Combinatorial and Robust Optimisation Models and Algorithms for Railway Applications

This thesis deals with an investigation of combinatorial and robust optimisation models to solve railway problems. Railway applications represent a challenging area for operations research. In fact, most problems in this context can be modelled as combinatorial optimisation problems, in which the number of feasible solutions is finite. Yet, despite the astonishing success in the field of combinatorial optimisation, the current state of algorithmic research faces severe difficulties with highly-complex and data-intensive applications such as those dealing with optimisation issues in large-scale transportation networks. One of the main issues concerns imperfect information. The idea of Robust Optimisation, as a way to represent and handle mathematically systems with not precisely known data, dates back to 1970s. Unfortunately, none of those techniques proved to be successfully applicable in one of the most complex and largest in scale (transportation) settings: that of railway systems. Railway optimisation deals with planning and scheduling problems over several time horizons. Disturbances are inevitable and severely affect the planning process. Here we focus on two compelling aspects of planning: robust planning and online (real-time) planning.

Sustainable urban infrastructure management : integration of urban computer modelling

Purpose - This paper seeks to examine the complex relationships between urban planning, infrastructure management, sustainable urban development, and to illustrate why there is an urgent need for local governments to develop a robust planning support system which integrates with advance urban computer modelling tools to facilitate better infrastructure management and improve knowledge sharing between the community, urban planners, engineers and decision makers. Design/methodology/approach - The methods used in this paper includes literature review and practical project case observations. Originality/value - This paper provides an insight of how the Brisbane's planning support system established by Brisbane City Council has significantly improved the effectiveness of urban planning, infrastructure management and community engagement through better knowledge management processes. Practical implications - This paper presents a practical framework for setting up a functional planning support system within local government. The integration of the Brisbane Urban Growth model, Virtual Brisbane and the Brisbane Economic Activity Monitoring (BEAM) database have proven initially successful to provide a dynamic platform to assist elected officials, planners and engineers to understand the limitations of the local environment, its urban systems and the planning implications on a city. With the Brisbane's planning support system, planners and decision makers are able to provide better planning outcomes, policy and infrastructure that adequately address the local needs and achieve sustainable spatial forms.

Conversation, community and kids: blogging for youth at Gold Coast City Council libraries

In late 2007, Gold Coast City Council libraries embarked on an online library project, designed to ramp up libraries’ online services to customers. As part of this project, the Young People’s team identified a need to connect with youth aged 12 to 16 in the online environment, in order to create a direct channel of communication with this market segment and encourage them to engage with the library. Blogging was identified as an appropriate means of communicating with both current and potential library customers from this age group. The Young People’s team consequently prepared a concept plan for a youth blog for launch in Children’s Book Week 2008 and are working towards development of management and administrative models and documentation and implementation of the blog itself. While many libraries have been quick to take up Web 2.0-style services, there has been little formal publication about the successes (or failures) of this type of project. Likewise, few libraries have published about the planning, management, and administration of such services. The youth blog currently in development at Gold Coast City Council libraries will be supported by a robust planning phase and will be rigorously evaluated as part of the project. This paper will report on the project (its aims, objectives and outputs), the planning process, and the evaluation activities and outcomes.

Efficient solutions to factored MDPs with imprecise transition probabilities

When modeling real-world decision-theoretic planning problems in the Markov Decision Process (MDP) framework, it is often impossible to obtain a completely accurate estimate of transition probabilities. For example, natural uncertainty arises in the transition specification due to elicitation of MOP transition models from an expert or estimation from data, or non-stationary transition distributions arising from insufficient state knowledge. In the interest of obtaining the most robust policy under transition uncertainty, the Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) has been introduced to model such scenarios. Unfortunately, while various solution algorithms exist for MDP-IPs, they often require external calls to optimization routines and thus can be extremely time-consuming in practice. To address this deficiency, we introduce the factored MDP-IP and propose efficient dynamic programming methods to exploit its structure. Noting that the key computational bottleneck in the solution of factored MDP-IPs is the need to repeatedly solve nonlinear constrained optimization problems, we show how to target approximation techniques to drastically reduce the computational overhead of the nonlinear solver while producing bounded, approximately optimal solutions. Our results show up to two orders of magnitude speedup in comparison to traditional ""flat"" dynamic programming approaches and up to an order of magnitude speedup over the extension of factored MDP approximate value iteration techniques to MDP-IPs while producing the lowest error of any approximation algorithm evaluated. (C) 2011 Elsevier B.V. All rights reserved.

Robustness and Recoverability in Transport Logistics

Abstract Transport is the foundation of any economy: it boosts economic growth, creates wealth, enhances trade, geographical accessibility and the mobility of people. Transport is also a key ingredient for a high quality of life, making places accessible and bringing people together. The future prosperity of our world will depend on the ability of all of its regions to remain fully and competitively integrated in the world economy. Efficient transport is vital in making this happen. Operations research can help in efficiently planning the design and operating transport systems. Planning and operational processes are fields that are rich in combinatorial optimization problems. These problems can be analyzed and solved through the application of mathematical models and optimization techniques, which may lead to an improvement in the performance of the transport system, as well as to a reduction in the time required for solving these problems. The latter aspect is important, because it increases the flexibility of the system: the system can adapt in a faster way to changes in the environment (i.e.: weather conditions, crew illness, failures, etc.). These disturbing changes (called disruptions) often enforce the schedule to be adapted. The direct consequences are delays and cancellations, implying many schedule adjustments and huge costs. Consequently, robust schedules and recovery plans must be developed in order to fight against disruptions. This dissertation makes contributions to two different fields: rail and air applications. Robust planning and recovery methods are presented. In the field of railway transport we develop several mathematical models which answer to RENFE’s (the major railway operator in Spain) needs: 1. We study the rolling stock assignment problem: here, we introduce some robust aspects in order to ameliorate some operations which are likely to fail. Once the rolling stock assignment is known, we propose a robust routing model which aims at identifying the train units’ sequences while minimizing the expected delays and human resources needed to perform the sequences. 2. It is widely accepted that the sequential solving approach produces solutions that are not global optima. Therefore, we develop an integrated and robust model to determine the train schedule and rolling stock assignment. We also propose an integrated model to study the rolling stock circulations. Circulations are determined by the rolling stock assignment and routing of the train units. 3. Although our aim is to develop robust plans, disruptions will be likely to occur and recovery methods will be needed. Therefore, we propose a recovery method which aims to recover the train schedule and rolling stock assignment in an integrated fashion all while considering the passenger demand. In the field of air transport we develop several mathematical models which answer to IBERIA’s (the major airline in Spain) needs: 1. We look at the airline-scheduling problem and develop an integrated approach that optimizes schedule design, fleet assignment and passenger use so as to reduce costs and create fewer incompatibilities between decisions. Robust itineraries are created to ameliorate misconnected passengers. 2. Air transport operators are continuously facing competition from other air operators and different modes of transport (e.g., High Speed Rail). Consequently, airline profitability is critically influenced by the airline’s ability to estimate passenger demands and construct profitable flight schedules. We consider multi-modal competition including airline and rail, and develop a new approach that estimates the demand associated with a given schedule; and generates airline schedules and fleet assignments using an integrated schedule design and fleet assignment optimization model that captures the impacts of schedule decisions on passenger demand.

DEVELOPMENT OF A BEAM-SPECIFIC PLANNING TARGET VOLUME AND A ROBUST PLAN ANALYSIS TOOL FOR PROTON THERAPY

Proton therapy is growing increasingly popular due to its superior dose characteristics compared to conventional photon therapy. Protons travel a finite range in the patient body and stop, thereby delivering no dose beyond their range. However, because the range of a proton beam is heavily dependent on the tissue density along its beam path, uncertainties in patient setup position and inherent range calculation can degrade thedose distribution significantly. Despite these challenges that are unique to proton therapy, current management of the uncertainties during treatment planning of proton therapy has been similar to that of conventional photon therapy. The goal of this dissertation research was to develop a treatment planning method and a planevaluation method that address proton-specific issues regarding setup and range uncertainties. Treatment plan designing method adapted to proton therapy: Currently, for proton therapy using a scanning beam delivery system, setup uncertainties are largely accounted for by geometrically expanding a clinical target volume (CTV) to a planning target volume (PTV). However, a PTV alone cannot adequately account for range uncertainties coupled to misaligned patient anatomy in the beam path since it does not account for the change in tissue density. In order to remedy this problem, we proposed a beam-specific PTV (bsPTV) that accounts for the change in tissue density along the beam path due to the uncertainties. Our proposed method was successfully implemented, and its superiority over the conventional PTV was shown through a controlled experiment.. Furthermore, we have shown that the bsPTV concept can be incorporated into beam angle optimization for better target coverage and normal tissue sparing for a selected lung cancer patient. Treatment plan evaluation method adapted to proton therapy: The dose-volume histogram of the clinical target volume (CTV) or any other volumes of interest at the time of planning does not represent the most probable dosimetric outcome of a given plan as it does not include the uncertainties mentioned earlier. Currently, the PTV is used as a surrogate of the CTV’s worst case scenario for target dose estimation. However, because proton dose distributions are subject to change under these uncertainties, the validity of the PTV analysis method is questionable. In order to remedy this problem, we proposed the use of statistical parameters to quantify uncertainties on both the dose-volume histogram and dose distribution directly. The robust plan analysis tool was successfully implemented to compute both the expectation value and its standard deviation of dosimetric parameters of a treatment plan under the uncertainties. For 15 lung cancer patients, the proposed method was used to quantify the dosimetric difference between the nominal situation and its expected value under the uncertainties.

A robust reactive scheduling model for intensive care units

The ICU is an integral part of any hospital and is under great load from patient arrivals as well as resource limitations. Scheduling of patients in the ICU is complicated by the two general types; elective surgery and emergency arrivals. This complicated situation is handled by creating a tentative initial schedule and then reacting to uncertain arrivals as they occur. For most hospitals there is little or no flexibility in the number of beds that are available for use now or in the future. We propose an integer programming model to handle a parallel machine reacting system for scheduled and unscheduled arrivals.

Ubiquitous and smart system approaches to infrastructure planning : learnings from Korea, Japan and Hong Kong

The robust economic growth across South East Asia and the significant advances in nano-technologies in the past two decades have resulted in the creation of intelligent urban infrastructures. Cities like Seoul, Tokyo and Hong Kong have been competing against each other to develop the first ‘ubiquitous city’, a strategic global node of science and technology that provides all municipal services for residents and visitors via ubiquitous infrastructures. This chapter scrutinises the development of ubiquitous and smart infrastructure in Korea, Japan and Hong Kong. These cases provide invaluable learnings for policy-makers and urban and infrastructure planners when considering adopting these systems approaches in their cities.

Robust tracking control of autonomous underwater vehicles in the presence of disturbance inputs

An autonomous underwater vehicle (AUV) is expected to operate in an ocean in the presence of poorly known disturbance forces and moments. The uncertainties of the environment makes it difficult to apply open-loop control scheme for the motion planning of the vehicle. The objective of this paper is to develop a robust feedback trajectory tracking control scheme for an AUV that can track a prescribed trajectory amidst such disturbances. We solve a general problem of feedback trajectory tracking of an AUV in SE(3). The feedback control scheme is derived using Lyapunov-type analysis. The results obtained from numerical simulations confirm the asymptotic tracking properties of the feedback control law. We apply the feedback control scheme to different mission scenarios, with the disturbances being initial errors in the state of the AUV.

Path planning, guidance and control for a UAV forced landing

A forced landing is an unscheduled event in flight requiring an emergency landing, and is most commonly attributed to engine failure, failure of avionics or adverse weather. Since the ability to conduct a successful forced landing is the primary indicator for safety in the aviation industry, automating this capability for unmanned aerial vehicles (UAVs) will help facilitate their integration into, and subsequent routine operations over civilian airspace. Currently, there is no commercial system available to perform this task; however, a team at the Australian Research Centre for Aerospace Automation (ARCAA) is working towards developing such an automated forced landing system. This system, codenamed Flight Guardian, will operate onboard the aircraft and use machine vision for site identification, artificial intelligence for data assessment and evaluation, and path planning, guidance and control techniques to actualize the landing. This thesis focuses on research specific to the third category, and presents the design, testing and evaluation of a Trajectory Generation and Guidance System (TGGS) that navigates the aircraft to land at a chosen site, following an engine failure. Firstly, two algorithms are developed that adapts manned aircraft forced landing techniques to suit the UAV planning problem. Algorithm 1 allows the UAV to select a route (from a library) based on a fixed glide range and the ambient wind conditions, while Algorithm 2 uses a series of adjustable waypoints to cater for changing winds. A comparison of both algorithms in over 200 simulated forced landings found that using Algorithm 2, twice as many landings were within the designated area, with an average lateral miss distance of 200 m at the aimpoint. These results present a baseline for further refinements to the planning algorithms. A significant contribution is seen in the design of the 3-D Dubins Curves planning algorithm, which extends the elementary concepts underlying 2-D Dubins paths to account for powerless flight in three dimensions. This has also resulted in the development of new methods in testing for path traversability, in losing excess altitude, and in the actual path formation to ensure aircraft stability. Simulations using this algorithm have demonstrated lateral and vertical miss distances of under 20 m at the approach point, in wind speeds of up to 9 m/s. This is greater than a tenfold improvement on Algorithm 2 and emulates the performance of manned, powered aircraft. The lateral guidance algorithm originally developed by Park, Deyst, and How (2007) is enhanced to include wind information in the guidance logic. A simple assumption is also made that reduces the complexity of the algorithm in following a circular path, yet without sacrificing performance. Finally, a specific method of supplying the correct turning direction is also used. Simulations have shown that this new algorithm, named the Enhanced Nonlinear Guidance (ENG) algorithm, performs much better in changing winds, with cross-track errors at the approach point within 2 m, compared to over 10 m using Park's algorithm. A fourth contribution is made in designing the Flight Path Following Guidance (FPFG) algorithm, which uses path angle calculations and the MacCready theory to determine the optimal speed to fly in winds. This algorithm also uses proportional integral- derivative (PID) gain schedules to finely tune the tracking accuracies, and has demonstrated in simulation vertical miss distances of under 2 m in changing winds. A fifth contribution is made in designing the Modified Proportional Navigation (MPN) algorithm, which uses principles from proportional navigation and the ENG algorithm, as well as methods specifically its own, to calculate the required pitch to fly. This algorithm is robust to wind changes, and is easily adaptable to any aircraft type. Tracking accuracies obtained with this algorithm are also comparable to those obtained using the FPFG algorithm. For all three preceding guidance algorithms, a novel method utilising the geometric and time relationship between aircraft and path is also employed to ensure that the aircraft is still able to track the desired path to completion in strong winds, while remaining stabilised. Finally, a derived contribution is made in modifying the 3-D Dubins Curves algorithm to suit helicopter flight dynamics. This modification allows a helicopter to autonomously track both stationary and moving targets in flight, and is highly advantageous for applications such as traffic surveillance, police pursuit, security or payload delivery. Each of these achievements serves to enhance the on-board autonomy and safety of a UAV, which in turn will help facilitate the integration of UAVs into civilian airspace for a wider appreciation of the good that they can provide. The automated UAV forced landing planning and guidance strategies presented in this thesis will allow the progression of this technology from the design and developmental stages, through to a prototype system that can demonstrate its effectiveness to the UAV research and operations community.

Using the Precede-Proceed Model of Health Program Planning in breast cancer nursing research

Aim: In this paper we discuss the use of the Precede-Proceed model when investigating health promotion options for breast cancer survivors. Background: Adherence to recommended health behaviors can optimize well-being after cancer treatment. Guided by the Precede-Proceed approach, we studied the behaviors of breast cancer survivors in our health service area. Data sources: The interview data from the cohort of breast cancer survivors are used in this paper to illustrate the use of Precede-Proceed in this nursing research context. Interview data were collected from June to December 2009. We also searched Medline, CINAHL, PsychInfo and PsychExtra up to 2010 for relevant literature in English to interrogate the data from other theoretical perspectives. Discussion: The Precede-Proceed model is theoretically-complex. The deductive analytic process guided by the model usefully explained some of the health behaviors of cancer survivors, although it could not explicate many other findings. A complementary inductive approach to the analysis and subsequent interpretation by way of Uncertainty in Illness Theory and other psychosocial perspectives provided a comprehensive account of the qualitative data that resulted in contextually-relevant recommendations for nursing practice. Implications for nursing: Nursing researchers using Precede-Proceed should maintain theoretical flexibility when interpreting qualitative data. Perspectives not embedded in the model might need to be considered to ensure that the data are analyzed in a contextually-relevant way. Conclusion: Precede-Proceed provides a robust framework for nursing researchers investigating health promotion in cancer survivors; however additional theoretical lenses to those embedded in the model can enhance data interpretation.

Wind-energy based path planning for electric unmanned aerial vehicles using Markov Decision Processes

Exploiting wind-energy is one possible way to ex- tend flight duration for Unmanned Arial Vehicles. Wind-energy can also be used to minimise energy consumption for a planned path. In this paper, we consider uncertain time-varying wind fields and plan a path through them. A Gaussian distribution is used to determine uncertainty in the Time-varying wind fields. We use Markov Decision Process to plan a path based upon the uncertainty of Gaussian distribution. Simulation results that compare the direct line of flight between start and target point and our planned path for energy consumption and time of travel are presented. The result is a robust path using the most visited cell while sampling the Gaussian distribution of the wind field in each cell.

Wind-energy based path planning for unmanned aerial vehicles using Markov decision processes

Exploiting wind-energy is one possible way to extend flight duration for Unmanned Arial Vehicles. Wind-energy can also be used to minimise energy consumption for a planned path. In this paper, we consider uncertain time-varying wind fields and plan a path through them. A Gaussian distribution is used to determine uncertainty in the Time-varying wind fields. We use Markov Decision Process to plan a path based upon the uncertainty of Gaussian distribution. Simulation results that compare the direct line of flight between start and target point and our planned path for energy consumption and time of travel are presented. The result is a robust path using the most visited cell while sampling the Gaussian distribution of the wind field in each cell.

Land use planning and the airport metropolis

Australian airports have emerged as important urban activity centres over the past decade as a result of privatisation. A range of reciprocal airport and regional impacts now pose considerable challenges for both airport operation and the surrounding urban and regional environment. The airport can no longer be managed solely as a specialised transport entity in isolation from the metropolis that it serves. In 2007 a multidisciplinary Australian Research Council Linkage Project (LP 0775225) was funded to investigate the changing role of airports in Australia. This thesis is but one component of this collaborative research effort. Here the issues surrounding the policy and practice of airport and regional land use planning are explored, analysed and detailed. This research, for the first time, assembles a distinct progression of the wider social, economic, technological and environmental roles of the airport within the Australian airport literature from 1914 – 2011. It recognises that while the list of airport and regional impacts has grown through time, treatment within practice and the literature has largely remained highly specialised and contained within disciplinary paradigms. The first publication of the thesis (Chapter 2) acknowledges that the changing role of airports demands the establishment of new models of airport planning and development. It argues that practice and research requires a better understanding of the reciprocal impacts of airports and their urban catchments. The second publication (Chapter 3) highlights that there is ad hoc examination and media attention of high profile airport and regional conflict, but little empirical analysis or understanding of the extent to which all privatised Australian airports are intending to develop. The conceptual and methodological significance of this research is the development of a national land use classification system for on-airport development. This paper establishes the extent of on-airport development in Australia, providing insight into the changing land use and economic roles of privatised airports. The third publication (Chapter 4) details new and significant interdependencies for airport and regional development in consideration of the progression of airports as activity centres. Here the model of an ‘airport metropolis’ is offered as an organising device and theoretical contribution for comprehending the complexity and planning of airport and regional development. It delivers a conceptual framework for both research and policy, which acknowledges the reciprocal impacts of economic development, land use, infrastructure and governance ‘interfaces’. In a timely and significant concurrence with this research the Australian Government announced and delivered a National Aviation Policy Review (2008 – 2009). As such the fourth publication (Chapter 5) focuses on the airport and urban planning aspects of the review. This paper also highlights the overall policy intention of facilitating broader airport and regional collaborative processes. This communicative turn in airport policy is significant in light of the communicative theoretical framework of the thesis. The fifth paper of the thesis (Chapter 6) examines three Australian case studies (Brisbane, Adelaide and Canberra) to detail the context of airport and regional land use planning and to apply the airport metropolis model as a framework for research. Through the use of Land Use Forums, over 120 airport and regional stakeholders are brought together to detail their perspectives and interactions with airport and regional land use planning. An inductive thematic analysis of the results identifies three significant themes which contribute to the fragmentation of airport and regional and land use planning: 1) inadequate coordination and disjointed decision-making; 2) current legislative and policy frameworks; and 3) competing stakeholder priorities and interests. Building on this new knowledge, Chapter 7 details the perceptions of airport and local, state and territory government stakeholders to land use relationships, processes and outcomes. A series of semi-structured interviews are undertaken in each of the case studies to inform this research. The potential implications for ongoing communicative practice are discussed in conclusion. The following thesis represents an incremental and cumulative research process which delivers new knowledge for the practical understanding and research interpretation of airport and regional land use planning practice and policy. It has developed and applied a robust conceptual framework which delivers significant direction for all stakeholders to better comprehend the relevance of airports in the urban character and design of our cities.