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  • 1.
    Silvander, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Business Process Optimization with Reinforcement Learning2019In: Lect. Notes Bus. Inf. Process., Springer Verlag , 2019, Vol. 356, p. 203-212Conference paper (Refereed)
    Abstract [en]

    We investigate the use of deep reinforcement learning to optimize business processes in a business support system. The focus of this paper is to investigate how a reinforcement learning algorithm named Q-Learning, using deep learning, can be configured in order to support optimization of business processes in an environment which includes some degree of uncertainty. We make the investigation possible by implementing a software agent with the help of a deep learning tool set. The study shows that reinforcement learning is a useful technique for business process optimization but more guidance regarding parameter setting is needed in this area. © 2019, Springer Nature Switzerland AG.

  • 2.
    Silvander, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Component Selection with Fuzzy Decision Making2018In: Procedia Computer Science, Elsevier B.V. , 2018, Vol. 126, p. 1378-1386Conference paper (Refereed)
    Abstract [en]

    In many situations a decision maker (DM) would like to grade a component, or rank several components of the same type. Often a component type has many features, which are deemed as valuable by the DM. Other vital features are not known by the DM but are needed for the component to function. However, it should be possible to guide the DM to find the desired business solution, without putting a requirement of detailed knowledge of the component type on the DM. We propose a framework for component selection with the help of fuzzy decision making. The work is based on algorithms from fuzzy decision making, which we have adapted or extended. The framework was validated by practitioners, which found the framework useful. © 2018 The Author(s).

  • 3.
    Silvander, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Svahnberg, Mikael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Uncover and Assess Rule Adherence Based on Decisions2018In: Lecture Notes in Business Information Processing / [ed] Shishkov B., Springer Verlag , 2018, Vol. 319, p. 249-259Conference paper (Refereed)
    Abstract [en]

    Context: Decisions taken by medical practitioners may be based on explicit and implicit rules. By uncovering these rules, a medical practitioner may have the possibility to explain its decisions in a better way, both to itself and to the person which the decision is affecting. Objective: We investigate if it is possible for a machine learning pipe-line to uncover rules used by medical practitioners, when they decide if a patient could be operated or not. The uncovered rules should have a linguistic meaning. Method: We are evaluating two different algorithms, one of them is developed by us and named “The membership detection algorithm”. The evaluation is done with the help of real-world data provided by a hospital. Results: The membership detection algorithm has significantly better relevance measure, compared to the second algorithm. Conclusion: A machine learning pipe-line, based on our algorithm, makes it possibility to give the medical practitioners an understanding, or to question, how decisions have been taken. With the help of the uncovered fuzzy decision algorithm it is possible to test suggested changes to the feature limits. © Springer International Publishing AG, part of Springer Nature 2018.

  • 4.
    Wilson, Magnus
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Silvander, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    A Literature Review on the Effectiveness and Efficiency of Business Modeling2018In: e-Informatica Software Engineering Journal, ISSN 1897-7979, E-ISSN 2084-4840, Vol. 12, no 1, p. 265-302Article, review/survey (Refereed)
    Abstract [en]

    Background: Achieving and maintaining a strategic competitive advantage through business and technology innovation via continually improving effectiveness and efficiency of the operations are the critical survival factors for software-intensive product development companies. These companies invest in business modeling and tool support for integrating business models into their product development, but remain uncertain, if such investments generate desired results. Aim: This study explores the effects of business modeling on effectiveness and efficiency for companies developing software-intensive products. Method: We conducted a systematic literature review using the snowballing methodology, followed by thematic and narrative analysis. 57 papers were selected for analysis and synthesis, after screening 16 320 papers from multiple research fields. Results: We analyzed the literature based on purpose, benefit, challenge, effectiveness, and efficiency with software and software-intensive products as the unit of analysis. The alignment between strategy and execution is the primary challenge, and we found no evidence that business modeling increases effectiveness and efficiency for a company. Any outcome variations may simply be a result of fluctuating contextual or environmental factors rather than the application of a specific business modeling method. Therefore, we argue that governance is the fundamental challenge needed for business modeling, as it must efficiently support simultaneous experimentation with products and business models while turning experiences into knowledge. Conclusion: We propose a conceptual governance model for exploring the effectiveness and efficiency of business modeling to occupy the missing link between business strategy, processes and software tools. We also recommend managers to introduce a systematic approach for experimentation and organizational learning, collaboration, and value co-creation.

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