An Optimization Algorithm for Forward-Scatter Radar Network Node Deployment Based on BFGS and Improved NSGA-II

"Coal-to-electricity" project can effectively reduce the air pollution in the northern China, and their benefits evaluation can reasonably reflect the effect of the projects. According to the present situation of the "coal-to-electricity" projects in a northern city, the evaluation index system is put forward to estimate the benefits of the projects with the aspects of economic benefits, social benefits and operation status of the distribution networks. A statistic method - TOPSIS (technique for order preference by similarity to ideal solution) is used to evaluation and analysis. In this method, the index system is weighted by the combination of analytic hierarchy process (AHP) and entropy weighting method. In the evaluation system of "coal-to-electricity" project, combination weighting - TOPSIS method can sort the different projects based on the distance between their comprehensive benefits and the positive and negative ideal solution. After the analysis of "coal-to-electricity" region with poor benefits, some recommendations can be given. Three actual project examples are applied for the evaluation system, and then the evaluation result of the examples is compared. Finally, the result is analyzed, while some improvement measures are put forward in these examples, which can give the reference and the guidance for the further implementation of the projects.

The Analytical Hierarchy Process (AHP) approach and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method are two methods that are commonly used to handle multicriteria problems. The AHP technique uses a matrix of pairwise comparisons and a hierarchical approach to criteria in order to obtain the final criteria weights. In contrast, the TOPSIS approach incorporates the concepts of proximity to the ideal solution and preference. Next, TOPSIS uses the previously established criterion weights to compute each option's relative closeness score to the ideal answer. In order to determine how effectively the AHP and TOPSIS methodologies complement one another, this study aims to assess the protocols and underlying assumptions of the two approaches. When decision makers are aware of the benefits and drawbacks of each approach, they may choose the best course of action from a mix of the two. The results of the study indicate that the process may be streamlined, subjectivity can be reduced, and criterion weights can be stabilized by combining the AHP and TOPSIS approaches.The Analytical Hierarchy Process (AHP) approach and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method are two methods that are commonly used to handle multicriteria problems. The AHP technique uses a matrix of pairwise comparisons and a hierarchical approach to criteria in order to obtain the final criteria weights. In contrast, the TOPSIS approach incorporates the concepts of proximity to the ideal solution and preference. Next, TOPSIS uses the previously established criterion weights to compute each option's relative closeness score to the ideal answer. In order to determine how effectively the AHP and TOPSIS methodologies complement one another, this study aims to assess the protocols and underlying assumptions of the two approaches. When decision makers are aware of the benefits and drawbacks of each approach, they may choose the best course of action from a mix of the two. The results of the study indicate that the process may be streamlined, subjectivity can be reduced, and criterion weights can be stabilized by combining the AHP and TOPSIS approaches.

The prediction of colour formulation is an important step in reproducing the target colour. At present, there are relatively few researches on multi‐objective colour formulation problem, and the colour matching accuracy needs to be improved. In this research, a multi‐objective evolutionary meta‐heuristic method based on the Fast and Elitist Multi‐objective Genetic Algorithm (NSGA‐II) was proposed to predict the target colour recipes. The method used dye concentration as a variable and included three objective functions: (1) minimising the CMC (Colour Measurement Committee) colour difference between the formulation colour and the target colour, (2) minimising the metamerism index, and (3) minimising the cost of the formulation. The algorithm could obtain the Pareto optimal solution set after iteration. On this basis, the best combination of formulations was selected from the optimal solution set by combining the Expert Scoring Method (ESM), Entropy Weight Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The prediction effect of the model was evaluated by taking cotton fabrics and reactive dyes actually used in plant as examples. The results showed that 87.5% of the formulations met the CMC colour difference value of no more than 1, the metamerism index of 90.0% of the formulations did not exceed 1, and the cost of 92.5% of the formulations was reduced relative to the maximum extent in the Pareto optimal solution set. Further studies should be focused on removing duplicate individuals to give better diversity in the Pareto optimal solution set.

To improve the accuracy of choosing restoration materials for repairing ancient Chinese buildings and to mitigate the risk of decision-making, this paper establishes a novel selection model of compositions and proportions of additive lime mortars for the restoration of ancient Chinese buildings. The selection process is influenced by multi-criteria and determined by a group of experts through comprehensive judgment. Thus, it is a multi-criteria group decision-making (MCGDM) problem. Firstly, considering subjective and objective criteria simultaneously, establish a selection index system for compositions and proportions of additive lime mortars in the restoration of ancient Chinese buildings. Secondly, applying a neutrosophic set to characterize experts’ evaluation information and quantify the evaluation information. Thirdly, the best–worst method (BWM) is implemented to obtain criteria weights, and the entropy weight method is utilized to obtain index weights. Finally, obtaining the priority of each alternative solution by using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) ranking technique. The practicality of the proposed model was demonstrated through a specific case of the selection of repair materials for a decorative window in one ancient Chinese building. The comparative analysis was carried out to verify the reliability and validity of the model.

Purpose - The purpose of this study, Borsa Istanbul 100 (BIST100) of the financial performance of energy companies traded multi-criteria decision making methods, TOPSIS (Technique for order preference by Similarity to ideal solution) method is measured. Methodology - For this purpose, 5 energy companies traded in BIST in 2021 and whose data are regularly accessed have been included in the scope of the study. The annual balance sheet and income statements of the companies were used in the study. The balance sheets and income statements of the companies within the scope of the research for the year 2021 were taken from the official website of the Public Lighting Platform (KAP). During the period when the application part of this study was conducted, the data of the year 2022 could not be used due to the fact that the data of the year 2022 has not yet been published. The obtained data were measured by TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, which is one of the multi-criteria Decisionmaking methods, and the performance of the companies were analyzed and compared among themselves. Findings - Ipek Natural Energy Resources Research and Production A. One of the companies included in the study as a result of the analyzes performed by the TOPSIS method.Sh. ((IPEK) was found to be the company with the best data in terms of financial performance in the analyzes carried out in accordance with the criteria included in the evaluation. After this company, the second best company in terms of financial performance is Koza Anadolu Metal Madencilik Işletmeleri A.Sh. He appeared as a (COCOON). Conclusion - In the light of the analyzes carried out in the study; IPEKE company ranked first as the best company in terms of financial performance. Ardın KOZAA of IPEKE company ranked second, ASELS company ranked third, AKSEN company ranked fourth and VESTL company ranked last in terms of financial performance among the 5 companies included in the study. Dec. Apart from these data, it can be said that changing the December period to be based on different studies, using different financial ratios, the degree of importance given to criteria, using other multi-criteria decision-making methods (MSCS) such as MOORA other than TOPSIS may give results that may change the performance ranking. Keywords: BIST-100, TOPSIS, financial performance, energy, MCDMM JEL Codes: C61, M41, M49

Ultra-high molecular weight polyethylene (UHMWPE) is widely used in biomedical applications due to its excellent wear resistance, low friction, and high impact strength than other polymers. However, its challenging machinability especially under dry machining stemming from poor thermal conductivity, viscoelasticity, and susceptibility to burr formation necessitates advanced process control to ensure high surface quality and productivity. This study proposes a data-driven, multi-objective optimization framework for UHMWPE high speed slot milling, aimed at minimizing surface roughness and burr height while maintaining high material removal rates (MRR). The methodology integrates principal component analysis (PCA) for constructing a unified surface roughness index (SRI) from twelve measured topographic parameters, gaussian process regression (GPR) for surrogate modeling of process responses, non-dominated sorting genetic algorithm II (NSGA-II) for pareto-based optimization, and the technique for order preference by similarity to ideal solution (TOPSIS) for decision support. Milling experiments were conducted under three cooling strategies including dry, super critical CO 2 (scCO₂), and scCO₂+ bio MQL, with four cutting speeds and four feed per tooth levels. Results demonstrate that the PCA-based dimensionality reduction captured over 87% of the surface texture variance, enabling efficient and interpretable optimization. Pareto front analysis revealed that scCO₂+MQL dominated the low-SRI and low-burr region. Under this condition, the process achieved superior surface quality with minimum SRI of − 2.57 and a burr height as low as 0.03 mm, while sustaining high MRR. TOPSIS identified the optimal condition (f z = 0.086 mm/tooth, v c = 970 m/min), which was experimentally validated with a prediction error below 20%.

As China’s “Dual Carbon” strategy is implemented and the new urbanization advances, balancing economic development, emission reduction, and carbon sequestration has become an important issue during the growth of emerging metropolitan areas, and it is also important for achieving high-quality urban development. Therefore, this study had three major objective functions: economic growth, carbon emission reduction, and increased carbon storage. The multi-objective land use quantity structure was solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the best solution in the solution set was introduced using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for evaluation. Finally, combined with the Future Land Use Simulation (FLUS) model, the low-carbon evolution of the metropolitan area was predicted on a spatial scale. The trade-off plan results show that by 2035, the economic benefits will reach CNY 7.65 trillion, carbon emissions will be kept under 99.24 million tons, and carbon storage will steadily increase by 15.2 million tons. Therefore, optimizing land use from the perspective of balancing carbon emissions, carbon sequestration, and economic development can provide valuable references for planning low-carbon development and the rational use of spatial resources in future metropolitan areas.

This study intends to optimize the carbon footprint management model of power enterprises through artificial intelligence (AI) technology to help the scientific formulation of carbon emission reduction strategies. Firstly, a carbon footprint calculation model based on big data and AI is established, and then machine learning algorithm is used to deeply mine the carbon emission data of power enterprises to identify the main influencing factors and emission reduction opportunities. Finally, the driver-state-response (DSR) model is used to evaluate the carbon audit of the power industry and comprehensively analyze the effect of carbon emission reduction. Taking China Electric Power Resources and Datang International Electric Power Company as examples, this study uses the comprehensive evaluation method of entropy weight- technique for order preference by similarity to ideal solution (TOPSIS). China Electric Power Resources Company has outstanding performance in promoting renewable energy, with its comprehensive evaluation index rising from 0.5458 in 2020 to 0.627 in 2022, while the evaluation index of Datang International Electric Power Company fluctuated and dropped to 0.421 in 2021. The research conclusion reveals the actual achievements and existing problems of power enterprises in energy saving and emission reduction, and provides reliable carbon information for the government, enterprises, and the public. The main innovation of this study lies in: using artificial intelligence technology to build a carbon footprint calculation model, combining with the data of International Energy Agency Carbon Dioxide (IEA CO2) emission database, and using machine learning algorithm to deeply mine the important factors in carbon emission data, thus putting forward a carbon audit evaluation system of power enterprises based on DSR model. This study not only fills the blank of carbon emission management methods in the power industry, but also provides a new perspective and basis for the government and enterprises to formulate carbon emission reduction strategies.

PurposeThis paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation.Design/methodology/approachThe optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done.FindingsThe optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA.Research limitations/implicationsIn this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order.Practical implicationsThe study presents the case of an automotive component, which illustrates practical relevance.Originality/valueIn several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.

Initial water rights allocation is a complex decision problem, and it is difficult for traditional methods to determine weights and solve the decision problem. In this study, an improved TOPSIS (Technique for order preference by similarity to ideal solution) method is proposed using vertical distance instead of Euclidean distance to calculate the closeness degree of schemes to the ideal solution, which overcomes the shortage of traditional TOPSIS method. In order to determine the weights of evaluation indexes reasonably, the improved method adopts game theory to harmonize and conjugate the subjective weight and the objective weight. On this basis, a comprehensive weight which eliminates the unilateral result can be obtained, and a model of initial water rights allocation in the watershed is established by the improved TOPSIS method. Finally, the proposed model is applied in the study of initial water rights allocation in Fuhuan River in Hubei Province, which shows rationality and validity when comparing with other models like traditional TOPSIS method, analytic hierarchy process and projection pursuit model.

This study aims to objectively analyze the feasibility of prospective recipients of the Smart Indonesia Card Scholarship (KIP-K) by integrating the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The research dataset consists of 287 data on prospective scholarship recipients with 11 main attributes that reflect the socio-economic and academic conditions of students. The research process includes data collection, pre-processing, transformation of categorical attributes into numerical values using a linear weighting scheme, cluster analysis using DBSCAN, and candidate ranking using TOPSIS. DBSCAN is used to identify cluster patterns and detect anomalies in the data of potential recipients, while TOPSIS is used to rank candidates based on proximity to the ideal solution. The results of the grouping produced 10 clusters and one noise cluster that showed a variety of socio-economic characteristics of prospective scholarship recipients. The results of the ranking show that some of the candidates with the highest TOPSIS scores come from clusters with higher levels of economic vulnerability. In addition, some of the high-scoring candidates also came from the noise cluster, indicating that even though they did not belong to a particular group, they still met the eligibility criteria based on a multi-criteria evaluation. These findings show that the combination of DBSCAN and TOPSIS has the potential to support the process of analyzing the eligibility of scholarship recipients in a more systematic and data-driven manner.

A scheduling strategy to inter-satellite links assignment in GNSS

Purpose – This paper aims to address the problem of optimal allocation of demand of items among candidate suppliers to maximize the purchase value of items. The purchase value of the items directly relates to cost and quality of raw materials purchased from the supplier. In an increasingly competitive environment, firms are paying more attention to selecting the right suppliers for procurement of raw materials and component parts for their products. The present research work focuses on this issue of supply chain management. Design/methodology/approach – This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches. Findings – The supply chain network is witnessing a changing business environment due to government policies aimed at promoting new small manufacturing enterprises (small and medium-sized enterprises) for intermediate parts and components. Hence, the managers have an option to select a new group of suppliers and allocate the optimal multi-period demand among the new group of suppliers to maximize their purchase value. In this context, the proposed hybrid model would be beneficial for the managers to operate their supply chain effectively and efficiently. The present research work will be helpful for the managers who are interested to reconfigure their supply chain under the failure of any supply chain partner or in a changing business environment. The model provides flexibility to the managers for evaluation of the different available alternatives to take a decision of optimal demand allocation among the suppliers. The proposed hybrid (fuzzy, TOPSIS and MILP) model provides more objective information for supplier evaluation and demand allocation among suppliers in a supply chain. The managers can use the proposed model to the analysis of other management decision-making problems. Originality/value – This present research work devises a hybrid algorithm for multi-period demand allocation among the suppliers. This hybrid algorithm prioritizes the suppliers and then allocates the multi-period demand among the suppliers. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy set theory and the mixed linear integer programming (MILP) approaches.

To address the problem of accurately evaluating age-friendly retrofit indicators in urban communities, this study constructs an evaluation model that takes into account user preferences and their interaction needs based on the social–ecological–technological systems (SETS) theory. The model aims to establish a set of precise community retrofit indicators, covering aspects such as public participation level, public ancillary facilities, green space layout, living environment building, health service support, and accessible design. By employing prioritization strategies, the model seeks to maximize resident satisfaction and promote harmonious coexistence between the community and the environment. Firstly, the retrofit evaluation indicators are formulated using the SETS theory. The entropy weight method (EWM) is then applied to determine the initial weights of these evaluation indicators, followed by the use of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to analyze the interrelationships among the indicators. The Kano model is integrated to adjust the weights, reflecting their importance. The final weights of the indicators are determined through normalization. Based on this, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is employed to rank and analyze the importance of age-friendly retrofits in the community. Finally, six communities in Dalian, Liaoning Province, China, are selected as samples to evaluate and analyze the age-friendly retrofit indicators. Different methods are compared, and their advantages and disadvantages are ranked to validate the effectiveness and feasibility of the proposed approach. Based on the analysis results, specific design schemes for improving the identified shortcomings in age-friendly aspects of these communities are proposed, considering the needs of aging populations and taking into account community public facilities, accessible design renovations, and the enhancement of green space layouts. This study aims to explore the comprehensive benefits of an age-friendly retrofit in urban communities and provide reference for the deep integration of social, ecological, and technological benefits in urban renewal. The evaluation indicators, methods, and conclusions presented can serve as a decision-making basis for the renovation and renewal of aging urban communities, particularly in terms of age-friendly updates.

In order to meet the electricity requirements and improve the quality of distribution networks, it is essential to improve the reliability level of the power system. This paper established several mathematical models based on circuit analysis and computer simulation to realize the evaluation of distribution network reliability. Firstly, the Sequential Monte Carlo method was utilized to simulate the indicators of series and parallel circuits, and the Entropy Weight Method was adopted to weight the indicators. Then, simulation results were comprehensively evaluated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the evaluation model of distribution network reliability was obtained. The established mathematical model provided some guidance to construct an industrial distribution network and improve the reliability level of the power system.