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Model Transformations are a key element of Model-Driven Software Engineering. As soon as variability is involved, transformations become increasingly complicated. The lack of support for variability in model transformations impairs the acceptance of approaches to organized reuse such as software product lines. In this position paper, the general problem of multi-variant model transformations is formulated for MOF-based, XMI-serialized models. A simplistic case study is presented to specify the input and the expected output of such a transformation. Furthermore, requirements for tool support are defined, including a standardized representation of both multi-variant model instances and variability information, as well as an execution specification for multi-variant transformations. A literature review reveals that the problem is weakly identified and often solved using ad-hoc solutions; there exists no tool providing a general solution to the proposed problem statement. The observation s presented here may serve for the future development of standards and tools.
Digitalization in Industry and Society is progressing quickly. Up to now, just 5 static and standard lighting applications have been dominating in the U.S. (Low/High Beam, Daytime Running Lamp Turn Indicator & Position Lamp).
The global interest in standardization of light driven messages from autonomous vehicles to other traffic participants has opened new research needs and research findings. In Europe, GTB has established a working group dedicated to this topic.
The article will discuss the possible contribution of signalling and lighting functions to Digitalization and Autonomous driving by explaining the first elements of functional definition and research results.
Model-driven development is a well-known practice in modern software engineering. Many tools exist which allow developers to build software in a model-based or even model-driven way, but they do not provide dedicated support for software product line development. Only recently some approaches combined model-driven engineering and software product line engineering. In this paper we present an approach that allows for combining feature models and Ecore-based domain models and provides extensive support to keep the mapping between the involved models consistent. Our key contribution is a declarative textual language which allows to phrase domain-specific consistency constraints which are preserved during the configuration process in order to ensure context-sensitive syntactical correctness of derived domain models.
Dieses Buch bietet eine einsteigerfreundliche Einführung in die für industrielle Betriebe maßgebliche Aufgabe der Produktionswirtschaft. Im Zentrum steht dabei das operative Produktionsmanagement. Hierzu gehören die erfolgswirksame Bewirtschaftung der Produktionsfaktoren Mensch, Betriebsmittel und Material, der Einsatz von Information sowie die Planung und Steuerung der industriellen Produktion. Mithilfe zahlreicher Unternehmensbeispiele und Übungsaufgaben wird der Leser durch produktionswirtschaftliche Grundlagen sowie durch Ablauf und Inhalt zentraler Prozesse in der Industrie geleitet. Auch die aktuelle Diskussion um das Thema Industrie 4.0 und cyber-physische Produktionssysteme werden lesefreundlich und mit konkretem Anwendungsbezug präsentiert.
This research presents a decision support methodology for the multi-criteria supplier selection and order allocation problem. The proposed approach supports purchasing managers in assembling mid-term supplier portfolios while making them aware of the trade-offs between the supplier sustainability, the purchasing costs, and the overall supply risk. First, we propose a multi-objective optimization model with three objectives: to maximize the supplier sustainability, to select the supplier portfolio with the lowest purchasing costs, and to minimize the supply risk. Our model extends existing mathematical approaches that follow the portfolio theory fathered by H. Markowitz by integrating the aspect ‘risk’ into the supplier selection problem. Secondly, since we allow for integer variables in our model — in contrast to the classical Markowitz portfolio theory — we use the ε-constraint method to visualize the efficient surface. The possibility of considering the non-dominated set of supplier portfolios is advantageous for purchasing managers as they gain a picture of the different optimal supplier portfolios and are able to analyze the trade-offs between the different purchasing goals before making a decision. Finally, we illustrate the applicability of the proposed methodology in a real-world supplier selection and order allocation case from the automotive industry. In the example case, we identify 1,754 optimal supplier portfolios that may be assembled based on the eight available suppliers. Our analyses show that each optimal portfolio consists of two suppliers, with one specific supplier being included in each portfolio. Furthermore, four suppliers are not part of any optimal solution.