The design of complex engineering systems requires that the problem is decomposed into subproblems of manageable size. From the perspective of decision-based design (DBD), typically this results in a set of hierarchical decisions. It is critically important for computational frameworks for engineering system design to be able to capture and document this hierarchical decision-making knowledge for reuse. Ontology is a formal knowledge modeling scheme that provides a means to structure engineering knowledge in a retrievable, computer-interpretable, and reusable manner. In our earlier work, we have created ontologies to represent individual design decisions (selection and compromise). Here, we extend the selection and compromise decision ontologies to an ontology for hierarchical decisions. This can be used to represent workflows with multiple decisions coupling together. The core of the proposed ontology includes the coupled decision support problem (DSP) construct, and two key classes, namely, Process that represents the basic hierarchy building blocks wherein the DSPs are embedded, and Interface to represent the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example. Advantages of this ontology are that it is decomposable and flexible enough to accommodate the dynamic evolution of a process along the design timeline.

Engineering design is increasingly recognized as a decision making process. Providing decision support is crucial to augment designers' decision-making capability in this process. In this paper, we present a template-based ontological method that integrates the decision-making mechanism with problem-specific information; thus, it can provide design decision support from both the “construct” and the “information” perspectives. The “construct,” namely, decision-making mechanism, is the utility-based Decision Support Problem (u-sDSP), which is a rigorous mathematical model that facilitates designers making multi-attribute selection decisions under uncertainty, while the information for decision making is archived as u-sDSP templates and represented using frame-based ontology to facilitate reuse, execution, and consistency-maintaining. This paper is an extension of our earlier work on the ontological modeling of the compromise decisions. The unique advantage of this ontology is that it captures both the declarative and procedural knowledge of selection decisions and represents them separately, thus facilitating designers reusing, executing previous documented decision knowledge to effect new decisions. The efficacy of ontology is demonstrated using a rapid prototyping (RP) resource selection example.

Utilizing the enterprise capital related the knowledge of design processes has become crucial to improve enterprise agility and respond to shifts or changes in markets. The complexity and uncertainty of design raise the challenge of capturing tacit knowledge and the ability to aid in designing design processes. In this paper, ontology is proposed for capturing, representing, and documenting the knowledge related to hierarchical decision workflows in the meta-design of complex engineered systems. The ontology is developed in the context of decision support problem technique (DSPT), considering the requirements being able to guide assistance in designing design workflows, and integrating problem, product, and process information in a design decision-making process. Then, the approach for building procedure of process templates is presented to facilitate the reuse of the populated template instances in future design. Finally, the meta-design of the heat exchanger in a small thermal system is presented as an example to illustrate the effectiveness of this approach.

Cyber-physical-social systems (CPSS) are natural extensions of cyber-physical systems that add the consideration of human interactions and cooperation with cyber systems and physical systems. CPSS are becoming increasingly important as we face challenges such as regulating our impact on the environment, eradicating disease, transitioning to digital and sustainable manufacturing, and improving healthcare. Human stakeholders in these systems are integral to the effectiveness of these systems. One of the key features of CPSS is that the form, structure, and interactions constantly evolve to meet changes in the environment. Designing evolving CPSS includes making tradeoffs amongst the cyber, the physical, and the social systems. Advances in computing and information science have given us opportunities to ask difficult and important questions, especially those related to cyber-physical-social systems. In this paper, we identify research opportunities worth investigating. We start with theoretical and mathematical frameworks for identifying and framing the problem—specifically, problem identification and formulation, data management, CPSS modeling, and CPSS in action. Then we discuss issues related to the design of CPSS including decision-making, computational platform support, and verification and validation. Building on this foundation, we suggest a way forward.