Smart cities are rapidly evolving concept-transforming urban developments in the 21st century. Smart cities use advanced technologies and data analytics to improve the quality of life for their citizens, increase the efficiency of infrastructure and services, and promote sustainable economic growth. Smart cities integrate multiple domains, including transportation, energy, health, education, and governance, to create an interconnected and intelligent urban environment. Our research study methodology was a structured literature review using Web of Science and Google Scholar and ten smart city research questions. The research questions included smart city definitions, advantages, disadvantages, implementation challenges, funding, types of applications, quantitative techniques for analysis, and prioritization metrics. In addition, our study analyzes the implementation of smart city solutions in international contexts and proposes strategies to overcome implementation challenges. The integration of technology and data-driven solutions in smart cities has the potential to revolutionize urban living by providing citizens with personalized and accessible services. However, the implementation also presents challenges, including data privacy concerns, unequal access to technology, and the need for collaboration across private, public, and government sectors. This study provides insights into the current state and future prospects of smart cities and presents an analysis of the challenges and opportunities they present. In addition, we propose a concise definition for smart cities: “Smart cities use digital technologies, communication technologies, and data analytics to create an efficient and effective service environment that improves urban quality of life and promotes sustainability”. Smart cities represent a promising avenue for urban development. As cities continue to grow and face increasingly complex challenges, the integration of advanced technologies and data-driven solutions can help to create more sustainable communities.

Adequately exploring the tradespace in the early system design phase is important to determine the best design concepts to pursue in the next life cycle stage. Tradespace exploration (TSE) often uses trade-off analysis. Set-based design (SBD) methods, compared to traditional point-based design, explore significantly more designs. An integrated framework with model-based system engineering (MBSE) and a life cycle cost model enables design evaluation in near real-time. This study proposes an early design phase SBD methodology and demonstrates how SBD enabled by an integrated framework with MBSE and life cycle cost provides an enhanced TSE that can inform system design requirements and help decision makers select high performing designs at an affordable cost. Specifically, this paper (1) provides an overview of TSE and SBD, (2) describes the Integrated Trade-off Analysis Framework, (3) describes a methodology to implement SBD in the early design phase, and (4) demonstrates the techniques using an unmanned aerial vehicle case study. We found that the Integrated Trade-off Analysis Framework informs requirement development based upon how the requirements affect the feasible tradespace. Additionally, the integrated framework that uses SBD better explores the design space compared to traditional methods by finding a larger set of feasible designs early in the design process.

The Engineered Resilient Systems research program seeks to improve decision making in the Analysis of Alternatives process by leveraging model-based engineering (MBE) early in the design process to develop more resilient systems. Traditional tradespace exploration using point-based design often converges quickly to an initial baseline design concept with subsequent engineering changes to modify the design. However, this process can lead to significant cost growth if the initial concept is not able to meet requirements or if the revised design is not affordable. Enabled by MBE, set-based design (SBD) considers sets of all possible design concepts and down-selects design concepts to converge to a final design using insights into design trade-off analysis, modeling and simulation, and test data. Using a notional unmanned aerial vehicle case study with low-fidelity physics-based models and an open source Excel® add-in called SIPmath©, this research implements an integrated MBE trade-off analytics framework that simultaneously generates numerous SBDs using parametric performance and cost models and evaluates the designs in the value and cost tradespace. In addition, this research explores incorporating resilience quantification and uncertainty into SBD trade-off analysis. Future research is needed to validate the use of SBD with low-fidelity models for tradespace exploration in early system design.

System design is an exercise in sequential decision-making, with the objective of developing resilient and affordable systems. Throughout the design process, engineering and program managers must balance several competing objectives, such as ensuring design feasibility, minimizing cost, schedule, and performance risk, while simultaneously achieving stakeholder value. Thus, engineering and program managers require design and analysis methods enabling complex and multiobjective design decisions under uncertainty. Unfortunately, there exists limited research providing quantitative methodologies specifically enabling program management decisions using quantitative set-based design. We, therefore, present a quantitative set maturation and uncertainty resolution decision methodology using value-focused multiobjective decision models and model-based engineering practices. This methodology assesses and quantifies uncertainty regarding stakeholder value, cost, requirements, and design maturity for each design set. These metrics facilitate the calculation of the program manager value, which when combined with design set feasibility entropy, enable tradeoff analysis informing design maturation and uncertainty resolution prioritization decisions. We develop and demonstrate our methodology using a model-based unmanned aerial vehicle case study implemented in the ModelCenter modeling environment. This methodology provides program managers an efficient, cost effective, and defensible approach to inform system design maturation and uncertainty resolution decisions enabling the development of resilient and affordable systems.