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Local Energy Communities are becoming key actors in the panorama of sustainable development. One of the biggest challenges for such communities is to become self-efficient, thanks to an efficient management of the balancing between produced and consumed energy. In order to achieve this goal, it is necessary to design and implement forecasting models that can provide accurate estimates to be subsequently used by optimization and planning algorithms. In this work, we show how neural networks, and in particular long short-term memory networks, can be used to this aim, highlighting an interesting trade-off between the computational requirements and the forecasting accuracy induced by learning different models for clusters of users.

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP . The focus of this article is on the role of the SCIP Optimization Suite in supporting research. SCIP ’s main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of SCIP ’s application as a research tool and as a platform for further developments. Furthermore, this article gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon SCIP .

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The prize-collecting Steiner tree problem (PCSTP) is a well-known generalization of the classic Steiner tree problem in graphs, with a large number of practical applications. It attracted particular interest during the 11th DIMACS Challenge in 2014, and since then, several PCSTP solvers have been introduced in the literature. Although these new solvers further, and often drastically, improved on the results of the DIMACS Challenge, many PCSTP benchmark instances have remained unsolved. The following article describes further advances in the state of the art in exact PCSTP solving. It introduces new techniques and algorithms for PCSTP, involving various new transformations (or reductions) of PCSTP instances to equivalent problems, for example, to decrease the problem size or to obtain a better integer programming formulation. Several of the new techniques and algorithms provably dominate previous approaches. Further theoretical properties of the new components, such as their complexity, are discussed. Also, new complexity results for the exact solution of PCSTP and related problems are described, which form the base of the algorithm design. Finally, the new developments also translate into a strong computational performance: the resulting exact PCSTP solver outperforms all previous approaches, both in terms of runtime and solvability. In particular, it solves several formerly intractable benchmark instances from the 11th DIMACS Challenge to optimality. Moreover, several recently introduced large-scale instances with up to 10 million edges, previously considered to be too large for any exact approach, can now be solved to optimality in less than two hours. Summary of Contribution: The prize-collecting Steiner tree problem (PCSTP) is a well-known generalization of the classic Steiner tree problem in graphs, with many practical applications. The article introduces and analyses new techniques and algorithms for PCSTP that ultimately aim for improved (practical) exact solution. The algorithmic developments are underpinned by results on theoretical aspects, such as fixed-parameter tractability of PCSTP. Computationally, we considerably push the limits of tractibility, being able to solve PCSTP instances with up to 10 million edges. The new solver, which also considerably outperforms the state of the art on smaller instances, will be made publicly available as part of the SCIP Optimization Suite.

Abstract Connectivity is a central concept in combinatorial optimization, graph theory, and operations research. In many applications, one is interested in finding an optimal subset of vertices with the essential requirement that the vertices are connected, but not how they are connected. In other words, it is not relevant which edges are selected to obtain connectivity. This article is concerned with the exact solution of such problems via integer programming. We analyze and compare (mixed) integer programming formulations with respect to the strength of their linear programming relaxations. Along the way, we also provide a tighter (compact) description of the connected subgraph polytope—the convex hull of subsets of vertices that induce a connected subgraph. Furthermore, we give a (compact) complete description of the connected subgraph polytope for graphs with no four independent vertices.

Security protocols ensure secure communication between and within systems such as internet services, factories, and smartphones. As evidenced by numerous successful attacks against popular protocols such as TLS, designing protocols securely is a tedious and error-prone task. Model checkers greatly aid protocol verification, yet any single model checker is oftentimes insufficient to check a protocol's security in full. Instead, engineers are forced to maintain multiple overlapping and hopefully non-contradicting and non-diverging specifications, one per model-checking tool---an error-prone task.

Abstract The concept of reduction has frequently distinguished itself as a pivotal ingredient of exact solving approaches for the Steiner tree problem in graphs. In this article we broaden the focus and consider reduction techniques for three Steiner problem variants that have been extensively discussed in the literature and entail various practical applications: The prize‐collecting Steiner tree problem, the rooted prize‐collecting Steiner tree problem and the maximum‐weight connected subgraph problem. By introducing and subsequently deploying numerous new reduction methods, we are able to drastically decrease the size of a large number of benchmark instances, already solving more than 90% of them to optimality. Furthermore, we demonstrate the impact of these techniques on exact solving, using the example of the state‐of‐the‐art Steiner problem solver SCIP‐Jack .

Distributed, software-intensive systems such as automotive electronic control units have to handle various situations employing message-based coordination. The growing complexity of such systems results in an increasing difficulty to achieve a high quality of the systemsâ requirements specifications. Scenario-based requirements engineering addresses the message-based coordination of such systems and enables, if underpinned with formal modeling languages, automatic analyses for ensuring the quality of requirements specifications. However, formal requirements modeling languages require high expertise of the requirements engineers and many manual iterations until specifications reach high quality. Patterns provide a constructive means for assembling high-quality solutions by applying reusable and established building blocks. Thus, they also gained momentum in requirements documentation. In order to support the requirements engineers in the systematic conception of formal , scenario-based requirements specification models, we hence introduce in this paper a requirement pattern catalog for a requirements modeling language. We illustrate and discuss the application of the requirement patterns with an example of requirements for an automotive electronic control unit.

Original equipment manufacturers (OEMs) build mechatronic systems using components from several suppliers in industry sectors like automation. The suppliers provide geometrical information via the standardized exchange format STEP, such that the OEM is able to virtually layout the overall system. Beyond the geometrical information, the OEM needs additional technical information for his development tasks. For that reason, STEP provides an extension mechanism for extending and tailoring STEP to project-specific needs. However, extending STEP moreover requires extending several capabilities of all involved tools, causing high development effort. This effort prevents the project-specific utilization of the STEP extension mechanism and forces the organizations to use awkward workarounds. In order to cope with this problem, we present a model-driven approach enabling the flexible specification of STEP extensions and particularly the automatic derivation of the required further capabilities for two involved tools. We illustrate and evaluate the approach with an automation production system example.

This paper describes how we solved 12 previously unsolved mixed-integer programming (MIP) instances from the MIPLIB benchmark sets. To achieve these results we used an enhanced version of ParaSCIP, setting a new record for the largest scale MIP computation: up to 80,000 cores in parallel on the Titan supercomputer. In this paper we describe the basic parallelization mechanism of ParaSCIP, improvements of the dynamic load balancing and novel techniques to exploit the power of parallelization for MIP solving. We give a detailed overview of computing times and statistics for solving open MIPLIB instances.

Presents an approach to facilitate the development and maintenance of translators for industrial robot programming languages. Such translators are widely used in robot simulation and offline programming systems to support programming in the respective native robot language. Our method is based upon a software architecture, that is provided as a complete translator framework. For the developer of a new translator, it offers convenient strategies to concentrate on robot specific language elements during the design and implementation process: fill-in templates, libraries for common functionality, design patterns etc., all tied up with a general translation scheme. In contrast to other compiler construction tools, the developers need not care about the complex details of a whole translator. As a matter of principle, the architecture offers a complete default translator (except for the grammar). Robot specific elements can be held in separate units-outside of the actual translator-to facilitate maintenance and feature extension. The most probable changes in the translator product life cycle are restricted to the adaptation of these units. Several translators built upon this framework are in actual use in the commercial robot simulation system COSIMIR(R) to support native language robot programming, as well as in the widely used robot programming system COSIROP to verify the syntax of robot programs.

In this paper, we present the implementation of a branch-and-cut algorithm for solving Steiner tree problems in graphs. Our algorithm is based on an integer programming formulation for directed graphs and comprises preprocessing, separation algorithms, and primal heuristics. We are able to solve nearly all problem instances discussed in the literature to optimality, including one problem that—to our knowledge—has not yet been solved. We also report on our computational experiences with some very large Steiner tree problems arising from the design of electronic circuits. All test problems are gathered in a newly introduced library called SteinLib that is accessible via the World Wide Web. © 1998 John Wiley & Sons, Inc. Networks 32: 207–232, 1998