approximated by the polynomial numbers. We think that, in the course of the M and A transaction implementation, the ability by the companies to absorb the newly acquired knowledge and to create the new innovative knowledge bases, is a key predeterminant of the M and A deal completion success as in Switzerland.Comment: 160 pages, 9 figures, 37 table

Response times in healthcare systems

It is a goal universally acknowledged that a healthcare system should treat its patients – and especially those in need of critical care – in a timely manner. However, this is often not achieved in practice, particularly in state-run public healthcare systems that suffer from high patient demand and limited resources. In particular, Accident and Emergency (A&E) departments in England have been placed under increasing pressure, with attendances rising year on year, and a national government target whereby 98% of patients should spend 4 hours or less in an A&E department from arrival to admission, transfer or discharge. This thesis presents techniques and tools to characterise and forecast patient arrivals, to model patient flow and to assess the response-time impact of different resource allocations, patient treatment schemes and workload scenarios. Having obtained ethical approval to access five years of pseudonymised patient timing data from a large case study A&E department, we present a number of time series models that characterise and forecast daily A&E patient arrivals. Patient arrivals are classified as one of two arrival streams (walk-in and ambulance) by mode of arrival. Using power spectrum analysis, we find the two arrival streams exhibit different statistical properties and hence require separate time series models. We find that structural time series models best characterise and forecast walk-in arrivals, but that time series analysis may not be appropriate for ambulance arrivals; this prompts us to investigate characterisation by a non-homogeneous Poisson process. Next we present a hierarchical multiclass queueing network model of patient flow in our case study A&E department. We investigate via a discrete-event simulation the impact of class and time-based priority treatment of patients, and compare the resulting service-time densities and moments with actual data. Then, by performing bottleneck analysis and investigating various workload and resource scenarios, we pinpoint the resources that have the greatest impact on mean service times. Finally we describe an approximate generating function analysis technique which efficiently approximates the first two moments of customer response time in class-dependent priority queueing networks with population constraints. This technique is applied to the model of A&E and the results compared with those from simulation. We find good agreement for mean service times especially when minors patients are given priority