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Product family development facilitates reuse across all phases of systems engineering; in case of model-based systems engineering, this reuse involves the models as well. Introducing a model-based way of working is challenging, especially for product family development. This paper describes a case of introducing a modelbased way of working in brown-field product family development. We explain how we developed a master model, i.e. a library of model elements, to predict and optimize the productivity of a family of industrial production systems. Using this master model, we construct models of existing and yet-to-be-developed product family members by configuring and combining the appropriate library elements. We use system and model execution traces to validate the productivity models. For this, we developed a master transformation, i.e. a library of execution trace transformation rules, to unify system and model execution traces. Besides the master model and the master transformation, we present lessons learned regarding the introducing a model-based way of working. This proves both technically and organizationally complex, especially for brown-field product family development, but besides the intended prediction and optimization, it brings benefits with respect to capturing domain knowledge and system validation.

Abstract The Eclipse Supervisory Control Engineering Toolkit (ESCET™) is an open-source project to provide a model-based approach and toolkit for developing supervisory controllers, targeting their entire engineering process. It supports synthesis-based engineering of supervisory controllers for discrete-event systems, combining model-based engineering with computer-aided design to automatically generate correct-by-construction controllers. At its heart is supervisory controller synthesis, a formal technique for the automatic derivation of supervisory controllers from the unrestricted system behavior and system requirements. Vital for the future development of these techniques and tools is the ESCET project’s open environment, allowing industry and academia to collaborate on creating an industrial-strength toolkit. We report on some crucial developments of the toolkit in the context of research projects with Rijkswaterstaat and ASML that have considerably improved its capability to deal with the complexity of real-life systems as well as its usability.

System-Performance Engineering (SysPE) encompasses modeling formalisms, methods, techniques, and industrial practices to design systems for performance, where performance is taken integrally into account during the whole system life cycle. Industrial SysPE state of practice is generally model-based. Due to the rapidly increasing complexity of systems, there is a need to develop and establish model-driven methods and techniques. To structure the field of SysPE, we identify (1) industrial challenges motivating the importance of SysPE, (2) scientific challenges that need to be addressed to establish model-driven SysPE, (3) important focus areas for SysPE and (4) best practices. We conducted a survey to collect feedback on our views. The responses were used to update and validate the identified challenges, focus areas, and best practices. The final result is presented in this paper. Interesting observations are that industry sees a need for better design-space exploration support, more than for additional performance modeling and analysis techniques. Also tools and integral methods for SysPE need attention. From the identified focus areas, scheduling and supervisory control is seen as lacking established best practices.

Model-driven engineering is used in the design of systems to (a.o.) enable analysis early in the design process. For instance, by using domain-specific languages, enabling engineers to model systems in terms of their domain, rather then encoding them into general purpose modeling languages. Domain-specific languages, like classical software, evolve over time. When domain languages evolve, they may trigger co-evolution of models, model-to-model transformations, editors (both graphical and textual), and other artifacts that depend on the domain-specific language. This co-evolution can be tedious and very costly. In literature, various approaches are proposed towards automated co-evolution. However, these approaches do not reach full automation. Several other studies have shown that there are theoretical limitations to the level of automation that can be achieved in certain scenarios. For several scenarios full automation can never be achieved. We wish to gain insight to which extent practically occurring scenarios can be automated. To gain this insight, in this paper, we investigate on a large-scale industrial repository, which (co-)evolutionary scenarios occur in practice, and compare them with the various scenarios and their theoretical automatability. We then assess whether practically occurring scenarios can be fully automated.

System design is a difficult process with many design-choices for which the impact may be difficult to foresee. Manufacturing system design is no exception to this. Increased use of flexible manufacturing systems which are able to perform different operations/use-cases further raises the design complexity. One important criterion to consider is the overall makespan and associated critical path for the different use-cases of the system. Stochastic critical path analysis plays a fundamental role in providing useful feedback for system designers to evaluate alternative specifications, which traditional fixed-time analysis cannot. In this paper, we extend our formal model-based framework, for the specification and design of manufacturing systems, with stochastic analysis abilities by associating a criticality index to each action performed by the system. This index can then be visualized and used within the framework such that a system designer can make better informed decisions. We propose a Monte-Carlo method as an estimation algorithm and we explicitly define and use confidence intervals to achieve an acceptable estimation error. We further demonstrate the use of the extended framework and stochastic analysis with an example manufacturing system.

This paper introduces a formal modeling approach for compositional specification of both functionality and timing of manufacturing systems. Functionality aspects can be considered orthogonally to timing aspects. The functional aspects are specified using two abstraction levels; high-level activities and lower level actions. Design of a functionally correct controller is possible by looking only at the activity level, abstracting from the different execution orders of actions and their timing. As a result, controller design can be performed on a much smaller state space compared to an explicit model where timing and actions are present. The performance of the controller can be analyzed and optimized by taking into account the timing characteristics. Since formal semantics are given in terms of a (max, +) state space, various existing performance analysis techniques can be used. We illustrate the approach, including performance analysis, on an example manufacturing system.

Development of high-level supervisory controllers is an important challenge in the design of high-tech systems. It has become a significant issue due to increased complexity, combined with demands for verified quality, time to market, ease of development, and integration of new functionality. To deal with these challenges, model-based engineering approaches are suggested as a cost-effective way to support easy adaptation, validation, synthesis, and verification of controllers. This paper presents an industrial case study on modular design of a supervisory controller for wafer logistics in lithography machines. The uncontrolled system and control requirements are modeled independently in a modular way, using small, loosely coupled and minimally restrictive extended finite automata. The multiparty synchronization mechanism that is part of the specification formalism provides clear advantages in terms of modularity, traceability, and adaptability of the model. We show that being able to refer to variables and states of automata in guard expressions and state-based requirements, enabled by the use of extended finite automata, provides concise models. Additionally, we show how modular synthesis allows construction of local supervisors that ensure safety of parts of the system, since monolithic synthesis is not feasible for our industrial case.