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 United States is actively attempting to reduce its national healthcare expenditures, which account for more than 16 percent of the gross domestic product. A significant cost and quality driver of the healthcare industry is the universal complexity of its supply chain. It has been suggested that even small gains in supply chain quality can produce major, long-term cost savings. The authors are currently engaged in a research effort to identify opportunities for quality improvement in the healthcare supply chain. Expert testimony reveals that the concept of healthcare supply chain quality measurement can be difficult to grasp. However, almost 90 percent of the respondents to the authors' recent survey of more than 1,000 healthcare supply chain professionals indicate that their organizations are measuring the quality of their supply chains in some manner. Using an adapted framework based on Garvin's eight dimensions of quality, the authors find that the quality metrics identified from the healthcare supply chain literature and their practitioner survey are focused on measuring the performance, conformance, and features of the healthcare supply chain.

In today’s competitive global markets, ports play a vital role in global supply chain operations. A port selection model was developed to demonstrate the potential to aid shipping line companies with decisions to select the best ports in the U.S. Gulf Coast. Because port selection is complex, is dynamic, and includes multiple objectives, a multiple-objective decision analysis quantified the value of the various ports using industry data and an industry expert’s knowledge. In addition, a corresponding cost model was developed using available cost data for each port. Monte Carlo simulation analyzed the uncertainties in the value and cost models. For the demonstration model scenario assumptions, the results show that the Houston port would be the best alternative for shipping lines in the Gulf Coast. The value model also identified opportunities for improvement for each port compared with the best West Coast port. The demonstration models show that port data exist to support the development of a multiple-objective decision analysis model and a cost model that can provide useful insights to port selection decision makers. Furthermore, these models can be easily tailored to support port selection in other regions by tailoring the objectives and measures to the decision-maker objectives.