Airline Disruption Recovery Using Symbiotic Simulation and Multi-fidelity Modelling

The airlines industry is prone to disruption due to various causes. Whilst an airline may not be able to control the causes of disruption, it can reduce the impact of a disruptive event, such as a mechanical failure, with its response by revising the schedule. Potential actions include swapping aircraft, delaying flights and cancellations. This paper will present our research into how symbiotic simulation could potentially be used to improve the response to a disruptive event by evaluating potential revised schedules. Due to the large solution space, exhaustive searches are infeasible. Our research is investigating the use of multi-fidelity models to help guide the search of the optimisation algorithm, leading to good solutions being generated within the time constraints of disruption management

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%

A proposed psychological model of driving automation

This paper considers psychological variables pertinent to driver automation. It is anticipated that driving with automated systems is likely to have a major impact on the drivers and a multiplicity of factors needs to be taken into account. A systems analysis of the driver, vehicle and automation served as the basis for eliciting psychological factors. The main variables to be considered were: feed-back, locus of control, mental workload, driver stress, situational awareness and mental representations. It is expected that anticipating the effects on the driver brought about by vehicle automation could lead to improved design strategies. Based on research evidence in the literature, the psychological factors were assembled into a model for further investigation

Mining Urban Heat

This study is about realigning the demand of energy in urban environments to take advantage of the excess energy from passive and mechanical sources - it is about mining urban heat. My interests in this topic lies in the possibility of designing an ecology of urban spaces with symbiotic relationships between thermal supply and demand. To this end, the thesis begins by mapping the availability of secondary heat from solar radiant sources, and building process systems, showing that an estimated 28.285 Trillion British Thermal Units (BTU) of heat are available for reuse in New York City - this is equal to 52 percent of the energy produced at the Indian Point Power Station in one year. If one hundred percent of this secondary heat could be captured and reused New York City could save up to 182 billion dollars in electricity purchase per year. The study explores how zoning and building code could be altered to capture these savings, eventually creating cities with net zero secondary heat production. Design proposals for a net-zero secondary heat city include co-location of building uses based on complementary heating demand schedules, distribution networks for available low grade secondary heat and consideration of viable end uses for this low grade heat, de-centralizing industries that regularly use large quantities of high grade heat to serve as neighborhood hearths of secondary heat production, and establishment of heat sinks by neighborhood in the form of open space. The thesis explores the technical, spatial and policy implications of these ideas in selected New York City neighborhoods

Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains

Urban heritage endangerment at the interface of future cities and past heritage: A spatial vulnerability assessment

Uncontrolled urban growth has been an increasing concern in many regions throughout the world. Generated by a natural tendency of population growth in relation to unsustainable land use, city sprawl has led to complex spatial developments that are creating both benefits to, and challenges for decision makers. A major problem inherent in the uncontrolled growth of cities is the threat to the fragile cultural and ecological heritage, which may escalate to permanent and irreversible damage as a result of factors such as environmental depletion and landscape decay. Using modern geosciences and spatial information technologies as predictive tools to analyse and forecast urban growth, a regional spatial decision system may be useful in order to provide seemly and timely information on the risk of overburdening the carrying capacity regarding the historico-cultural heritage at local and regional levels.The present paper develops a predictive toolkit for urban heritage in relation to urban cultural endangerment. This common problem is shared through many regions of the world and is increasingly jeopardizing fragile archaeological landscape due to urban pressure. In this sense, and to forecast an example of this common pressure, the Algarve is exemplified as a laboratory for testing this novel methodology, relying on a combined analysis of urban growth potential and threats to the abundant presence of archaeological heritage in the area. Our appro ach supports the paradigm of city growth in the context of a common agenda emerging from the Valetta Treaty, in which preserving the archaeological heritage is recognized as a key element for sustainable development. The study provides novel empirical results from the above mentioned modelling approach, with important lessons for the developing world. This paper proposes as such, an integrative spatial analysis methodology on the issue of historico-cultural endangerment, which is a new approach to comparative spatial analysis for decision making on urban heritage endangerment at the regional scale. Later, the discussion extends to a more conceptual level of urban planning by considering the questions: Is urban sprawl influencing the way we perceive cities? If so, are there positive advantages in the paradigm of urban growth and urban sprawl which might help us to protect past heritage while ensuring sustainable and modern cities? © 2011 Elsevier Ltd

SCS: 60 years and counting! A time to reflect on the Society's scholarly contribution to M&S from the turn of the millennium.

The Society for Modeling and Simulation International (SCS) is celebrating its 60th anniversary this year. Since its inception, the Society has widely disseminated the advancements in the field of modeling and simulation (M&S) through its peer-reviewed journals. In this paper we profile research that has been published in the journal SIMULATION: Transactions of the Society for Modeling and Simulation International from the turn of the millennium to 2010; the objective is to acknowledge the contribution of the authors and their seminal research papers, their respective universities/departments and the geographical diversity of the authors' affiliations. Yet another objective is to contribute towards the understanding of the overall evolution of the discipline of M&S; this is achieved through the classification of M&S techniques and its frequency of use, analysis of the sectors that have seen the predomination application of M&S and the context of its application. It is expected that this paper will lead to further appreciation of the contribution of the Society in influencing the growth of M&S as a discipline and, indeed, in steering its future direction