Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Large-Scale Solution Approaches for Healthcare and Supply Chain Scheduling

This research proposes novel solution techniques for two real world problems. We first consider a patient scheduling problem in a proton therapy facility with deterministic patient arrivals. In order to assess the impacts of several operational constraints, we propose single and multi-criteria linear programming models. In addition, we ensure that the strategic patient mix restrictions predetermined by the decision makers are also enforced within the planning horizon. We study the mathematical structures of the single criteria model with strict patient mix restrictions and derive analytical equations for the optimal solutions under several operational restrictions. These efforts lead to a set of rule of thumbs that can be utilized to assess the impacts of several input parameters and patient mix levels on the capacity utilization without solving optimization problems. The necessary and sufficient conditions to analytically generate exact efficient frontiers of the bicriteria problem without any additional side constraint are also explored. In a follow up study, we investigate the solution techniques for the same patient scheduling problem with stochastic patient arrivals. We propose two Markov Decision Process (MDP) models that are capable of tackling the stochasticity. The second problem of interest is a variant of the parallel machine scheduling problem. We propose constraint programming (CP) and logic-based Benders decomposition algorithms in order to make the best decisions for scheduling nonidentical jobs with time windows and sequence dependent setup times on dissimilar parallel machines in a fixed planning horizon. This problem is formulated with (i) maximizing total profit and (ii) minimizing makespan objectives. We conduct several sensitivity analysis to test the quality and robustness of the solutions on a real life case study

Query processing in complex modern traffic networks

The transport sector generates about one quarter of all greenhouse gas emissions worldwide. In the European Union (EU), passenger cars and light-duty trucks make up for over half of these traffic-related emissions. It is evident that everyday traffic is a serious environmental threat. At the same time, transport is a key factor for the ambitious EU climate goals; among them, for instance, the reduction of greenhouse gas emissions by 85 to 90 percent in the next 35 years. This thesis investigates complex traffic networks and their requirements from a computer science perspective. Modeling of and query processing in modern traffic networks are pivotal topics. Challenging theoretical problems are examined from different perspectives, novel algorithmic solutions are provided. Practical problems are investigated and solved, for instance, employing qualitative crowdsourced information and sensor data of various sources. Modern traffic networks are often modeled as graphs, i.e., defined by sets of nodes and edges. In conventional graphs, the edges are assigned numerical weights, for instance, reflecting cost criteria like distance or travel time. In multicriteria networks, the edges reflect multiple, possibly dynamically changing cost criteria. While these networks allow for diverse queries and meaningful insight, query processing usually is significantly more complex. Novel means for computation are required to keep query processing efficient. The crucial task of computing optimal paths is particularly expensive under multiple criteria. The most established set of optimal paths in multicriteria networks is referred to as path skyline (or set of pareto-optimal paths). Until now, computing the path skyline either required extensive precomputation or networks of minor size or complexity. Neither of these demands can be made on modern traffic networks. This thesis presents a novel method which makes on-the-fly computation of path skylines possible, even in dynamic networks with three or more cost criteria. Another problem examined is the exponentially growth of path skylines. The number of elements in a path skyline is potentially exponential in the number of cost criteria and the number of edges between start and target. This often produces less meaningful results, sometimes hindering usability. These drawbacks emphasize the importance of the linear path skyline which is investigated in this thesis. The linear path skyline is based on a different notion of optimality. By the notion of optimality, the linear path skyline is a subset of the conventional path skyline but in general contains less and more diverse elements. Thus, the linear path skyline facilitates interpretation while in general reducing computational effort. This topic is first studied in networks with two cost criteria and subsequently extended to more cost criteria. These cost criteria are not limited to purely quantitative measures like distance and travel time. This thesis examines the integration of qualitative information into abstractly modeled road networks. It is proposed to mine crowdsourced data for qualitative information and use this information to enrich road network graphs. These enriched networks may in turn be used to produce routing suggestions which reflect an opinion of the crowd. From data processing to knowledge extracting, network enrichment and route computation, the possibilities and challenges of crowdsourced data as a source for information are surveyed. Additionally, this thesis substantiates the practicability of network enrichment in real-world experiments. The description of a demonstration framework which applies some of the presented methods to the use case of tourist route recommendation serves as an example. The methods may also be applied to a novel graph-based routing problem proposed in this thesis. The problem extends the family of Orienteering Problems which find frequent application in tourist routing and other tasks. An approximate solution to this NP-hard problem is presented and evaluated on a large scale, real-world, time-dependent road network. Another central aspect of modern traffic networks is the integration of sensor data, often referred to as telematics. Nowadays, manifold sensors provide a plethora of data. Using this data to optimize traffic is and will continue to be a challenging task for research and industry. Some of the applications which qualify for the integration of modern telematics are surveyed in this thesis. For instance, the abstract problem of consumable and reoccurring resources in road networks is studied. An application of this problem is the search for a vacant parking space. Taking statistical and real-time sensor information into account, a stochastic routing algorithm which maximizes the probability of finding a vacant space is proposed. Furthermore, the thesis presents means for the extraction of driving preferences, helping to better understand user behavior in traffic. The theoretical concepts partially find application in a demonstration framework described in this thesis. This framework provides features which were developed for a real-world pilot project on the topics of electric and shared mobility. Actual sensor car data collected in the project, gives insight to the challenges of managing a fleet of electric vehicles.Verkehrsmittel erzeugen rund ein Viertel aller Treibhausgas-Emissionen weltweit. Für über die Hälfte der verkehrsbedingten Emissionen in der Europäischen Union (EU) zeichnen PKW und Kleinlaster verantwortlich. Die Tragweite ökologischer Konsequenzen durch alltäglichen Verkehr ist enorm. Zugleich ist ein Umdenken im Bezug auf Verkehr entscheidend, um die ehrgeizigen klimapolitischen Ziele der EU zu erfüllen. Dazu gehört unter anderem, Treibhausgas-Emissionen bis 2050 um 85 bis 90 Prozent zu verringern. Die vorliegende Arbeit widmet sich den komplexen Anforderungen an Verkehr und Verkehrsnetzwerke aus der Sicht der Informatik. Dabei spielen sowohl die Modellierung von als auch die Anfragebearbeitung in modernen Verkehrsnetzwerken eine entscheidende Rolle. Theoretische Fragestellungen werden aus unterschiedlichen Persepektiven beleuchtet, neue Algorithmen werden vorgestellt. Ebenso werden praktische Fragestellungen untersucht und gelöst, etwa durch die Einbindung nutzergenerierten Inhalts oder die Verwendung von Sensordaten aus unterschiedlichen Quellen. Moderne Verkehrsnetzwerke werden häufig als Graphen modelliert, d.h., durch Knoten und Kanten dargestellt. Man unterscheidet zwischen konventionellen Graphen und sogenannten Multiattributs-Graphen. Während die Kanten konventioneller Graphen numerische Gewichte tragen, die statische Kostenkriterien wie Distanz oder Reisezeit modellieren, beschreiben die Kantengewichte in Multiattributs-Graphen mehrere, möglicherweise dynamisch veränderliche Kostenkriterien. Das erlaubt einerseits vielseitige Anfragen und aussagekräftige Erkenntnisse, macht die Anfragebearbeitung jedoch ungleich komplexer und verlangt deshalb nach neuen Berechnungsmethoden. Eine besonders aufwendige Anfrage ist die Berechnung optimaler Pfade, zugleich eine der zentralsten Fragestellungen. Die gängigste Menge optimaler Pfade wird als Pfad-Skyline (auch: Menge der pareto-optimalen Pfade) bezeichnet. Die effiziente Berechnung der Pfad-Skyline setzte bisher überschaubare Netzwerke oder beträchtliche Vorberechnungen voraus. Keine der beiden Bedingung kann in modernen Verkehrsnetzwerken erfüllt werden. Diese Arbeit stellt deshalb eine Methode vor, die die Berechnung der Pfad-Skyline erheblich beschleunigt, selbst in dynamischen Netzwerken mit drei oder mehr Kostenkriterien. Außerdem wird das Problem des exponentiellen Wachstums der Pfad-Skyline betrachtet. Die Anzahl der Elemente der Pfad-Skyline wächst im schlechtesten Fall exponentiell in der Anzahl der Kostenkriterien sowie in der Entfernung zwischen Start und Ziel. Dies kann zu unübersichtlichen und wenig aussagekräftigen Resultatmengen führen. Diese Nachteile unterstreichen die Bedeutung der linearen Pfad-Skyline, die auch im Rahmen diese Arbeit untersucht wird. Die lineare Pfad-Skyline folgt einer anderen Definition von Optimalität. Stets ist die lineare Pfad-Skyline eine Teilmenge der konventionellen Pfad-Skyline, meist enthält sie deutlich weniger, unterschiedlichere Resultate. Dadurch lässt sich die lineare Pfad-Skyline im Allgemeinen schneller berechnen und erleichtert die Interpretation der Resultate. Die Berechnung der linearen Pfad-Skyline wird erst für Netzwerke mit zwei Kostenkriterien, anschließend für Netzwerke mit beliebig vielen Kostenkriterien untersucht. Kostenkriterien sind nicht notwendigerweise auf rein quantitative Maße wie Distanz oder Reisezeit beschränkt. Diese Arbeit widmet sich auch der Integration qualitativer Informationen, mit dem Ziel, intuitivere und greifbarere Routingergebnisse zu erzeugen. Dazu wird die Möglichkeit untersucht, abstrakte Straßennetzwerke mit qualitativen Informationen anzureichern, wobei die Informationen aus nutzergenerierten Daten geschöpft werden. Solche sogenannten Enriched Networks ermöglichen die Berechnung von Pfaden, die in gewisser Weise das Wissen der Nutzer reflektieren. Von der Datenverarbeitung, über die Extraktion von Wissen, bis hin zum Network-Enrichment und der Pfadberechnung, gibt diese Arbeit einen überblick zum Thema. Weiterhin wird die Praktikabilität dieses Vorgehens mit Experimenten auf Realdaten untermauert. Die Beschreibung eines Demonstrationstools für den Anwendungsfall der Navigation von Touristen dient als anschauliches Beispiel. Die vorgestellten Methoden sind darüber hinaus auch anwendbar auf ein neues, graphentheoretisches Routingproblem, das in dieser Arbeit vorgestellt wird. Es handelt sich dabei um eine zeitabängige Erweiterung der Familie der Orienteering Probleme, die häufig Anwendung finden, etwa auch im der Bereich der Touristennavigation. Das vorgestellte Problem ist NP-schwer lässt sich jedoch dank eines hier vorgestellten Algorithmus effizient approximieren. Die Evaluation untermauert die Effizienz des vorgestellten Lösungsansatzes und ist zugleich die erste Auswertung eines zeitabhängigen Orienteering Problems auf einem großformatigen Netzwerk. Ein weiterer zentraler Aspekt moderner Verkehrsnetzwerke ist die Integration von Sensordaten, oft unter dem Begriff Telematik zusammengefasst. Heutzutage generiert eine Vielzahl von Sensoren Unmengen an Daten. Diese Daten zur Verkehrsoptimierung einzusetzen ist und bleibt eine wichtige Aufgabe für Wissenschaft und Industrie. Einige der Anwendungen, die sich für den Einsatz von Telematik anbieten, werden in dieser Arbeit untersucht. So wird etwa das abstrakte Problem konsumierbarer und wiederkehrender Ressourcen im Straßennetzwerk untersucht. Ein alltägliches Beispiel für dieses Problem ist die Parkplatzsuche. Der vorgeschlagene Algorithmus, der die Wahrscheinlichkeit maximiert, einen freien Parkplatz zu finden, baut auf die Verwendung statistischer sowie aktueller Sensordaten. Weiterhin werden Methoden zur Ableitung von Fahrerpräferenzen entwickelt. Die theoretischen Fundamente finden zum Teil in einem hier beschriebenen Demonstrationstool Anwendung. Das Tool veranschaulicht Features, die für ein Pilotprojekt zu den Themen Elektromobilität und Fahrzeugflotten entwickelt wurden. Im Rahmen eines Pilotversuchs wurden Sensordaten von Elektrofahrzeugen erhoben, die Einblick in die Herausforderungen beim Management von Elektrofahrzeugflotten geben

Modeling and Algorithmic Development for Selected Real-World Optimization Problems with Hard-to-Model Features

Mathematical optimization is a common tool for numerous real-world optimization problems. However, in some application domains there is a scope for improvement of currently used optimization techniques. For example, this is typically the case for applications that contain features which are difficult to model, and applications of interdisciplinary nature where no strong optimization knowledge is available. The goal of this thesis is to demonstrate how to overcome these challenges by considering five problems from two application domains. The first domain that we address is scheduling in Cloud computing systems, in which we investigate three selected problems. First, we study scheduling problems where jobs are required to start immediately when they are submitted to the system. This requirement is ubiquitous in Cloud computing but has not yet been addressed in mathematical scheduling. Our main contributions are (a) providing the formal model, (b) the development of exact and efficient solution algorithms, and (c) proofs of correctness of the algorithms. Second, we investigate the problem of energy-aware scheduling in Cloud data centers. The objective is to assign computing tasks to machines such that the energy required to operate the data center, i.e., the energy required to operate computing devices plus the energy required to cool computing devices, is minimized. Our main contributions are (a) the mathematical model, and (b) the development of efficient heuristics. Third, we address the problem of evaluating scheduling algorithms in a realistic environment. To this end we develop an approach that supports mathematicians to evaluate scheduling algorithms through simulation with realistic instances. Our main contributions are the development of (a) a formal model, and (b) efficient heuristics. The second application domain considered is powerline routing. We are given two points on a geographic area and respective terrain characteristics. The objective is to find a ``good'' route (which depends on the terrain), connecting both points along which a powerline should be built. Within this application domain, we study two selected problems. First, we study a geometric shortest path problem, an abstract and simplified version of the powerline routing problem. We introduce the concept of the k-neighborhood and contribute various analytical results. Second, we investigate the actual powerline routing problem. To this end, we develop algorithms that are built upon the theoretical insights obtained in the previous study. Our main contributions are (a) the development of exact algorithms and efficient heuristics, and (b) a comprehensive evaluation through two real-world case studies. Some parts of the research presented in this thesis have been published in refereed publications [119], [110], [109]

Desenvolvimento de ferramentas de apoio multicritério à decisão em problemas de localização

Tese de doutoramento, Estatística e Investigação Operacional (Análise de Sistemas), Universidade de Lisboa, Faculdade de Ciências, 2014Nesta tese, apresenta-se um Sistema de Apoio à Decisão, desenvolvido e implementado com o objectivo de apoiar a tomada de decisão em problemas de localização bicritério que envolvam preocupações ambientais. O apoio é dado em duas fases interactivas distintas, acreditando que os métodos interactivos são a melhor forma de abordar os modelos multicritério. Na primeira fase, recorre-se a um procedimento de optimização combinatória para obter, de forma progressiva e participativa, qualquer solução não dominada dos modelos de localização bicritério implementados. Nesta primeira fase interactiva, destaca-se a importância de ser possível utilizar um Sistema de Informação Geográfica, integrado no Sistema de Apoio à Decisão, para a obtenção de dados relevantes para os modelos em causa, especialmente aqueles que requerem mais preocupações relativamente aos impactos ambientais. O uso do Sistema de Informação Geográfica, ao longo de todo o processo de decisão, também permite uma visualização apelativa e real das soluções interactivamente obtidas. Na segunda fase, caso seja necessário, usa-se a posteriori uma ferramenta de análise multiatributo para estudar em detalhe as soluções de compromisso provenientes da primeira fase. Esta ferramenta corresponde a uma implementação interactiva simples do método conjuntivo, fazendo uso de um gráfico radar como base do procedimento. A ferramenta proposta pretende contornar o problema da compensação, evitando uma agregação intercritério. O método de análise inerente à ferramenta não exige qualquer transformação ou normalização, de forma a assegurar a comparabilidade entre os critérios. De modo a descrever e a validar as potencialidades e as funcionalidades do SABILOC – o Sistema de Apoio à Decisão desenvolvido e implementado, explora-se um caso de estudo de um problema real relativo à localização de estações de transferência de resíduos.In this thesis, we present a two-phase interactive Decision Support System aimed at supporting decision-making concerning bicriteria location models in which the facilities to be located could have environmental impacts. The decision support is provided through two interactive phases, believing that interactive methods are the best way to deal with multicriteria models. First, a combinatorial optimization procedure to obtain, in a progressive and participatory way, any non-dominated solution of the bicriteria location models implemented, is used. In this first phase, we highlight that a Geographic Information System, embedded into the Decision Support System, can be used to obtain relevant data for the models concerned, especially those considering environmental issues. The Geographic Information System also allows, throughout the decision process, visualizing in an appealing and real way the solutions interactively obtained. Next, if necessary, a multiattribute a posteriori analysis tool could also be employed in order to analyze in detail a set of compromise solutions from the first phase. This one stands for a simple interactive implementation of the conjunctive method making use of a radar chart as basis for the procedure. The tool proposed is intended to circumvent the problem of compensation, avoiding aggregation inter-criteria. To use the method inherent to the tool proposed, it is not required any transformation or normalization to insure the comparability between criteria. In order to describe and validate the potentialities and functionalities of SABILOC – the Decision Support System developed and implemented, we present a case study of a real world problem applied to waste transfer station siting.Fundação para a Ciência e a Tecnologia (FCT, Programa de Apoio à Formação Avançada de Docentes do Ensino Superior Politécnico - PROTEC

GIS and genetic algorithm based integrated optimization for rail transit system planning

The planning of a rail transit system is a complex process involving the determination of station locations and the rail line alignments connecting the stations. There are many requirements and constraints to be considered in the planning process, with complex correlations and interactions, necessitating the application of optimization models in order to realize optimal (i.e. reliable and cost-effective) rail transit systems. Although various optimization models have been developed to address the rail transit system planning problem, they focus mainly on the planning of a single rail line and are therefore, not appropriate in the context of a multi-line rail network. In addition, these models largely neglect the complex interactions between station locations and associated rail lines by treating them in separate optimization processes. This thesis addresses these limitations in the current models by developing an optimal planning method for multiple lines, taking into account the relevant influencing factors, in a single integrated process using a geographic information system (GIS) and a genetic algorithm (GA). The new method considers local factors and the multiple planning requirements that arise from passengers, operators and the community, to simultaneously optimize the locations of stations and the associated line network linking them. The new method consists of three main levels of analysis and decision-making. Level I identifies the requirements that must be accounted for in rail transit system planning. This involves the consideration of the passenger level of service, operator productivity and potential benefits for the community. The analysis and decision making process at level II translates these requirements into effective criteria that can be used to evaluate and compare alternative solutions. Level III formulates mathematical functions for these criteria, and incorporates them into a single planning platform within the context of an integrated optimization model to achieve a rail transit system that best fits the desired requirements identified at level I. This is undertaken in two main stages. Firstly, the development of a GIS based algorithm to screen the study area for a set of feasible station locations. Secondly, the use of a heuristic optimization algorithm, based on GA to identify an optimum set of station locations from the pool of feasible stations, and, together with the GIS system, to generate the line network connecting these stations. The optimization algorithm resolves the essential trade-off between an effective rail system that provides high service quality and benefits for both the passenger and the whole community, and an economically efficient system with acceptable capital and operational costs. The proposed integrated optimization model is applied to a real world case study of the City of Leicester in the UK. The results show that it can generate optimal station locations and the related line network alignment that satisfy the various stakeholder requirements and constraints.Open Acces

Optimization approaches to the ambulance dispatching and relocation problem

In the Emergency Medical Service (EMS) context, the decision-making process plays a very important role since some decisions highly impact patients’ health. This thesis focuses on the operational level by solving the dispatching and relocation ambulance problems. Dispatching decisions assign ambulances to emergencies, and the relocation problem decides to which base ambulances should be (re)assigned. Two optimization approaches are proposed to improve the effectiveness and efficiency in the EMS response: a mixed-integer linear programming (MILP) model and a pilot method heuristic. The aim is to maximize the system’s coverage using a time-preparedness measure allowing relocations to any base. Experiments are performed using EMS data from Lisbon, Portugal, where solving these problems is still a handmade task. Different ambulance types are considered, which should be used according to the severity of each emergency. The proposed approaches are tested under different scenarios: varying the period size, varying the number of emergencies, and simulating a whole day. Furthermore, they are adapted to compare the proposed strategy with the current Portuguese EMS strategy, which dispatches the closest available ambulance for each emergency and always relocates ambulances to their home bases. Results highlight the potential of the mathematical model and of the proposed strategy to be applied in realtime contexts since a reduction of 10% is obtained in the average response time to emergencies in the simulation scenario. The heuristic should be used when more emergencies occur in the same time period since a solution can be obtained almost immediately in contrast to the MILP usage. To help EMS managers in the decision-making process, we propose an ambulance management tool using Geographic Information Systems, which embeds the proposed approaches. It can be used in real-time or for simulation purposes. It incorporates a map visualization that analyzes ambulances’ movements on the map and the emergencies’ location

Planning of mental health services in Portugal under uncertain conditions

The demand for mental health care services is increasing significantly in the World and in Europe. For a country like Portugal, that is one of the countries with the largest prevalence of mental illnesses in Europe and with a level of supply that is not enough for the level of demand that exists nowadays, the urgency to be able to present a mental health care network able to respond to the expected increase in the demand for mental health services is higher and higher. In this thesis, a mathematical programming model - MHCU model - is presented in order to assist the decision makers to plan a mental health network that can respond to the current and future situation of the mental health care in Portugal. The model focus in the Great region of Lisbon and considers the different services provided and multiple objectives relevant in the mental health sector like the minimization of the cost or the maximization of the different equities values that are used in the model. The MHCU model is a stochastic model in order to be able to take into consideration the uncertainty associated with the mental health sector in different parameters like the demand for service and the length of stay in the network for each patient.A procura por serviços da rede de saúde mental está a aumentar significativamente no mundo e na Europa. Para um país como Portugal, que é um dos países com maior número de doentes mentais na Europa e com um nível de oferta deste tipo de serviços que não é suficiente para corresponder ao nível de procura que existe. A urgência de conseguir reformular a rede de saúde mental em Portugal de forma a que consiga responder ao expectável aumento da procura é cada vez maior. Nesta tese, é apresentado um modelo matemático - modelo MHCU - como forma de assistir os responsáveis pela gestão da saúde mental em Portugal a tomar decisões que permitam reformular a rede de saúde mental em Portugal de forma a que esta consiga responder a atual e futura realidade deste sector em Portugal Este modelo é focado na grande região de Lisboa e considera os diferentes serviços e diferentes objetivos que são relevantes para o sector da saúde mental, como minimizar o custo ou maximizar as diferentes equidades que são utilizadas no modelo. O modelo MHCU é um modelo estocástico de forma a que consiga ter em consideração a incerteza que se encontra associada ao sector da saúde mental em diferentes parâmetros como a procura pelos serviços e o tempo de permanencia nos serviços por parte de cada paciente