Competitiveness Evaluation of Electric Bus Charging Services Based on Analytic Hierarchy Process

Against the satellite heterogeneous network optimal selection problem, in this paper we propose a network selection algorithm based on the Analytic Hierarchy Process (AHP) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The algorithm considers the parameters that may affect the network selection like quality of business services, user preferences, network load, and so on. The use of AHP is to calculate the weights of various parameters, and TOPSIS is used to obtain the final access network ranking. The simulation results verify the correctness and validity of the algorithm.

A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process (AHP) and technique for order preference by similarity to an ideal solution (TOPSIS) is established to identify potential hazards in time. First, a barrier model and fault tree analysis are used to establish an index system for oil and gas pipeline risk evaluation on the basis of five important factors: corrosion, external interference, material/construction, natural disasters, and function and operation. Next, the index weight for oil and gas pipeline risk evaluation is computed by applying the improved AHP based on the five-scale method. Then, the TOPSIS of a multi-attribute decision-making theory is studied. The method for determining positive/negative ideal solutions and the normalized equation for benefit/cost indexes is improved to render TOPSIS applicable for the comprehensive risk evaluation of pipelines. The closeness coefficient of oil and gas pipelines is calculated by applying the improved TOPSIS. Finally, the weight and the closeness coefficient are combined to determine the risk level of pipelines. Empirical research using a long-distance pipeline as an example is conducted, and adjustment factors are used to verify the model. Results show that the risk evaluation model of oil and gas pipelines based on the improved AHP–TOPSIS is valuable and feasible. The model comprehensively considers the risk factors of oil and gas pipelines and provides comprehensive, rational, and scientific evaluation results. It represents a new decision-making method for systems engineering in pipeline enterprises and provides a comprehensive understanding of the safety status of oil and gas pipelines. The new system engineering decision-making method is important for preventing oil and gas pipeline accidents.

Ecological agriculture projects have achieved a growing development in the context of low carbon. However, because of the great difference in these issues from traditional types, there exist risks in progression quality and sustainability. To better identify the risk, this paper proposes a novel hybrid approach that integrates the analytic hierarchy process (AHP) with technique for order preference by similarity to an ideal solution (TOPSIS), as well as an improved support vector machine (SVM) based on the brainstorming algorithm (BSO). First, a risk evaluation index framework is developed and elaborated in terms of the natural environment, society, market economy, management, technology, and finance. Then the traditional assessment can be derived from AHP with TOPSIS. In addition, BSO is applied to improve SVM for rapid computation. Finally, a case study is implemented to verify the accuracy of the proposed technique. In this research, based on the low-carbon perspective, artificial intelligence algorithm and risk assessment are introduced into the field of ecological agriculture project management, which is conducive to the rapid and effective evaluation of ecological agriculture project risk. It can improve managers’ risk awareness and risk management ability, reduce investment blindness, and help ecological agriculture projects achieve healthy and sustainable development under the background of low carbon, thus contributing to the development of a low-carbon economy.

Surface coal mines, when abandoned or closed, pose significant environmental and socioeconomic challenges. Repurposing these sites is crucial for sustainable land use and responsible resource management. This study presents a comprehensive decision framework tailored to the Indian mining context, utilizing a hybrid approach combining the analytic hierarchy process (AHP) and technique for order preference by similarity to an ideal solution (TOPSIS) methodology. The proposed framework assesses and ranks alternative repurposing options by considering a multi-criteria evaluation, including ecological, economic, social, and regulatory factors. AHP is employed to determine the relative importance of these criteria, reflecting the unique priorities and perspectives of stakeholders involved in the repurposing process. TOPSIS then identifies the optimal alternatives based on their overall performance against the established criteria. This hybrid methodology contributes to informed decision-making in the sustainable repurposing of abandoned surface coal mines in India. It aids in identifying the most viable and environmentally responsible alternatives, promoting efficient land use and resource conservation while addressing the challenges associated with abandoned mine sites. The methodology’s applicability extends globally to industries facing similar repurposing challenges, facilitating the transition toward a more sustainable and responsible land reclamation and resource management approach. The methodology is implemented using real mine data and demonstrates the analysis for evaluation among multiple alternatives such as solar parks, fish farming, eco-resorts, forestry, and museums. In our study, eco-resorts show more promise based on the significant potential for local economic development, provision of local employment, long-term revenue generation, potential for upskilling local youth in management, gardening, construction, and animal husbandry, and serving as a site for exhibitions of various arts and crafts.

The safety issue during bridge construction and operation has become a major concern for the society. This research focuses on how to conduct integral health-status assessment for bridges in operation on the basis of test and monitoring data. The result of the assessment will be the foundation of bridge maintenance, damage repair and reinforcement decision-making. Bridge state assessment origins in America, where bridge management systems were developed to give comprehensive assessment and help make daily management and maintenance decision for medium and small bridges. Afterward, different bridge health-status evaluation methods have been developed. For small and medium bridges, bridge assessment is usually conducted by applying assessment code. For complicated bridge, such code could not reflect the complexity of the structure. Another assessment method is reliability method. Reliability method is based on theory of probability and fully considers the uncertainty of structures. Analytic Hierarchy Process (AHP) is proposed in the 1970s to conduct multi-criteria decision-making combining qualitative and quantitative factors. (Saaty 2008) Bridge state assessment is a typical multicriteria decision-making problem. In the assessment model, multiple assessment indexes should be taken into consideration, including quantitative indexes and qualitative indexes. When the AHP assessment model is built and the weight of each index is derived, the evaluation value of the base indexes of the model should be derived and then be integrated by a certain relationship to get an integrated evaluation value representing the general state of the bridge. This research focuses on the evaluation value of base level indexes and the integrated evaluation method. The base level indexes of the bridge health-status assessment model are all from field test and monitoring directly. They need to be transformed to unified non-dimensional evaluation value. Considering the fuzziness and uncertainty of the evaluation process and transformation process, fuzzy grading is used to give a quantitative description of the base index’s evaluation. Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is a multiple attribute decision making method, which was developed for ranking and evaluation of limited number of alternatives (Yoon & Hwang 1995). Under AHP assessment framework, an improved fuzzy TOPSIS method has been proposed to conduct overall assessment of bridges. An integrated evaluation method is proposed by improving TOPSIS method and adapting it to bridge health-status assessment. The improvements include: ① absolutization of positive and negative ideal solution value; ② establishment of the virtual ideal solutions; ③ calculation of variable weight of base-level indexes considering the impact of the variation of the base-level indexes ④ fuzzy assessment based on level sets; ⑤ defuzzificaiton and standardization treatment of the assessment result. A case study applying the proposed fuzzy TOPSIS methodis introduced. This implementation verifies the feasibility and utility of the proposed method.

Multi-attribute decision making (MADM) is a state-of-the-art, popular technique for dealing with real-world problems. An effective decision can be made for various real-world problems involving multiple attributes to decide a proper solution. Software testing and bug fixing are essential steps in the field of software engineering. Bug triaging is a real challenge in large-scale software development. Bug triaging is the process of allocating newly reported bugs to the best developer who meets the requirements for addressing them. In this paper, a software developer ranking model based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed for an effective bug triaging process. Developers are ranked based on various criteria using the TOPSIS model for effective bug triaging. Assigning newly reported bugs to the appropriate and available software developer is a complex decision-making process. It involves consideration of multiple criteria for discovering the optimal solution. In software engineering, bug triaging refers to the process of allocating appropriate developers to a newly reported bug. This paper presents criteria for finding suitable developers using multi-criteria decision-making (MCDM) techniques. The Analytic Hierarchy Process (AHP) method is used to determine the weights of the criteria, and the TOPSIS MCDM technique is used to rank the most appropriate developer.

Background: To basically solve the critical environmental problems, macro and infrastructural perspectives of rational development should be designed following environmental protection rules. Creating the proper performance measurement systems in every organization has had a promising interest in university studies, and many researchers are working on it. Methods: This research intends to compare and rank 4 evaluation performance models used in gas refineries. To achieve the desired model in this study, we employed the grounded theory. The study research consisted of 20 professionals and Health, Safety, and Environmental (HSE) managers of gas refineries who had relevant experience and skills in this area. The normal score was estimated based on the weight obtained from the Analytic Hierarchy Process (AHP) to apply the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) process and prioritize the models. Results: The pairwise comparison matrix of 4 research models of the balanced scoring card, European foundation for quality management business excellence model, Iran national quality award, and environmental performance evaluation depicted that the geometric mean of the evaluation criteria includes the following items: the capability to quantify qualitative data for environmental performance, facilitating, efficient implementing, structuring, and mapping a roadmap of organization maturity to select an environmental performance estimated at 1.22, 0.90, 0.95, and 0.96, respectively. Also, the normal weights of these four criteria are estimated at 0.3039423, 0.2242449, 0.2347026, and 0.2371102, respectively. Conclusion: The findings resulting from prioritizing the organization’s Environmental Performance Evaluation (EPE) models based on TOPSIS depicted that the suggested model with relative proximity to 99% is the first choice of EPE for the gas refinery.

The Original TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) Method is a ranking method based on the principle that the chosen alternative should be as close to the ideal solution, rather than the negative-ideal solution, as possible. However, currently utilizing in establishing the evaluation system of indicator-based model, the Original TOPSIS Method is failed to take the weights of indices into account. Besides, other weighting method like Subjective Evaluation Method is proved to be largely influenced by various subjective situations. To resolve the problem of weighting, in this paper, we propose a new method named Modified TOPSIS Method utilizing Gray Correlation Analysis. By eliminating the mistakes caused by indices which are essentially measured by the same factors, it is useful to improve the evaluation system of indicator-based model. The verification utilizing Spearman's Rank Correlation Coefficient demonstrates that our Modified TOPSIS Method is improved.

BackgroundBy analyzing 23 evaluation indicators included in 14 national basic public health service programs in a region of Hohhot City, Inner Mongolia Autonomous Region, China, the performance of basic public health services in the region in 2021 were analyzed to clarify the implementation and conduct of relevant programs. We also use this study as a basis to radiate the work of municipal basic public health services centered on the region and the outstanding problems reflected and to provide theoretical contents and suggestions that can be referred to for the same regions in central and western China as well as worldwide.MethodsUsing the TOPSIS method as the basis for the data analysis method, the evaluation indexes are ranked in terms of their proximity to the idealized target, and combined with the entropy value method, Technique for order preference by similarity to an ideal solution (TOPSIS) and rank-sum ratio (RSR) were used to rank 14 basic health care providers by grade. A comprehensive evaluation of the performance of basic public health services in a region of Hohhot City, Inner Mongolia Autonomous Region in 2021 was conducted through a joint model of entropy -weighted TOPSIS and RSR, making full use of the characteristics and advantages of the fuzzy joint, and conducting a comprehensive analysis from the perspective of the ratio weight and the method of graded calculation, making the study more distinguishable and measureable.ResultsIn this study, for the regional basic public health services, a total of 23 evaluation indicators of basic public health service projects were included, among which the top three indicators with the weight of the entropy value method indicators were found to include the rate of Chinese medicine health management for the elderly, the rate of health management for the elderly, and the BCG vaccination rate after the analysis of the weight of the indicators; After the entropy-weighted TOPSIS evaluation showed that the Ci values of the regions were found to be between 0.378 and 0.715 through the calculation of the positive and negative ideal values of each indicator; RSR staging method evaluation showed that three community health centers (X2, X10, X12) had excellent evaluations of basic public health services; The number of evaluations as poor and moderate are 2 (X3, X9) and 9 (X1, X4, X5, X6, X7, X8, X11, X13, X14), respectively; Finally, the results of the entropy-weighted TOPSIS method and the fuzzy joint model of RSR staging method are basically consistent with the overall trend of the above two methods, and the reliability and credibility of the research results are high.ConclusionThe entropy-weighted TOPSIS and RSR joint model can evaluate the effectiveness of basic public health services in a more comprehensive and holistic way. The results of the RSR staging results and the related weight ratio analysis show that the basic public health service programs in Hohhot, Inner Mongolia Autonomous Region are relatively balanced, but there are some differences; The same genus of elderly Chinese medicine health management rate, health management rate of the elderly, BCG vaccination rate several indicators accounted for a higher weight, its correlation with the key population-related items is high, suggesting that the future key population health service items should be focused on, and future research should be suggested from two key research.

The goal of tone recognition is to accurately identify the type and the name of the musical instruments through processing and analyzing the sound signals. In order to reduce the influence of feature confusion on classification process, a method of tone recognition based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Random Forests is proposed. In this process, Mel Frequency Cepstral Coefficients (MFCCs) are acquired, and the quadratic sum of distance between two MFCCs and the entropy of information are computed which are used as indices to analyze and select the MFCCs based the TOPSIS decision. The selected MFCCs are used to classify tone of trumpet- piano, trumpet-cello and piano-cello, and the recognition rates were 100%, 99.9% and 100% respectively. The results are satisfactory and verify feasibility of the developed method.

Recently, energy saving problem attracts increasing attention from researchers. This study aims to determine the optimal arrangement of a tube bundle to achieve the best overall performance. The multi-objective genetic algorithm (MOGA) is employed to determine the best configuration, where two objective functions, the average heat flux q and the pressure drop Δp, are selected to evaluate the performance and the consumption, respectively. Subsequently, a decision maker method, technique for order preference by similarity to an ideal solution (TOPSIS), is applied to determine the best compromise solution from noninferior solutions (Pareto solutions). In the optimization procedure, all the two-dimensional (2D) symmetric models are solved by the computational fluid dynamics (CFD) method. Results show that performances alter significantly as geometries of the tube bundle changes along the Pareto front. For the case 1 (using staggered arrangement as initial), the optimal q varies from 2708.27 W/m2 to 3641.25 W/m2 and the optimal Δp varies from 380.32 Pa to 1117.74 Pa, respectively. For the case 2 (using in-line arrangement as initial), the optimal q varies from 2047.56 W/m2 to 3217.22 W/m2 and the optimal Δp varies from 181.13 Pa to 674.21 Pa, respectively. Meanwhile, the comparison between the optimal solution with maximum q and the one selected by TOPSIS indicates that TOPSIS could reduce the pressure drop of the tube bundle without sacrificing too much heat transfer performance.

Technique for order preference by similarity to an ideal solution (TOPSIS) is an effective technique to solve multi-criteria decision-making problem. It aims to select the alternative, which has the “shortest distance” from positive ideal solution (PIS) and the “farthest distance” from negative ideal solution (NIS). Nevertheless, much literature has demonstrated that the solution calculated by TOPSIS only is the compromise of PIS and NIS, and it is of great restriction in dealing with the practical problems which have diverse demands and properties. Therefore, in the presented model, an optimism coefficient is defined to expand the physical meaning of the standard TOPSIS. Decision-makers (DMs) can describe their attitudes toward risk and profit by changing the value of optimism coefficient. Furthermore, intuitionistic fuzzy number is introduced to measure the evaluations (linguistic values) of DMs to alternatives under diverse criteria. Intuitionistic fuzzy weighted averaging operator is used for fusing the judgments of all DMs. Finally, a conclusion can be safely obtained that the proposed model is stable and validity from the illustrative examples and sensitivity analysis.

During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The GRA(grey relational analysis)-based TOPSIS(technique for order preference by similarity to an ideal solution) decision-making approach, in combination with the advantages of both GRA and TOPSIS, was developed to solve multiple criteria decision making(MCDM) in fuzzy environment where attributes weights and ratings were expressed in interval numbers. A numerical example is proposed to illustrate an application of the proposed model. Finally, the direction for further research was pointed out.

RETRACTED: E-commerce logistics distribution mode in big-data context: A case analysis of JD.COM

Sustainable desalination process selection: Decision support framework under hybrid information