This paper presents a SysML profile that enables the direct integration of planning semantics based on the Planning Domain Definition Language (PDDL) into system models. Reusable stereotypes are defined for key PDDL concepts such as types, predicates, functions and actions, while formal OCL constraints ensure syntactic consistency. The profile was derived from the Backus-Naur Form (BNF) definition of PDDL 3.1 to align with SysML modeling practices. A case study from aircraft manufacturing demonstrates the application of the profile: a robotic system with interchangeable end effectors is modeled and enriched to generate both domain and problem descriptions in PDDL format. These are used as input to a PDDL solver to derive optimized execution plans. The approach supports automated and model-based generation of planning descriptions and provides a reusable bridge between system modeling and AI planning in engineering design.

Abstract In many recent application domains, software systems must repeatedly reconfigure themselves at runtime to satisfy changing contextual requirements. To decide which next configuration is presumably best suited is a very challenging task as it involves not only functional requirements but also non-functional properties (NFP). NFP include multiple, potentially contradicting, criteria like real-time constraints and cost measures like energy consumption. Effectiveness of context-aware reconfiguration decisions further depends on mostly uncertain future contexts which makes greedy one-step decision heuristics potentially misleading. Moreover, the computational runtime overhead for reconfiguration planning should not nullify the benefits. Nevertheless, entirely pre-planning reconfiguration decisions during design time is also not feasible due to missing knowledge about runtime contexts. In this article, we propose a model-based technique for precomputing context-aware reconfiguration decisions under partially uncertain real-time constraints and cost measures. We employ a game-theoretic approach based on stochastic priced timed game automata as reconfiguration model. This formal model allows us to automatically synthesize winning strategies for the first player (the system) which efficiently delivers presumably best-fitting reconfiguration decisions as reactions to moves of the second player (the context) at runtime. Our tool implementation copes with the high computational complexity of strategy synthesis by utilizing the statistical model checker Uppaal Stratego to approximate near-optimal solutions. We applied our tool to a real-world example consisting of a reconfigurable robot support system for the construction of aircraft fuselages. Our evaluation results show that Uppaal Stratego is indeed able to precompute effective reconfiguration strategies within a reasonable amount of time.

timeaware reconfiguration strategies based on stochastic priced timed games", written by Hendrik Göttmann, Birte Caesar,

Model-based systems Engineering (MBSE) approaches can be applied in the early engineering phases of the development of production systems. They help the system engineer to determine the suitability of production systems and assist in design decisions. To achieve this in a structured manner, several Domain-Specific Modeling Languages (DSMLs) were developed in this contribution using the UML profile mechanism and then integrated into an MBSE workflow. These DSMLs are based on industry standards which consider the Product, Process, and Resource (PPR) structures, allowing consistent modeling while considering the system variability. The application of the DSMLs is demonstrated through the development of a highly automated production system for aircraft fuselage components. Additionally, this contribution showcases how different implementation variants can be compared through simulation.

Contrary to classic document-based engineering, model-based systems engineering (MBSE) is an approach that focuses on the definition of engineering artifacts as formal information models. In complex engineering projects, such as the design of automated aircraft production systems, MBSE is a means to control complexity and support interdisciplinary collaboration. To implement MBSE in industry, ready-to-use modeling workflows that guide and support system engineers are required to minimize overhead workload. System Modeling Language (SysML), a general purpose modeling language derived from Unified Modeling Language (UML), is widely accepted as the standard modeling language in MBSE and therefore supported by industry-leading software. In this paper, we propose a SysML-based modeling workflow that supports the conceptual engineering of automated production systems. The system engineer is guided through a multi-layer modeling workflow, which is based on the Software Platform Embedded Systems (SPES) method. The modeling workflow is validated by applying it in the engineering of an automated production system that is used in aircraft manufacturing.

Being able to quickly integrate new equipment and functions into an existing plant is a major goal for both discrete and process manufacturing. But currently, these two industry domains use different approaches to achieve this goal. While the Module Type Package (MTP) is getting more and more adapted in practical applications of process manufacturing, so-called skill-based manufacturing approaches are favored in the context of discrete manufacturing. The two approaches are incompatible because their models feature different contents and they use different technologies. This contribution provides a comparison of the MTP with a skill-based approach as well as an automated mapping that can be used to transfer the contents of an MTP into a skill ontology. Through this mapping, an MTP can be semantically lifted in order to apply functions like querying or reasoning. Furthermore, machines that were previously described using two incompatible models can now be used in one production process.

Many modern software systems continuously reconfigure themselves to (self-)adapt to ever-changing environmental contexts. Selecting presumably best-fitting next configurations is, however, very challenging, depending on functional and non-functional criteria like real-time constraints as well as inherently uncertain future contexts which makes greedy one-step decision heuristics ineffective. In addition, the computational overhead caused by reconfiguration planning at run-time should not outweigh its benefits. On the other hand, completely pre-planning reconfiguration decisions at design time is also infeasible due to the lack of knowledge about the context behavior. In this paper, we propose a game-theoretic setting for precomputing reconfiguration decisions under partially uncertain real-time behavior. We employ stochastic timed game automata as reconfiguration model to derive winning strategies which enable the first player (the system) to make fast look-ups for presumably best-fitting reconfiguration decisions satisfying the second player (the context). To cope with the high computational complexity of finding winning strategies, our tool implementation1 utilizes the statistical model-checker Uppaal Stratego to approximate near-optimal solutions. In our evaluation, we investigate efficiency/effectiveness trade-offs by considering a real-world example consisting of a reconfigurable robot support system for the construction of aircraft fuselages.