Yes / Information systems are critical for companies since they offer quick and easy access to complex and significant data in a structured manner to make informed and effective business decisions. Hence, the objective of this study is to conceptualize and implement an innovative information system in the case study organization. The study identified the requirements for Organizing Vision Theory (OVT) and developed architecture based on Organizational Information Processing Theory (OIPT). This architecture is designed and developed using the Industrial Internet of Things (IIoT) to support a self-organizing vision and enhanced information processing. The study’s contribution lies in developing and executing an integrative architecture of IIoT-driven information systems from the lenses of OVT and OIPT. Further, this study contributes by mapping OVT elements (such as transparency, continuity, and coherence) and OIPT elements (information processing needs and capabilities) to drive value and knowledge through a robust architecture of IIoT-driven information systems. The study also highlights the contribution of IIoT-based information systems to a new knowledge system, facilitating better decision-making by professionals.

TEAMDEC is a Group Decision Support System (GDSS). The development of a GDSS is supported by a broad spectrum of theories and techniques. Two major aspects of GDSS development were considered in TEAMDEC design: HCI and decision-making assistance. These two aspects interact to promote an interactive group decision support system with high quality. Decision guidance using a script-based knowledge representation improves the GDSS's efficiency, effectiveness, and flexibility. The traditional script, however, is relatively inflexible. The proposed application, TEAMDEC, provides a set of solutions to support customization in a script system to enhance the decision guidance utilization. The user interface design plays an important role in the overall system design. Two software development models (lifecycle model and V-model with backtracking) are adopted for TEAMDEC development. The user interface design of TEAMDEC is considered from three perspectives: functional, aesthetic, and structural. Quality is emphasized in the development of the interactive system. It can be measured from two perspectives: those of the user and the designer. The quality measures of TEAMDEC are categorized into external properties and internal properties, corresponding to the two perspectives. / Master of Science

Federal, state and private organizations have an urgent need for renewal of water and wastewater pipelines. A pertinent gap remains in understanding the relationship between deteriorated host-pipe conditions and renewal products cost and performance. This work provides a framework Decision-Support System that supports water and wastewater pipeline renewal-products. Various renewal products fit utility needs, and the optimization of this process streamlines the decision-making for renewal product selection. The Thesis has classified various factors for use in the renewal product decision-making process, and it provides the justification for use of the renewal decision-making factors in recommending a product. Pipeline problem definition, system causes, system requirements and renewal product characteristics are the key decision-making areas controlling the recommendation of a renewal product. The Decision-Support System framework is developed in a user-friendly Visual Basic forms, using Microsoft tools and evaluated for vendor information. The given framework allows the user to edit product information needs, factors affecting decision-making and the classification of each factor. This allows for ease in modification, utilization and collaborative understanding. The prototype framework An online hosting of the proposed framework will improve accessibility and validity of the renewal decision-making process. / Master of Science

Yes / Tunnel maintenance requires complex decision making, which involves pathology diagnosis and risk assessment, to ensure full safety while optimising maintenance and repair costs. A Decision Support System (DSS) can play a key role in this process by supporting the decision makers in identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key aspect is to identify which spatial stretches within a tunnel contain pathologies of similar kinds within neighbouring tunnel segments. This paper presents PADTUN, a novel intelligent decision support system that assists with pathology diagnosis and assessment of tunnels with respect to their disorders and diagnosis influencing factors. It utilises semantic web technologies for knowledge capture, representation, and reasoning. The core of PADTUN is a family of ontologies which represent the main concepts and relations associated with pathology assessment, and capture the decision process concerning tunnel maintenance. Tunnel inspection data is linked to these ontologies to take advantage of inference capabilities offered by semantic technologies. In addition, an intelligent mechanism is presented which exploits abstraction and inference capabilities. Thus PADTUN provides the world’s first semantically based intelligent DSS for tunnel maintenance. PADTUN was developed by an interdisciplinary team of tunnel experts and knowledge engineers in real-world settings offered by the NeTTUN EU Project. An evaluation of the PADTUN system is performed using real-world tunnel data and diagnosis tasks. We show how the use of semantic technologies allows addressing the complex issues of tunnel pathology inferencing, aiding in, and matching transportation experts’ expectations of decision support. The methodology is applicable to any linear transport structures, offering intelligent ways to aid with complex decision processes related to diagnosis and maintenance. / This work was part of the NeTTUN project, funded by the EC 7th Framework under Grant Agreement 280712.

Yes / We present a decision-support framework for speeding up the ramp-up of modular assembly systems by learning from past experience. Bringing an assembly system to the expected level of productivity requires engineers performing mechanical adjustments and changes to the assembly process to improve the performance. This activity is time-consuming, knowledge-intensive and highly dependent on the skills of the engineers. Learning the ramp-up process has shown to be effective for making progress faster. Our approach consists of automatically capturing information about the changes made by an operator dealing with disturbances, relating them to the modular structure of the machine and evaluating the resulting system state by analysing sensor data. The feedback thus obtained on applied adaptations is used to derive recommendations in similar contexts. Recommendations are generated with a variant of the k-nearest neighbour algorithm through searching in a multidimensional space containing previous system states. Applications of the framework include knowledge transfer among operators and machines with overlapping structure and functionality. The application of our method in a case study is discussed. / Funded by the European Commission as part of the 7th Framework Program under the Grant agreement CP-FP 229208-2, FRAME project.

The increased use of automated systems in the public sector has led to two types of processes, fully automated decision making and humans making decisions assisted by automated decision support systems (ADSS). While having a human in the loop is often motivated by having them act as a “safeguard” for imperfect automated systems, humans themselves are not perfect decision makers. Automation bias, a tendency to agree with the recommendations of automated systems even when they are wrong, is one problem facing humans using ADSS. Mainly found in monitoring tasks such as autopilots, it has also been studied in clinical decision support systems. The aim of this systematic review is to investigate whether automation bias poses a risk for ADSS in the public sector, and to identify possible moderators. Thirteen studies were included. By doing a narrative synthesis of the included studies I found mixed results for the existence of automation bias. While there is a lack of strong evidence for automation bias, even low levels could result in consequences for the public sector as these are decisions that impact citizens' everyday life. A number of moderators are identified and suggestions for system designers are made. / En ökning av automatiserade system inom offentlig sektor har lett till två typer av processer, helt automatiserade beslut och människor som tar beslut stödda av automatiserade beslutsstöd. Att ha en människa i processen är ofta motiverat av att använda dem som ett skydd mot bristfälliga automatiserade system, men människor är i sig själva inte perfekta beslutstagare. Automation bias, en tendens att följa rekommendationer från automatiserade system även när de är inkorrekta, är ett problem för människor som använder automatiserade system. Det har främst studerats i autopiloter, men också i kliniska beslutsstöd. Syftet med denna systematiska litteraturöversikt var att undersöka om automation bias är en risk för automatiserade beslutsstöd i offentlig sektor, och att identifiera möjliga moderatorer. Tretton studier inkluderades. Genom att genomföra en narrativ syntes fann jag blandade slutsatser gällande automation bias. Samtidigt som det finns begränsade starka bevis för automation bias, kan även de nivåerna resultera i konsekvenser för offentlig sektor då de tar beslut som påverkar befolkningens vardag. Ett antal moderatorer identifierades och förslag till systemdesigners presenteras.

Our workshop aims to bring together researchers and practitioners from the fields of AI and/or production investigating, developing, or exploring AI techniques in production. We aim to provide a platform for the exchange of ideas and experiences under the general topic of ‘AI in Production’, not specializing in certain fields of production nor AI but explicitly including production planning, control and optimization. Ideally, our workshop will enable us to standardize approaches for supporting production applying AI or to transfer these approaches from one area of application in production to another. Thus, the Workshop is not only intended for experts in artificial intelligence (in production), but explicitly also for professionals from production.:This Proceedings on 'AI in Production' consists of 5 Proceedings: - Charging Strategies for Automated Guided Vehicles Using Supervised Learning - Optical Neural Networks for Low-latency and Energy-efficient Applications in Production - Flexible Data Architecture for Enabling AI Applications in Production Environments - Perception of Biases in Machine Learning in Production Research - A Structured Literature Review Dissecting Bias Categories - Supporting machine operators in paper production using machine learning based state estimation and user assistance system

In this study, four real-life problem situations were used to explore the challenges of developing and implementing decision support tools for planning and management within an urban water utility. The study sought to explore how the degree of adoption of formal decision support tools in practice, generally perceived to be low, could be improved. In the study, an Action Research (AR) approach was used. AR is an inquiry process that involves partnership between researchers and practitioners for the purpose of addressing a real-life problem issue, while simultaneously generating scientific knowledge. Unlike other research methods where the researcher seeks to study organizational phenomena but not to change them, the action researcher attempts to create organizational change and simultaneously to study the process. During the study, a number of prototype data management tools were developed. GIS-based spatial analysis and visualisation tools were extensively used to inform and enhance the processes of participatory problem identification and structuring, while a number of modelling tools were applied in the generation and evaluation of alternative solutions. As an outcome of the study, a prototype framework for the application of decision support tools within an urban water supply planning and management context was proposed. The study highlighted the challenges of embedding formal decision support processes within existing work systems in organizations, and recommendations were made on how best to achieve this. The AR approach was found to be useful in bridging the gap between academic research and technological practice, supporting the development of computerised planning and decision support tools of practical benefit to organizations. / QC 20100723

Materials have traditionally been selected for the design of a product; however, advances in the understanding of material processing along with simulation and computation techniques are now making it possible to systematically design materials by tailoring the properties of the material to achieve the desired product performance. Material design offers the potential to increase design freedom and enable improved product performance; however, this increase in design freedom brings with it significant complexity in predictive models used for design, as well as many new design variables to consider. Material selection, on the other hand, is a well-established method for identifying the best materials for a product and does not require the complex models needed for material design. But material selection inherently limits the design of products by only considering existing materials. To balance increasing design costs with potentially improved product performance, designers must have a method for assessing the value of material design in the context of product design. In this thesis, the Design Space Expansion Strategy (DSES) and the Value of Design Space Expansion (VDSE) metric are proposed for supporting a designer s decision between material selection and material design in the context of product design. The strategy consists of formulating and solving two compromise Decision Support Problems (cDSP). The first cDSP is formulated and solved using a selected baseline material. The second cDSP is formulated and solved in an expanded material design space defined by material property variables in addition to other system variables. The two design solutions are then compared using the VDSE metric to quantify the value of expanding the material design space. This strategy is demonstrated in this thesis with an example of blast resistant panel design and is validated by application of the validation square, a framework for the validating design methods.

In order to cope with the turbulent environments in digital age, an enterprise should response to the changes quickly. Therefore, an enterprise must improve her ability of real-time decision-making. One way to increase the competence of real-time decision-making is to use Real-Time Decision Support Systems (RTDSS). A key feature for a Decision Support Systems (DSS) to successfully support real-time decision-making is to help decision-makers selecting the best models within deadline. This study focuses on developing methods to support the mechanism of model selection in DSS. There are five results in this study. Firstly, we have developed a time-based framework to evaluate models. This framework can help decision-makers to evaluate the quality and cost of model solutions. Secondly, based on the framework of models evaluation, we also developed three models selection strategies. These strategies can help decision-makers to select the best model within deadline. Thirdly, according the definitions of parameter value precision and model solution precision in this study, we conduct a simulation analysis to understand the impacts of the precision of parameter values to the precision of a model solution. Fourthly, in order to understand the interaction among the model selection variables, we also simulate the application of model selection strategies. The results of simulation indicate our study can support models selection well. Finally, we developed a structure-based model retrieval method to help decision-makers find alternative models from model base efficiently and effectively. In conclusion, the results of this research have drawn a basic skeleton for the development of models selection. This research also reveals much insight into the development of real-time decision support systems.