Architecting a System Model for Personalized Healthcare Delivery and Managed Individual Health Outcomes

In recent years, healthcare needs have shifted from treating acute conditions to meeting an unprecedented chronic disease burden. The healthcare delivery system has structurally evolved to address two primary features of acute care: the relatively short time period, on the order of a patient encounter, and the siloed focus on organs or organ systems, thereby operationally fragmenting and providing care by organ specialty. Much more so than acute conditions, chronic disease involves multiple health factors with complex interactions between them over a prolonged period of time necessitating a healthcare delivery model that is personalized to achieve individual health outcomes. Using the current acute-based healthcare delivery system to address and provide care to patients with chronic disease has led to significant complexity in the healthcare delivery system. This presents a formidable systems’ challenge where the state of the healthcare delivery system must be coordinated over many years or decades with the health state of each individual that seeks care for their chronic conditions. This paper architects a system model for personalized healthcare delivery and managed individual health outcomes. To ground the discussion, the work builds upon recent structural analysis of mass-customized production systems as an analogous system and then highlights the stochastic evolution of an individual’s health state as a key distinguishing feature

Diagnosis of an EPS module

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e ComputadoresThis thesis addresses and contextualizes the problem of diagnostic of an Evolvable Production System (EPS). An EPS is a complex and lively entity composed of intelligent modules that interact through bio-inspired mechanisms, to ensure high system availability and seamless reconfiguration. The actual economic situation together with the increasing demand of high quality and low priced customized products imposed a shift in the production policies of enterprises. Shop floors have to become more agile and flexible to accommodate the new production paradigms. Rather than selling products enterprises are establishing a trend of offering services to explore business opportunities. The new production paradigms, potentiated by the advances in Information Technologies (IT), especially in web related standards and technologies as well as the progressive acceptance of the multi-agent systems (MAS) concept and related technologies, envision collections of modules whose individual and collective function adapts and evolves ensuring the fitness and adequacy of the shop floor in tackling profitable but volatile business opportunities. Despite the richness of the interactions and the effort set in modelling them, their potential to favour fault propagation and interference, in these complex environments, has been ignored from a diagnostic point of view. With the increase of distributed and autonomous components that interact in the execution of processes current diagnostic approaches will soon be insufficient. While current system dynamics are complex and to a certain extent unpredictable the adoption of the next generation of approaches and technologies comes at the cost of a yet increased complexity.Whereas most of the research in such distributed industrial systems is focused in the study and establishment of control structures, the problem of diagnosis has been left relatively unattended. There are however significant open challenges in the diagnosis of such modular systems including: understanding fault propagation and ensuring scalability and co-evolution. This work provides an implementation of a state-of-the-art agent-based interaction-oriented architecture compliant with the EPS paradigm that supports the introduction of a new developed diagnostic algorithm that has the ability to cope with the modern manufacturing paradigm challenges and to provide diagnostic analysis that explores the network dimension of multi-agent systems

Applying agent technology to constructing flexible monitoring systems in process automation

The dissertation studies the application of agent technology to process automation monitoring and other domain specific functions. Motivation for the research work derives from the development of industrial production and process automation, and thereby the work load of operating personnel in charge of these large-scale processes has become more complex and difficult to handle. At the same time, the information technology infrastructure in process automation domain has developed ready to accept and utilise novel software engineering solutions. Agent technology is a new programming paradigm which has attractive properties like autonomy, flexibility and a possibility to distribute functions. In addition, agent technology offers a systematic methodology for designing goal based operations. This enables parts of the monitoring tasks to be delegated to the system. In this research, new agent system architecture is introduced. The architecture specifies a structure that enables the use of agents in the process monitoring domain. In addition, an introductory internal layered design of an agent aiming to combine Semantic Web and agent technologies is presented. The developed agent architecture is used in conjunction with the systematic agent design methodology to construct and implement four test cases. Each case has industrially motivated interest and illustrates various aspects of monitoring functionalities. These tests provide evidence that by utilising agent technology it is possible to develop new monitoring features for process operators, otherwise infeasible as such within current process automation systems. As a result of the research work, it can be stated that agent technology is a suited methodology to realise monitoring functionalities in process automation. It is also shown, that by applying solutions gained from the agent technology research, it is possible to define an architecture that enables to utilise the properties offered by agents in process automation environment. The proposed agent architecture supports features that are of generic interest in monitoring tasks. The developed architecture and research findings provide ground to import novel software engineering solutions to process automation monitoring

A Structured Approach to Modelling Lean Batch Production

A problem relating to the manufacture of automotive body panels concerns the appropriate choice of production size or batch quantity of a body panel production run that ensures a minimum inventory profile is maintained while not compromising production efficiency. Due to underlying variation within the body panel production process it is difficult to determine a relationship between the batch quantity and production efficiency.This thesis determines the appropriate production batch size through the creation of an iterative modelling methodology that initially examines the nature of the variation within the panel production process. Further iterations of the methodology apply appropriate analytical modelling methods until a satisfactory solution is achieved. The modelling construction is designed so that it is potentially applicable to a wider range of manufacturing problems. As there is variation inherent within the system, regression analysis, experimental design (traditional and Taguchi) are considered. Since an objective of creating the modelling methodology is the potential of apply the methodology to a wider variety of manufacturing problems, additional modelling methods are assessed. These include the operational research methods of mathematical programming (linear and non-linear and dynamic programming) and queuing systems. To model discrete and continuous behaviour of a manufacturing system, the application of hybrid automata is considered. Thus a suite of methodologies are assessed that assess variation, optimisation and networks of manufacturing systems. Through the iterative stages of the modelling approach, these analytical methods can be applied as appropriate to converge on to the appropriate solution for the problem under investigation. The appropriate methods identified to quantify a relationship between the batch production quantity and production efficiency include regression modelling and traditional experimental design. The conclusion drawn from the application of both methods is that relative to the inherent variation present in the production system, lower batch quantities can be chosen for production runs without affecting the production performance. Consequently, a minimum inventory profile can be maintained satisfying the objective of a lean system

Business strategy driven IT systems for engineer-to-order and make-to-order manufacturing enterprises

This thesis reports research into the specification and implementation of an Information Technology (IT) Route Map. The purpose of the Route Map is to enable rapid design and deployment of IT solutions capable of semi-automating business processes in a manufacturing enterprise. The Map helps structure transition processes involved in “identification of key business strategies and design of business processes” and “choice of enterprise systems and supporting implementation techniques”. Common limitations of current Enterprise Resource Planning (ERP) systems are observed and incorporated as Route Map implications and constraints. Scope of investigation is targeted at Small to Medium Sized Enterprises (SMEs) that employ Engineer-To-Order (ETO) and Make-To-Order (MTO) business processes. However, a feature of the Route Map is that it takes into account contemporary business concerns related to “globalisation”, “mergers and acquisitions” and “typical resource constraint problems of SMEs”. In the course of the research a “Business Strategy Driven IT System Concept” was conceived and examined. The main purpose of this concept is to promote the development of agile and innovative business activity in SMEs. The Road Map encourages strategy driven solutions to be (a) specified based on the use of emerging enterprise engineering theories and (b) implemented and changed using componentbased systems design and composition techniques. Part-evaluation of the applicability and capabilities of the Road Map has been carried out by conducting industrial survey and case study work. This assesses requirements of real industrial problems and solutions. The evaluation work has also been enabled by conducting a pilot implementation of the thesis concepts at the premises of a partner SME

Autonomous Finite Capacity Scheduling using Biological Control Principles

The vast majority of the research efforts in finite capacity scheduling over the past several years has focused on the generation of precise and almost exact measures for the working schedule presupposing complete information and a deterministic environment. During execution, however, production may be the subject of considerable variability, which may lead to frequent schedule interruptions. Production scheduling mechanisms are developed based on centralised control architecture in which all of the knowledge base and databases are modelled at the same location. This control architecture has difficulty in handling complex manufacturing systems that require knowledge and data at different locations. Adopting biological control principles refers to the process where a schedule is developed prior to the start of the processing after considering all the parameters involved at a resource involved and updated accordingly as the process executes. This research reviews the best practices in gene transcription and translation control methods and adopts these principles in the development of an autonomous finite capacity scheduling control logic aimed at reducing excessive use of manual input in planning tasks. With autonomous decision-making functionality, finite capacity scheduling will as much as practicably possible be able to respond autonomously to schedule disruptions by deployment of proactive scheduling procedures that may be used to revise or re-optimize the schedule when unexpected events occur. The novelty of this work is the ability of production resources to autonomously take decisions and the same way decisions are taken by autonomous entities in the process of gene transcription and translation. The idea has been implemented by the integration of simulation and modelling techniques with Taguchi analysis to investigate the contributions of finite capacity scheduling factors, and determination of the ‘what if’ scenarios encountered due to the existence of variability in production processes. The control logic adopts the induction rules as used in gene expression control mechanisms, studied in biological systems. Scheduling factors are identified to that effect and are investigated to find their effects on selected performance measurements for each resource in used. How they are used to deal with variability in the process is one major objective for this research as it is because of the variability that autonomous decision making becomes of interest. Although different scheduling techniques have been applied and are successful in production planning and control, the results obtained from the inclusion of the autonomous finite capacity scheduling control logic has proved that significant improvement can still be achieved