Using simulation models for manufacturing facilities is a common approach for planning, optimizing, and testing different machine configurations and positioning before the actual construction. However, creating these models is time-consuming and costly. Consequently, only a few different simulation models are usually created based on best practices and experience, precluding any examination of the entire variety of possible solutions. To address these obstacles, we present a proof of concept to automate and hence reduce the cost of the process of simulation model creation, thereby allowing for the creation of a larger number of selectable solution variants. Based on a given master simulation model, which obtained all possible variations of a shop floor, we defined simulation building blocks as components. We used component-based synthesis using combinatory logic to synthesize a product line of varying simulation models for a given configuration to be executed and evaluated to find suitable solutions.

Motion Planning is widely acknowledged as a fundamental problem of robotics. Due to the continuous efforts of the scientific community, various algorithmic families emerged that have different strengths and weaknesses. Finding a suitable motion planning program is often not trivial for real-world problems, as various domain-specific factors must be considered. An obvious example is a potential trade-off between path length, computation time, and resource constraints. We propose a technique to systematically explore the space of suitable programs, aiming to find Pareto optimal algorithm configurations. Our approach makes use of Combinatory Logic Synthesis to perform component-based software composition. Software components are injected with domain-knowledge, effectively restricting the solution space of synthesizable programs. We synthesize sample-based global planning programs that make use of the Open Motion Planning Library (OMPL) and evaluate the produced programs to yield numeric result vectors. These steps are encapsulated in a black-box function which is used with a multi-objective optimization tool (Hypermapper) to yield an automatic, learning-based search procedure for a given feature space. We validate our approach with a series of experiments that demonstrate the extensibility and transferability of our methodology regarding different robotic systems and planning instances.

The planning of machining operations for components using CAM requires careful consideration by highly trained personnel, with rising complexity in part requirements. This work proposes a novel domain-specific planning technique using Combinatory Logic Synthesis to generate a multitude of tool paths. By analyzing current state CNC path planning algorithms, motion primitives are identified and implemented in a modular representation using software components. Thus, a large variety of path planning strategies can be generated, while maintaining a multitude of different boundary constraints and productivity metrics. These solutions are subjected to geometric physically-based simulations in order to validate their ranking and compliance.

We introduce an approach that aims to combine the usage of satisfiability modulo theories (SMT) solvers with the Combinatory Logic Synthesizer (CL)S framework. (CL)S is a tool for the automatic composition of software components from a user-specified repository. The framework yields a tree grammar that contains all composed terms that comply with a target type. Type specifications for (CL)S are based on combinatory logic with intersection types. Our approach translates the tree grammar into SMT functions, which allows the consideration of additional domain-specific constraints. We demonstrate the usefulness of our approach in several experiments.

Increasing competition, stronger customer focus, shorter product lifecycles and accelerated technological developments imply that companies are faced with the challenge of adapting their own production to the circumstances at ever shorter intervals. The factory planning project is becoming increasingly complex, but there is less and less time available for adaptation. Particularly in the initial planning phase, targets are defined without reliable planning information for the further course, which have far-reaching consequences for the outcome of a successful planning. This paper shows a possibility to generate meaningful solution alternatives at an early stage of the target planning in order to enable an efficient planning process in terms of time and costs. With the help of a constraint-based variant compilation on the basis of previously defined target and frame parameters as well as existing information on the current factory system, various possible solution variants for target planning are to be created. A specific use case scenario was used to develop and test the presented methodology. By comparing combinations of the most diverse possible solutions, the use of a combinatory logic approach enables the first rough and plausible solution variants to be generated automatically, on the basis of which the detailed planning process for achieving the determined solution variant can be created. This way, planning bottlenecks due to the wrong choice of variants as well as large time expenditure for the creation of solution variants can be avoided.

Combinatory Process Synthesis (CPS) is a special case of software synthesis that can be used to manage variability by synthetizing target-specific processes from a repository of components. While conducted CPS research mainly addresses formal aspects of algorithm engineering, no structured methodology is available that enables the broader industrial application. This study addresses this gap and proposes a procedural model for CPS. The presented research bases on the Design Science Research principles. A case study in the healthcare sector shows the successful applicability of the elaborated procedure.