A novel method for mining the most hazardous failure path of a cascade dam group under risk transfer effect

Aircraft type Choice is flight A for companies Make important decisions process, in which Includes assessment various factors such as capacity, revenue potential, customer expectations, maintenance costs, and more. Technique for Priority Ranking by Similarity of Ideal Solution (TOPSIS) method provides a systematic approach to compare and rank different aircraft types based on multiple criteria. This abstract explores the application of the TOPSIS method in aircraft type selection, highlighting its benefits in considering diverse objectives and facilitating a more informed decision-making process. By assigning weights to criteria and comparing the alternatives to an ideal solution, the TOPSIS method helps airlines identify the most suitable aircraft type that aligns with their requirements and objectives. The abstract emphasizes the importance of accurate data and periodic reassessment due to evolving market conditions and technological advancements. Ultimately, the TOPSIS method assists airlines in making optimal decisions that optimize operational efficiency, customer satisfaction, and financial performance in aircraft type selection. Selecting the right type of aircraft is a crucial decision that involves a careful evaluation of various factors and considerations. Whether it's for commercial airlines, private aviation, military operations, or cargo transportation, choosing the appropriate aircraft type is essential to meet specific requirements, optimize performance, ensure safety, and achieve operational efficiency. The process of aircraft type selection involves a comprehensive analysis of multiple parameters, such as mission profile, range, payload capacity, operational costs, environmental considerations, regulatory requirements, and technological advancements. It requires a thorough understanding of the intended purpose, operational constraints, and long-term objectives. For commercial airlines, factors like passenger capacity, range capability, fuel efficiency, and passenger comfort play a vital role in selecting the right aircraft type. Airlines need to consider market demand, route network, and the ability to maximize revenue while minimizing operating expenses. The research on aircraft type selection holds significant importance in the aviation industry due to the following reasons: Operational Efficiency: The selection of the right aircraft type can significantly impact operational efficiency. By identifying the most suitable aircraft for a specific mission profile, airlines, private operators, and military organizations can optimize fuel consumption, reduce operating costs, and enhance overall performance. Research in this area helps to develop methodologies, models, and decision-support tools that enable informed and data-driven aircraft selection processes. Safety and Security: The safety and security of passengers, crew, and cargo are paramount in aviation. Choosing the appropriate aircraft type involves considering safety features, maintenance requirements, compliance with regulatory standards, and the ability to handle emergencies. Research in aircraft type selection contributes to the identification of aircraft that meet the highest safety standards, reducing the risk of accidents and ensuring the secure transportation of people and goods. Market Competitiveness: In the commercial aviation sector, staying competitive in a rapidly evolving market is crucial. Airlines need to select aircraft that align with market demand, passenger preferences, and route networks. Research in aircraft type selection aids in understanding market dynamics, forecasting future trends, and identifying aircraft that offer the best balance between capacity, range, fuel efficiency, and passenger comfort. It enables airlines to make informed decisions that enhance their competitiveness and profitability. We will be use TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) in this study, which is a research approach that gives TOPSIS research methods because it enables researchers to make informed and objective decisions in a multicriteria environment. Its quantitative and transparent approach, consideration of positive and negative aspects, simplicity, flexibility, and robustness make it a valuable tool for various research disciplines, facilitating systematic analysis and comparison of alternatives. Alternative Parameters taken as Boeing78C, Boeing79L, Airbus321, Airbus32C, Airbus320, Airbus319. Evaluation Parameters taken as Capacity, Revenue, Customer expectation, Maintenance Cost. RANK graph and the values making using the Analysis method in TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). Alternative: Boeing78C, Boeing79L, Airbus321, Airbus32C, Airbus320, Airbus319. Evaluation preference: Capacity, Revenue, Customer expectation, Maintenance Cost. By the final rank graph, we can conclude that Airbus321 and Airbus 319 values are high. Training aircraft selection is a complex process that requires a thorough understanding of performance characteristics, training requirements, cost factors, technological advancements, and safety considerations. This research article provides a comprehensive analysis of these factors and offers insights into the methodologies and decision-making frameworks used in training aircraft selection. By considering the information presented in this article, flight training departments can make informed decisions that enhance the effectiveness and efficiency of their training programs, ensuring the development of skilled and competent pilots.

Mine designers often face difficulties in selecting an appropriate mining method; however, such a method should be selected based on ore and rock characteristics. The selection of mining methods can be considered a type of multi-criteria decision making, and this depends on many factors used in the selection process. The general method used in this field is the University of British Columbia (UBC) method, which determines the criteria of the properties that are compared to determine the best and worst of several mining methods. In this paper we used as new technique which define as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The criteria considered in the UBC method include general shape, ore thickness, ore plunge, and grade distribution, beside the rock quality designation (RQD), and the rock substance strength (RSS). This paper presents an improved TOPSIS method based on experimental design. Additionally, this paper will introduce a modified version of the UBC method that can be employed based on Excel sheet. The best mining methods is cut and fill Stoping and Top slicing with the same rank equal 0.72, and the second-best mining method is Square set Stoping with rank equal 0.65.

In recent times, supplier selection has become one of the most important and crucial activities for companies. In this study, using the extended fuzzy cognitive maps (E-FCM) and technique for order of preference by similarity to ideal solution (TOPSIS), a decision-making support system is realised to assist managers in this activity. E-FCM expresses a causal relationship among criteria, computing linguistic variables to describe a complex situation. The proposed system allows managers to conduct an a priori evaluation regarding supplier suitability, according to both company and market requirements. A panel of experts was formed, according to their expertise areas, to cover the entire problem domain and model it. The problem was investigated in terms of the factors identified by the experts, such as costs, delivery quality, organisational capability, supplier flexibility, service quality and supplied product quality. These factors were analysed using the TOPSIS approach to rank the suppliers, and the use of TOPSIS allows for discrimination of the E-FCM. This decision-making support system was applied to a real case scenario to test its functionality; in particular, an Italian shoes and accessories company. The TOPSIS ideal solutions were defined from two different points of view: based on the standard TOPSIS procedure and on specifics fixed by the company managers. The two approaches resulted in considerably different outcomes, highlighting the need to consider concepts related to company expectations in the E-FCM.

A bibliometric-based survey on AHP and TOPSIS techniques

A key issue faced within the manufacturing industry is determining how to measure quality characteristics and prioritise improvements to be made to all substandard quality characteristics of a product with respect to resource requirements and performance improvement potential. This study proposes a QCAC–Entropy–TOPSIS approach in order to address this issue. It combines the Quality Characteristic Analysis Chart (QCAC), entropy method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed method is not only helpful to measure and determine whether the quality characteristics meet 6σ, 5σ, 4σ or 3σ but also to rank improvements in all substandard quality characteristics of a product in light of resource requirements and potential for performance improvements simultaneously, making it suitable to all manufacturing industries. Moreover, it also can be a powerful tool for analysing the problems that lead to substandard quality characteristics due to poor accuracy and/or precision. Firstly, using the QCAC, the substandard quality characteristics of the product can be determined and the corresponding values of the Discrimination Distance (DD) can then be computed. Subsequently, all the substandard quality characteristics can be regarded as alternatives when conducting entropy and TOPSIS analyses. Secondly, the weights of the evaluation criteria can be calculated by using the entropy method. Lastly, the weights of the evaluation criteria and the values of DD can be substituted into the TOPSIS method. The manufacturer can then categorically prioritise improvement options for all substandard quality characteristics with respect to resource requirements, and consider potential for performance improvements simultaneously. An example is provided for a bicycle quick release manufacturer to illustrate in detail the calculation process of the developed approach. Finally, the advantages of the proposed method are also given through comparisons with Process Capability Analysis Chart and TOPSIS methods.

This paper presents a modified Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for evaluating system concepts using minimum and maximum customer requirements to determine a best value alternative. The purpose of this decision analysis technique is to identify system alternatives with an optimal combination of performance, reliability, and cost based on ideal customer requirements. Currently, most conventional TOPSIS models use minimum and maximum values under a specific criterion to evaluate each alternative. Using these models, an alternative being evaluated could receive significantly higher scores when reported capabilities are greater than ideal customer requirements. This problem is pronounced whenever weights are applied to criteria where excessive capabilities are recorded. If an organizational goal is to select the best value alternative, use of conventional TOPSIS models may not lead to the best value decision. The modified TOPSIS method presented in this paper restricts scoring for alternatives that provide excess capabilities beyond ideal customer requirements. Criteria weights are assigned according to a customer's prioritized requirements. This Objective Criteria Saturation TOPSIS (OCS-TOPSIS) method was created to provide Decision Makers (DM) with a tool to evaluate system alternatives against performance criteria, reliability and life cycle costs. To demonstrate the effectiveness of OCS-TOPSIS, a basic example is provided that displays the cost savings outcome over traditional TOPSIS.

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an algorithm that can be used for alternative design in a decision support system (DSS). TOPSIS provides recommendation so that users can get information that support their decision, for example a tourist wants to visit a tourist destination in Malang, then TOPSIS provides recommendations of tourist destinations in the form of ranking recommendation, with the highest rank is the most recommended recommendation. TOPSIS-based Mobile Decision Support System (DSS) has relatively low algorithm complexity. However, there are some cases that require development from personal DSS to group DSS, for example tourists rarely come alone, in which case most of them invite friends or family. For users who are more than 1 person, the TOPSIS algorithm can be combined with the BORDA algorithm. This study explains about the implementation & testing of TOPSIS and TOPSIS-BORDA as algorithms for personal and group DSS in mobile-based tourism recommendation system in Malang. Correlation testing was conducted to test the effectiveness of TOPSIS in mobile-based recommendation system. In previous study, correlation testing for personal DSS showed that there was a relationship between the recommendation and user choice, with correlation value of 0.770769231. In this study, correlation testing for group DSS showed there is a positive correlation of 0.88 between the recommendations of the group produced by TOPSIS-BORDA and personal recommendations for each user produced by TOPSIS.

In recent years several previous scholars made attempts to develop, extend, propose and apply Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for solving problems in decision making issues. Indeed, there are questions, how TOPSIS can help for solving these problems? Or does TOPSIS solved decision making problems in the real world? Therefore, this study shows the recent developments of TOPSIS approach which are presented by previous scholars. To achieve this objective, there are 105 reviewed papers which developed, extended, proposed and presented TOPSIS approach for solving DM problems. The results of the study indicated that 49 scholars have extended or developed TOPSIS technique and 56 scholars have proposed or presented new modifications for problems solution related to TOPSIS technique from 2000 to 2015. In addition, results of this study indicated that, previous studies have modifications related to this technique in 2011 more than other years.

Selecting a method is a multi-criteria decision-making issue which includes both qualitative and quantitative aspects. In order to choose the best method or a solution,, it is necessary to make a transition between both visible and invisible aspects. The focus of this work is to expand a methodology to evaluate the best method based on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In this manuscript, by ‘method’ is meant an algorithm. The relevant criteria which affect the process of algorithmic selection are the image, the sensors, and the resources, and the available alternatives are discrete wavelet transform (DWT)-based image fusion, principal component analysis (PCA)-based image fusion, intensity hue saturation (IHS)-based image fusion, and Laplacian-based image fusion. The assessments of each criterion are calculated using pairwise comparisons based on analytic hierarchy process (AHP) and inserted to the TOPSIS method to rank the alternatives. The process of selecting the alternative and the drawback of an algorithm (AHP, TOPSIS) is demonstrated with the help of numerical example. This manuscript comprises the following section headings: Introduction, Concept of TOPSIS and AHP, Hybrid Method, A Numerical Example, Computation of TOPSIS, Occurrence of Rank Reversal, Conclusion, and References.

This research was conducted to conduct an analysis that was used to assist in providing recommendations in making decisions regarding the allocation of village funds regarding Karangturi village infrastructure development. The analysis carried out aims to improve the welfare of the community and fulfill the needs of the facilities and infrastructure needed to meet the daily needs of the surrounding village communities, such as landfills, green parks, asphalt roads and so forth. The data that will be used for data analysis in this study include infrastructure data, Criteria, Weight Weights, Calculations and Final Report on what infrastructure is feasible for development. Where this analysis is carried out with the help of a decision-making method namely TOPSIS (Technique For Order Of Preference By Similarity To Ideal Solution) which means that by using TOPSIS (Technique For Order Of Preference By Similarity To Ideal Solution) in addition to getting accurate results, it is expected also get the value of the criteria used to determine the priority of village infrastructure development and get the value of an ideal solution that can be used as a recommendation in making decisions for the allocation of village funds in the field of village infrastructure. The results of the research analysis using the TOPSIS (Technique For Order Of Preference By Similarity To Ideal Solution) method are expected to provide recommendation data along with ranking results in determining the infrastructure that is needed by the community around Karangturi village, Sumbang District.

In the future, China’s demand for centralized industrial development in remote areas will gradually increase, but the operation evaluation analysis of off-grid systems applicable to the development of such areas has not yet matured, and it is an urgent challenge to improve the operation mechanism of off-grid systems and then conduct a comprehensive benefit evaluation of off-grid systems. First of all, this paper focuses on the problem that the existing dimensions of the benefit evaluation of multi-energy complementary off-grid systems are not refined and comprehensive enough, and takes into account their high safety and reliability requirements, as well as the potential impacts on local industries and people’s lives after their completion, and then constructs a more complete comprehensive benefit evaluation indicator system for multi-energy complementary off-grid systems. Secondly, the subjective and objective weighting method based on the combination of the AHP (analytic hierarchy process) and AEM (anti-entropy method) is used to assign weights to the evaluation indicators. Finally, based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) comprehensive evaluation method, a comprehensive benefit evaluation of a multi-energy complementary off-grid system under different operation schemes is conducted, and the example results show that the size of the relative closeness under different operation schemes has a maximum difference of 0.5592, which verifies that the proposed evaluation indicator system and the multilevel evaluation method can comprehensively evaluate and analyze the strengths and weaknesses of multi-energy complementary off-grid systems under different operation schemes, and provide theoretical guidance and decision-making support for the further promotion and construction of multi-energy complementary off-grid systems.

Population growth has resulted in an increase in land exploitation on a large scale. Therefore, to increase crop yield and sustainable use of soil, it is necessary to exploit the land according to its potential. Due to land suitability assessment's multifactor nature, it needs a method for evaluating the factors simultaneously; in this case, multi-criteria decision models can be used. Therefore, this study aimed to compare the efficiency of the parametric method (square root) with multi-criteria decision-making approaches (technique for order of preference by similarity to ideal solution (TOPSIS) and simple additive weighting (SAW)) for evaluating land suitability in some rice cultivated areas in Khuzestan province, southwest Iran. A total of 28 rice farms were selected in the study area, and a pedon was dug, examined, and sampled in each. Several physicochemical land characteristics were used for the evaluation process, such as soil, climate, and topographical factors. According to the results, soil texture is the main limiting factor for rice farming in the study region, and organic carbon, salinity, and alkalinity ranked next. The range of land index for rice cultivation calculated by the square root method was from 19.3 to 70.9, from 49 to 95.3 by TOPSIS, and from 3.57 to 74.7 by SAW. The calculated explanatory coefficients between the actual yield and land indices for rice products estimated by the square root method, the TOPSIS approach, and the SAW method were 0.44, 0.63, and 0.60, respectively. This result confirms the high accuracy of TOPSIS method compared to SAW and square root methods. TOPSIS is therefore the ideal method for prioritizing options based on the simulation of the ideal answer because it is highly technical and robust in its decision-making approach. Furthermore, it uses the standardization method, equations, mathematical matrices, and suitable weights. Overall, it can be recommended as a suitable efficiency approach for land suitability evaluation.

Mobile based culinary recommendation system has received significant attention in recent mobile application research . Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has regained popularity in supporting multi-criteria decision making due to this method allowing inclusion of many factors and criteria into the decision making process. Previous works on mobile based scenario culinary recommendation system reveal that TOPSIS stand out from other recommendation approaches like AHP and Fuzzy by providing a lightweight computation algorithm that have promising performance in time complexity. However, computing a culinary recommendation using TOPSIS has own limitations especially in the menu judgment processes due to the alternatives priority only include personal preferences for recommendation. In such a culinary recommendation system scenario, users more likely search culinary menus in group instead of alone. This research aims to develop a culinary recommendation system based on group decision support system (GDSS) using TOPSIS that possible to calculate a recommendation by using group preferences instead of personal preferences. The experimental results show that the overall functional of proposed GDSS gives better recommendation result. GDSS using TOPSIS have 100% rank consistency for 6 group of users with 5 combination of menus. The accuracy testing shows that 83,33 % recommendation of GDSS TOPSIS are match with real user preferences. Furthermore, it can be run well in various type of Android smartphone.

Evaluation and optimization of multi-criteria with multiple alternatives have been essential activities for decision-making process. TOPSIS (Technique for order of preference by similarity to ideal solution), a multi-criteria decision-making (MCDM) technique, has been adopted in the past for research & decision-making and ranking of alternatives by optimizing input parameters to get the maximum overall output from the system. This study aims to explore the context, reasons, and particular advantages of using TOPSIS in the materials science and engineering field for realizing goals of competitive supply chains (SCs). This study has reviewed and analyzed research papers from the approach of systematic review of the literature. This study has presented a conceptual framework to emphasize the antecedents and consequences of using the TOPSIS methodology for output optimization in the materials science and engineering industry that can improve the competitiveness of SCs. This study found that TOPSIS based methodologies have been used in eleven types of industries in India, indicating the prowess of TOPSIS methodology. The results of TOPSIS have compared very well with other MCDM methods that are relatively more difficult and cumbersome. This study will help the engineers, practitioners, academicians, researchers, and SC managers with the application approach of TOPSIS for output optimization in various fields.

In the context of isolated photovoltaic (PV) installations, selecting the optimal combination of modules and batteries is crucial for ensuring efficient and reliable energy supply. This paper presents a Decision Support System (DSS) designed to aid in the selection process of the development of new PV isolated installations. Two different multi-criteria decision-making (MCDM) approaches are employed and compared: AHP (Analytic Hierarchy Process) combined with TOPSIS (technique for order of preference by similarity to ideal solution) and Entropy combined with TOPSIS. AHP and Entropy are used to weight the technical and economic criteria considered, and TOPSIS ranks the alternatives. A comparative analysis of the AHP + TOPSIS and Entropy + TOPSIS methods was conducted to determine their effectiveness and applicability in real-world scenarios. The results show that AHP and Entropy produce contrasting criteria weights, yet TOPSIS converges on similar top-ranked alternatives using either set of weights, with the combination of lithium-ion batteries with the copper indium gallium selenide PV module as optimal. AHP allows for the incorporation of expert subjectivity, prioritising costs and an energy yield intuitive to PV projects. Entropy’s objectivity elevates criteria with limited data variability, potentially misrepresenting their true significance. Despite these discrepancies, this study highlights the practical implications of using structured decision support methodologies in optimising renewable energy systems. Even though the proposed methodology is applied to a PV isolated system, it can effectively support decision making for optimising other stand-alone or grid-connected installations, contributing to the advancement of sustainable energy solutions.