Abstract Model-Driven Engineering (MDE) has been widely adopted across various industrial sectors due to its ability to manage the complexity of modern engineering products. However, traditional modeling languages and tools are often limited to a single, specific concrete syntax, which poses challenges for the diverse stakeholders involved in the modeling process.. To address these limitations, the emerging field of blended modeling introduces the use of multiple concrete syntaxes, and in some cases, even multiple abstract syntaxes, for representing the same information. In this expert perspective, we present generalized, technology-agnostic concepts developed within a European research and development project focused on blended modeling. Specifically, we contribute a standardized terminology and ontology for blended modeling, along with a methodology for creating blended modeling environments. These concepts were developed through collaboration between academic and industrial partners, who aligned on the motivations and benefits of this approach. The insights gained from this project are not only relevant to blended MDE but also can be applied to traditional MDE practices.

Optimizing the productivity of Flexible Manufacturing Systems requires online scheduling to ensure that the timing constraints due to complex interactions between modules are satisfied. This work focuses on optimizing a ranking metric such that the online scheduler locally (i.e., per product) chooses an option that yields the highest productivity in the long term. In this paper, we focus on the scheduling of a re-entrant Flexible Manufacturing System, more specifically a Large Scale Printer capable of printing hundreds of sheets per minute. The system requires an online scheduler that determines for each sheet when it should enter the system, be printed for the first time, and when it should return for its second print. We have applied genetic programming, a hyper-heuristic, to heuristically find good ranking metrics that can be used in an online scheduling heuristic. The results show that metrics can be tuned for different job types, to increase the productivity of such systems. Our methods achieved a significant reduction in the jobs' makespan.

Schedulers assign starting times to events in a system such that a set of constraints is met and system productivity is maximized. We characterize the scheduler behaviour for the case where decisions are made by comparing affine expressions of design parameters such as task workload, processing speed, robot travelling speed, or a controller’s rise and settling time. Deterministic schedulers can be extended with symbolic execution, to keep track of the affine conditions on the parameters for which the scheduling decisions are made. We introduce a divide-and-conquer algorithm that uses this information to determine parameter regions for which the same sequence of decisions is taken given a particular scenario. The results provide designers insight in the impact of parameter changes on the performance of their system. The exploration can also be executed with the KLEE symbolic execution engine of the LLVM tool chain to extract the same results. We show that the divide-and-conquer approach provides the results much faster than the generic symbolic execution engine of KLEE. The results allow visualization of the sensitivity to all parameter combinations. The results of our approach therefore provide more insight in the sensitivity to parameters.

Flexible Manufacturing Systems (FMSs) need a scheduler to provide timing instructions for the operations of different products. Previous work has presented heuristics for fixed-order 2-re-entrant scheduling problems; where products visit a re-entrant machine exactly two times for production. We propose an extension to this scheduling model, and an extension to the scheduling heuristic, that allows jobs to move along different flows on re-entrant machines; i.e. jobs can visit the re-entrant machine once or twice. An FMS that requires such variable re-entrance with fixed-order output is a Large Scale Printer (LSP). The scheduling problem in an LSP is modeled as a variable re-entrance flowshop with relative due dates and sequence-dependent setup times, with a fixed order output. We show that out-of-order input of products can be beneficial to the scheduling quality in variable re-entrance scheduling. A fixed re-entrant heuristic is extended such that it orders operations on the re-entrant machine to minimize the completion time of variable re-entrant job sets. The resulting heuristic produces good quality schedules for variable re-entrant job sets without losing schedule quality for fixed re-entrant job sets.

High-end manufacturing systems are cyber-physical systems, where productivity depends on the close cooperation of mechanical (physical) and scheduling (cyber) aspects. Mechanical and control constraints impose minimal and maximal time differences between events in the product flow. Sequence-dependent constraints are used by a scheduler to optimize system productivity while satisfying operational requirements. The numerous constraints in a schedule are typically related to a relatively small set of parameters, such as speeds, lengths, or settling times. We contribute a parametric critical path algorithm that identifies bottlenecks in terms of the feasible parameter combinations. This algorithm allows analysis of schedules to identify bottlenecks in terms of the underlying cause of constraints. We also contribute a way to find Pareto-optimal cost-performance tradeoffs and their associated parameter combinations. These results are used to quantify the impact of relaxing constraints that hinder system productivity.

Online scheduling of operations is essential to optimize productivity of flexible manufacturing systems (FMSs) where manufacturing requests arrive on the fly. An FMS processes products according to a particular flow through processing stations. This work focusses on online scheduling of re-entrant FMSs with flows using processing stations where products pass twice and with limited buffering between processing stations. This kind of FMS is modelled as a re-entrant flow shop with due dates and sequence-dependent set-up times. Such flow shops can benefit from minimization of the time penalties incurred from set-up times. On top of an existing greedy scheduling heuristic we apply a meta-heuristic that simultaneously explores several alternatives considering trade-offs between the used metrics by the scheduling heuristic. We identify invariants to efficiently remove many infeasible scheduling options so that the running time of online implementations is improved. The resulting algorithm is much faster than the state of the art and produces schedules with on average 4.6% shorter makespan.

Self re-entrant flowshops consist of machines which process jobs several times. They are found in applications like TFT-LCD assembly, LED manufacturing and industrial printing. The structure of a self re-entrant flowshop influences its performance. To get better performance while reducing costs a fast performance estimation method can be used to explore the trade-offs between the structure and the performance during the design process. We present a novel performance estimator that uses the information in the jobs being processed to analyse the trade-offs. We study the impact of the design parameters of an industrial printer using the performance estimator with an average estimation time of 1.1 milliseconds per job and with an average accuracy of not less than 96%.