Applying TOPSIS Method for Optimal Data Mining Algorithm Selection in Software Defect Prediction Systems

Watershed prioritization using morphometric analysis by MCDM approaches

<p>One common multi-criteria decision making (MCDM) technique is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is frequently applied in several application fields. Finding an ideal and an anti-ideal solution, which are then utilized to determine the distances between the alternatives and the ideal solution, is the foundation of the TOPSIS approach. The method then ranks the alternatives according to their closeness to the ideal solution. TOPSIS is able to handle both quantitative and qualitative criteria, however, the method can be sensitive to the weight of the criteria, and the ranking results can be influenced by the choice of the reference alternatives. This paper provides an overview of the TOPSIS method, its applications, main characteristics and limitations. The paper also provides step-by-step instructions on how to apply the TOPSIS method, including the determination of the criteria weights, the construction of the decision matrix, and the calculation of the TOPSIS scores.</p>

This article describes in detail how optimizing performance studies were measured on the selection of low global warming potential (GWP) refrigerants for automobile air conditioning to obey the environmental protocols with the multi-criteria decision-making (MCDM) tools. Among 14 alternatives, three different types of MCDM tools are used in this study, i.e., the technique for order of preference by similarity to ideal solution (TOPSIS) approach, evaluation based on distance from average solution (EDAS), and multi-objective optimization based on ratio analysis (MOORA) for time consumption. For the data processing, the thermodynamic characteristics, environmental friendliness, and economic circumstances are optimized with the multiple response attributes, including the latent heat of vaporization, thermal conductivity, vapor pressure, saturated fluid density, specific heat capacity, dynamic viscosity, GWP, ozone depletion potential, and cost per pound. From MCDM techniques, the grade values and weights assigned by decision-makers are standardized into a comparable measure. Among the 14 refrigerants, R430A has been applied foremost in EDAS, TOPSIS, and MOORA methods with the highest closeness coefficient values of 0.786, 0.701, and 0.064, respectively. R-744 was found to be the worst refrigerant by both EDAS and TOPSIS methods, while R1233zd (E) was found to be the worst refrigerant by the MOORA method.

PurposeThe purpose of this study is to provide a decision support tool to deal with the problem of seting priorites among patients competing for limited health care resources. Limited resources and unlimited demands prevent health-care services to be provided to all those in need. This became publicity evident with the current Covid-19 pandemic. Although controversial, health care rationing has always existed and is now inevitable. Setting priorities becomes then inevitable. How to define those priorities is a complex and yet irresolvable issue mainly because it involves several and conflicting criteria, translated into efficiency and equity considerations. This is why multi-criteria decision analysis (MCDA) was introduced to health care as an appropriate decision-support framework for solving complex problems.Design/methodology/approachThis paper proposes the application of two combined approaches – analytic hierarchy process (AHP)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and AHP-VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), as decision support tools to rank patients with competing needs in a more effective and equitable way. A rationing scenario involving four patients, differentiated by personal characteristics and health conditions, is used to illustrate, test and compare the applicability of both approaches. After extraction of the relative weights of the prioritization criteria involved in the hypothetical scenario from paired wise comparison methods, TOPSIS and VIKOR priority setting methods were designed.FindingsResults suggest that patients ranking from both combination approaches are similar and in accordance with the order made directly by health-care professionals. Therefore, the relative weights computed by AHP in combination with TOPSIS and/or VIKOR methods could be used with suitable applicability by health-care decision-makers.Originality/valueThis study is the first attempt to apply a combination of MCDA methods to patients’ prioritization context and the first to cross previous studies to deepen and consolidate the research.

The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that can support decision-making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are among the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision-making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. The geographical criteria are geology, land use, sediment, erosion, slope, groundwater, and discharge. The water quality criteria include Soluble Sodium Percentage, Total Dissolved Solid, Potential of Hydrogen, and Electrical Conductivity of water. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this study area. Actual locations of dams constructed in the area were used to verify the results of both methods.

The importance of disaster logistics and its share in the logistics sector are increasing significantly. Most disasters are difficult to predict; therefore, a set of measures seems to be necessary to reduce the risks. Thus, disaster logistics needs to be designed with the pre-disaster and post-disaster measures. These disasters are experienced intensely in Turkey and the importance of these measures becomes more evidential. Therefore, accurate models are required to develop an effective disaster preparedness system. One of the most important decisions to increase the preparedness is to locate the centres for handling material inventory. In this context, this paper analyses the response phase designing the disaster distribution centres in Turkey at the provincial level. AHP (Analytical Hierarchy Process) based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and goal programming model integration is used to decide alternative locations of distribution centres. TOPSIS method is employed for ranking the locations, which is based on hazard scores, total area, population, and distance to centre. Two conflicting objectives are first proposed in the goal programming formulation, in which maximization of the TOPSIS scores and minimization of the number of distribution centres covering all demands named set covering model are included. Although Gecimli has the highest priority with 0.8 p score in the TOPSIS ranking, Altincevre (0.77) and Buzlupınar (0.75) ensure both the TOPSIS score and coverage of the demand nodes. The results from this paper confirm that the computational results ensure disaster prevention insights especially in regions with limited data.

The multi-criteria decision-making (MCDM) method often entails a number of steps, such as identifying the decision problem, defining the criteria, calculating the weighting of the criteria, assessing the alternatives against the criteria, and finally choosing the best alternative based on the overall evaluation. In this paper, the essential factor for sports, politics, journalism or media, and technological empowerment of women in India are found by MCDM techniques, which may also be utilized to examine the key factors influencing women’s empowerment. The uses the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) MCDM methodologies are taken to discover the optimal criteria for empowerment. The decision-making for finding the factors is done by taking multi experts’ opinions. First, determined the best criteria among the six criterion’s, such as Family Support (C1), Financial Support (C2), Cultural and Religious Barrier (C3), Child Marriage (C4), Nutrition (C5), and Shame and concealment (C6). The weights are calculated by the AHP method. According to the importance of the criteria, select a significant alternative for empowering women in many fields. Also take six possibilities into consideration, which are Empowerment in Sports (A1), Empowerment in Politics (A2), Empowerment in Administrative Service (A3), Empowerment of Journalism or Media (A4), Empowerment for Social Work (A5) and Empowerment of Technological (A6) and they are ranked by TOPSIS method. To verify the validity and robustness of our result, sensitivity analysis is performed by removing some criteria to make sure the procedure is reliable and a comparative study using a fuzzy environment.

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.

Dam site selection is an important task in water resource management and it depends on a number of biophysical as well as socio-economic factors. Dam site selection is becoming important research area due to the increase of water scarcity and also to prevent flood in certain area. This paper presents Multi Criteria Decision Making (MCDM) approach in water resources management through the integration of Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). These methods were applied for identification of suitable dam site in Tlawng sub-basin. The influential criteria for dam site selection were initially identified through literature review and experts opinion which was followed by the selection of four (4) feasible sites after having studied Tlawng watershed. Lastly, AHP and TOPSIS were applied to determine the relative weight and rank respectively.

A research on the cutting database system based on machining features and TOPSIS

The fuzzy TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) is an attractive tool for measuring complex phenomena based on uncertain data. The original version of the method assumes that the object assessments in terms of the adopted criteria are expressed as triangular fuzzy numbers. One of the crucial stages of the fuzzy TOPSIS is selecting the fuzzy conversion scale, which is used to evaluate objects in terms of the adopted criteria. The choice of a fuzzy conversion scale may influence the results of the fuzzy TOPSIS. There is no uniform approach in constructing and selecting the fuzzy conversion scale for the fuzzy TOPSIS. The choice is subjective and made by researchers. Therefore, the aim of the article is to present a new, objective approach to the construction of fuzzy conversion scales based on Item Response Theory (IRT) models. The following models were used in the construction of fuzzy conversion scales: Polychoric Correlation Model (PM), Polytomous Rasch Model (PRM), Rating Scale Model (RSM), Partial Credit Model (PCM), Generalized Partial Credit Model (GPCM), Graded Response Model (GRM), Nominal Response Model (NRM). The usefulness of the proposed approach is presented on the example of the analysis of a survey’s results on measuring the quality of professional life of inhabitants of selected communes in Poland. The obtained results indicate that the choice of the fuzzy conversion scale has a large impact on the closeness coefficient values. A large difference was also observed in the spreads of triangular fuzzy numbers between scales based on IRT models and those used in the literature on the subject. The use of the fuzzy TOPSIS with fuzzy conversion scales built based on PRM, RSM, PCM, GPCM, and GRM models gives results with a greater range of variability than in the case of fuzzy conversion scales used in empirical research.

The TOPSIS method in the Decision Support System for Determining Outstanding Students at SMP XX explains that this method is used to select alternatives (students) who have the closest distance to the positive ideal solution and the furthest from the negative ideal solution. This method considers criteria such as academic grades, activeness in extracurricular activities, and disciplinary attitudes. By implementing TOPSIS, it is hoped that objective and accurate decisions can be made in determining outstanding students. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method was chosen because of its reliability in overcoming complexity and uncertainty in multi-criteria decision making. Eka got a preference value of 0.31144246, Elsa got a preference value of 0.62095919, Frits got a preference value of 0.80287008, and Ninik got a preference value of 0.38000306. With the results of these preference values, it can be determined that Frits gets rank 1, Elsa gets rank 2, Ninik gets rank 3, and Eka gets rank 4.

Appropriate tree fertilization with essential nutrients is considered as one of the major factors in enhancing the quality and quantity of horticultural crops. The most efficient way to fertilize trees is to dig holes around the trunks and fill them with appropriate chemical and organic fertilizer. Doing this operation with mechanized methods reduces costs and increases productivity compared to traditional methods. In the present study, multi‐criteria decision‐making (MCDM) methods, including deterministic analytical hierarchy process (AHP) and fuzzy analytical hierarchy process (FAHP), technique for order of preference by similarity to ideal solution (TOPSIS), fuzzy TOPSIS (FTOPSIS), and analytic network process (ANP), were used to score and select the appropriate fertilizing method for apple trees based on the growers and expert's perspectives. The criteria, including fertilizing operation cost, crop yield, the percentage of tree damages, ease of entering and moving fertilizing equipment in tree rows, field capacity (with or without machinery), comfort and safety of fertilizing operations, after‐sales service, access to the required machinery and implements, crop selling price, and crop quality, were used in the above‐mentioned methods. The fertilization methods (Hole digging) considered in the present study were traditional fertilization (Shovel), orchard Trencher, motor hole digger, fixed centerline tractor‐mounted hole digger, and off‐set tractor‐mounted hole digger. Based on the results, the priority of mechanized fertilizing methods was determined as tractor‐mounted hole diggers (AHP weight of 0.286, FAHP weight of 0.285, TOPSIS relative proximity of 0.65, and FTOPSIS relative proximity of 0.64), fixed centerline tractor‐mounted hole diggers (AHP weight of 0.219, FAHP weight of 0.158, TOPSIS relative proximity of 0.56, and FTOPSIS relative proximity of 0.62), motor hole diggers (AHP weight of 0.171, FAHP weight of 0.079, TOPSIS relative proximity of 0.46, and FTOPSIS relative proximity of 0.31), and orchard trenchers (AHP weight of 0.12, FAHP weight of 0.057, TOPSIS relative proximity of 0.19, and FTOPSIS relative proximity of 0.20), respectively. Based on the ANP method, off‐set and fixed centerline tractor‐mounted hole diggers had the highest priority (weights of 0.43 and 0.27), followed by trencher (weight of 0.16), motor hole diggers (weight of 0.09), and the traditional method (weight of 0.04). Results showed that applying orchard tractors equipped with mounted diggers, especially off‐set types, can play an important role in enhancing the quantity and quality of apples produced, as well as reducing the costs of fertilizing operations.

Considering the high energy and material consumption, the environmental impact of additive manufacturing through FDM has faced significant criticism. For a more sustainable production process, industries require efficient optimization of the FDM process to lower environmental impact while retaining process efficiency. This study utilizes advanced multi-criteria decision-making (MCDM) methodologies, specifically the fuzzy analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS), to evaluate and enhance the environmental performance of FDM. Focusing on standard thermoplastic materials (e.g., PLA and PETG) and applications such as functional prototyping, we optimize key parameters layer height, print speed, and infill density to achieve reductions in energy usage (20%) and material waste (15%) compared to baseline FDM practices. These findings not only highlight a pathway toward greener FDM processes but also lay the groundwork for future research in sustainable optimization frameworks, applicable to other additive manufacturing methods and materials.

Application of AHP, TOPSIS, and TFNs to plant selection for phytoremediation of petroleum-contaminated soils in shale gas and oil fields