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The purpose of this paper is to solve the realistic problem of source maintenance scheduling (SMS) based on reliability criterion. A novel effective optimization technique is proposed to solve the problem at hand.

The problem has been formulated as a combinatorial optimization task, with the goal of maximizing reliability by minimizing the sum of squares of the reserve loads while satisfying unit and system constraints. This paper employs a nature inspired algorithm known as Teaching Learning Based Optimization (TLBO) for solving the SMS problem based on reliability.

The results reveal that optimal maintenance schedules of generating units has been obtained using TLBO algorithm with minimized values of sum of squares of reserve loads while satisfying system and operational constraints. It is also found that the inclusion of resource constraints (RC) in the model have significant effects on the objective function value which provides a deep insight of the proposed methodology.

The contribution of this paper is that an efficient nature inspired algorithm has been applied to solve source maintenance scheduling problem in viewpoint of the planning for future system capacity expansion. The incorporation of exclusion and RC in the model makes the analysis about the impact of SMS on the system reliability more reasonable.

The purpose of this paper is to solve the maintenance management problems of generating units under the reliability criterion.

The problem has been formulated as a combinatorial optimization task, with explicit and simultaneous treatment of multiple objectives: maximization of reliability, minimization of fuel costs and minimization of constraint violations. This paper formulates a general generator maintenance management (GMM) problem using a reliability criterion and a novel bio‐inspired search technique, namely, artificial bee colony (ABC) algorithm is applied to determine the optimal generator maintenance schedule.

A novel meta‐heuristic search technique based algorithm has been developed to determine the optimal maintenance schedule of generating units to improve the system reliability.

The contribution of the paper is that an efficient bio‐inspired algorithm based solution technique has been developed to solve a very important problem for a power utility, i.e. the economical and reliable operation of a power system.

Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems.

The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem.

The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems.

As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues.

The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS.

As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique.

As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

Minimizing the impact on the surrounding environment and maximizing the use of production raw materials while ensuring that the relevant processes and services can be delivered within the specified time are the contents of enterprise supply chain management in the green financial system.

With the continuous development of China's economy and the continuous deepening of the concept of sustainable development, how to further upgrade the enterprise supply chain management is an urgent need to solve. How to maximize the utilization of resources in the supply chain needs to be realized from the whole process of raw material purchase, transportation and processing.

It was proved that digital twin technology had a partial intermediary role in the role of supply chain big data analysis capability on corporate finance, market, operation and other performance.

This paper focused on describing how digital twin technology could be applied to big data analysis of enterprise supply chain under the green financial system and proved its usability through experiments. The experimental results showed that the indirect effect of the path big data analysis capability digital twin technology enterprise financial performance was 0.378. The indirect effect of the path big data analysis capability digital twin technology enterprise market performance was 0.341. The indirect effect of the path big data analysis capability digital twin technology enterprise operational performance was 0.374.

The present study is aimed to determine the infoecology of scientific articles in the field of smart manufacturing (SM). The researchers designed a general framework for the investigation of infoecology.

The qualitative and quantitative data collection methods are applied to collect data from the Scopus and experts. The bibliometric technique, clustering and graph mining are applied to analysis data by Scopus data analysis tools, VOSviewer and Excel software.

It is concluded that researchers paid attention to “Flow Control”, “Embedded Systems”, “IoT”, “Big Data” and “Cyber-Physical System” more than other infocenose. Finally, a thematic model presented based on the infoecology of SM in Scopus for future studies. Also, as future work, designing a “research-related” metamodel for SM would be beneficial for the researchers, to highlight the main future research directions.

The results of the present study can be applied to the following issues: (1) To make decisions based on research and scientific evidence and conduct scientific research on real needs and issues in the field of SM, (2) Holding the workshops on infoecology to determine research priorities with the presence of experts in related industries, (3) Determining the most important areas of research in order to improve the index of applied research, (4) Assist in prioritizing research in the field of SM to select a set of research and technological activities and allocate resources effectively to these activities, (5) Helping to increase the relationship between research and technological activities with the economic and long-term goals of industry and society, (6) Helping to prioritize the issues of SM in research and technology in order to target the allocation of financial and human capital and solving the main challenges and take advantage of opportunities, (7) Helping to avoid fragmentation of work and providing educational infrastructure based on prioritized research needs and (8) Helping to hold start-ups and the activities of knowledge-based companies based on research priorities in the field of SM.

The analysis results demonstrated that the information ecosystem of SM studies dynamically developed over time. The continuous conduction flow of scientific studies in this field brought continuous changes into the infoecology of this field.

This information will be useful in the selection of materials and technology for the detection and removal of mercury ions at a low cost and with high sensitivity and selectivity. The purpose of this study is to provide the useful information for selection of materials and technology to detect and remove the mercury ions from water with high sensitivity and selectivity. The purpose of this study is to provide the useful information for selection of materials and technology to detect and remove the mercury ions from water with high sensitivity and selectivity.

Different nano- and bio-materials allowed for the development of a variety of biosensors – colorimetric, chemiluminescent, electrochemical, whole-cell and aptasensors – are described. The materials used for their development also make it possible to use them in removing heavy metals, which are toxic contaminants, from environmental water samples.

This review focuses on different technologies, tools and materials for mercury (heavy metals) detection and remediation to environmental samples.

This review gives up-to-date and systemic information on modern nanotechnology methods for heavy metal detection. Different recognition molecules and nanomaterials have been discussed for remediation to water samples. The present review may provide valuable information to researchers regarding novel mercury ions detection sensors and encourage them for further research/development.