With the intensification of the aging population trend, China is facing an increasingly growing demand for older adult care services. As an important field that meets the needs of the older adult, the development of the older adult care service industry is of significant importance for social stability and the well-being of the older adult. This paper examines the trends and optimization paths of the older adult care service industry in China. It aims to analyze the current situation, problems, and causes of the industry, and propose corresponding policy adjustment recommendations. Through comparative analysis of scholars’ viewpoints, the paper redefines the connotation and scope of the older adult care service industry, emphasizing the characteristics of its compound industrial system. The analysis reveals that the current Chinese older adult care service industry is characterized by a small scale, single functionality, narrow coverage, short industrial chain, and a lack of policy support and rational resource allocation. Policy adjustment recommendations are proposed, including top-level institutional design, improvement of the social security system, and the formation of a comprehensive industrial system, in order to promote the development of the older adult care service industry. These recommendations not only promote the expansion of industry scale and the expansion of functionality, but also enhance the quality and effectiveness of older adult care services to meet the diverse needs of the older adult. The value of this paper lies in its in-depth analysis of the current situation of the older adult care service industry in China and the proposal of specific and feasible policy adjustment recommendations, providing important guidance for government departments and practitioners. The research findings can provide beneficial references for the sustainable development of the older adult care service industry, further promoting the progress of the social economy and the healthy development of an aging society.

Cyber-physical Systems (CPS) have revolutionized urban transportation worldwide, but their implementation in developing countries faces significant challenges, including infrastructure modernization, resource constraints, and varying internet accessibility. This paper proposes a methodological framework for optimizing the implementation of Cyber-Physical Urban Mobility Systems (CPUMS) tailored to improve the quality of life in developing countries. Central to this framework is the Dependency Structure Matrix (DSM) approach, augmented with advanced artificial intelligence techniques. The DSM facilitates the visualization and integration of CPUMS components, while statistical and multivariate analysis tool such as Principal Component Analysis (PCA) and artificial intelligence methods such as K-means clustering enhance complex system the analysis and optimization of complex system decisions. These techniques enable engineers and urban planners to design modular and integrated CPUMS components that are crucial for efficient, and sustainable urban mobility solutions. The interdisciplinary approach addresses local challenges and streamlines the design process, fostering economic development and technological innovation. Using DSM and advanced artificial intelligence, this research aims to optimize CPS-based urban mobility solutions, by identifying critical outliers for targeted management and system optimization.

Finding the right technique to optimize a complex problem is not an easy task. There are hundreds of methods, especially in the field of metaheuristics suitable for solving NP-hard problems. Most metaheuristic research is characterized by developing a new algorithm for a task, modifying or improving an existing technique. The overall rate of reuse of metaheuristics is small. Many problems in the field of logistics are complex and NP-hard, so metaheuristics can adequately solve them. The purpose of this paper is to promote more frequent reuse of algorithms in the field of logistics. For this, a framework is presented, where tasks are analyzed and categorized in a new way in terms of variables or based on the type of task. A lot of emphasis is placed on whether the nature of a task is discrete or continuous. Metaheuristics are also analyzed from a new approach: the focus of the study is that, based on literature, an algorithm has already effectively solved mostly discrete or continuous problems. An algorithm is not modified and adapted to a problem, but methods that provide a possible good solution for a task type are collected. A kind of reverse optimization is presented, which can help the reuse and industrial application of metaheuristics. The paper also contributes to providing proof of the difficulties in the applicability of metaheuristics. The revealed research difficulties can help improve the quality of the field and, by initiating many additional research questions, it can improve the real application of metaheuristic algorithms to specific problems. The paper helps with decision support in logistics in the selection of applied optimization methods. We tested the effectiveness of the selection method on a specific task, and it was proven that the functional structure can help the decision when choosing the appropriate algorithm.