Clean and renewable energy is developing to realize the sustainable utilization of energy and the harmonious development of the economy and society. Microgrids are a key technique for applying clean and renewable energy. The operation optimization of microgrids has become an important research field. This paper reviews the developments in the operation optimization of microgrids. We first summarize the system structure and provide a typical system structure, which includes an energy generation system, an energy distribution system, an energy storage system and energy end users. Then, we summarize the optimization framework for microgrid operation, which contains the optimization objective, decision variables and constraints. Next, we systematically review the optimization algorithms for microgrid operations, of which genetic algorithms and simulated annealing algorithms are the most commonly used. Lastly, a literature bibliometric analysis is provided; the results show that the operation optimization of microgrids has received increasing attention in recent years, and developing countries have shown more interest in this field than developed countries have. Finally, we highlight future research challenges for the optimization of the operation of microgrids.

Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids.

Linear multistate consecutively connected systems (LMCCSs) have been widely applied in telecommunications. An LMCCS usually has several nodes arranged in sequence along a line, where connecting elements (CEs) are deployed at each node to provide connections to the following nodes. Many researchers have studied the reliability modeling and optimization of LMCCSs. However, most of the existing works on LMCCSs have focused on the uncertainty in connection ranges of CEs; none of them have considered signal loss during the transmission. In practice, a signal emitted from a node may neither completely reach nor completely not reach the destination node. In other words, only a fraction of the signal may reach the destination node whereas the rest is lost. This article makes new contributions by proposing a model that evaluates the expected signal fraction receivable by the sink node in an LMCCS subject to signal loss. Moreover, we solve the optimal design policy problem, which co-determines CEs allocation and nodes building to minimize the system cost while meeting certain constraints on system reliability and expected receivable signal fraction. Three examples are provided to illustrate the proposed model.

In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation optimization research is of great significance. This study reviews the research progress on appointment scheduling system optimization. Firstly, we classify and conclude the existing appointment scheduling system structures and decision-making frameworks. Subsequently, we summarize the system reliability optimization framework from three aspects: appointment scheduling system optimization objectives, decision variables and constraints. Following that, we methodically review the most applied system optimization algorithms in different appointment scheduling systems. Lastly, a literature bibliometric analysis is provided. During our review of the literature, we observe that (1) optimization methods in ASs predominantly involve the application of genetic algorithms and simulation optimization algorithms; (2) neural networks and deep learning methods are core technologies in health management optimization; (3) a bibliometric analysis reveals a heightened interest in the optimization technology of ASs within China compared to other nations; and (4) further advancements are essential in the comprehensive optimization of the system, exploration of practical usage scenarios, and the application of advanced simulation and modeling techniques in this research.