Technique For Order Preference By Similarity To Ideal Solution Method Research Articles

ЗАСТОСУВАННЯ МЕТОДУ БАГАТОКРИТЕРІАЛЬНОЇ ОПТИМІЗАЦІЇ ДЛЯ ПРИЙНЯТТЯ РІШЕНЬ У СФЕРІ ФІНАНСОВОГО ЗАБЕЗПЕЧЕННЯ ВІЙСЬК

The article considers the theoretical basis for the use of optimisation methods for managerial decision-making and search for an optimal solution. The article describes the methods of multi-criteria optimisation, which, based on the analysis of the relevant literature, are often used in various spheres of human activity, namely: simple additive weighting (SAW), elimination and choice expressing the reality (ELECTRE), preference ranking organisation method for enrichment evaluation (PROMETHEE), technique for order preference by similarity to ideal solution (TOPSIS), analytical hierarchy process (AHP), and analytical network process (ANP). The key characteristics of the decision-making environment in the field of military financial support are identified, based on the conditions and characteristics of such a decision-making environment, namely: a significant number of criteria to be taken into account, the presence of subjectivity in decision-making and the efficiency of such decisions, TOPSIS was chosen as the main method. In the methodological part of the article, the main characteristics, procedure of use and indicators of this method are considered. For practical application of the method, the article develops a case of decision-making by the military organisation A0001 regarding the optimal use of a limited financial resource out of 4 proposed options. In the article, a list of criteria was compiled, they were grouped, and a matrix was formed. In the final part of the article, a matrix of weighting coefficients for each of the 4 options is formed, its normalisation is carried out and the distance/closeness of the proposals of further solutions to the ideal solution is calculated. Based on the calculations, the author chose the optimal option for the use of financial resources, which, according to the TOPSIS method, had the smallest distance to the ideal solution, i.e., was the closest of the proposed options to the ideal option for the use of financial resources.

Intuitionistic fuzzy-based TOPSIS method for multi-criterion optimization problem: a novel compromise methodology

<abstract><p>The decision-making process is characterized by some doubt or hesitation due to the existence of uncertainty among some objectives or criteria. In this sense, it is quite difficult for decision maker(s) to reach the precise/exact solutions for these objectives. In this study, a novel approach based on integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the intuitionistic fuzzy set (IFS), named TOPSIS-IFS, for solving a multi-criterion optimization problem (MCOP) is proposed. In this context, the TOPSIS-IFS operates with two phases to reach the best compromise solution (BCS). First, the TOPSIS approach aims to characterize the conflicting natures among objectives by reducing these objectives into only two objectives. Second, IFS is incorporated to obtain the solution model under the concept of indeterminacy degree by defining two membership functions for each objective (i.e., satisfaction degree, dissatisfaction degree). The IFS can provide an effective framework that reflects the reality contained in any decision-making process. The proposed TOPSIS-IFS approach is validated by carrying out an illustrative example. The obtained solution by the approach is superior to those existing in the literature. Also, the TOPSIS-IFS approach has been investigated through solving the multi-objective transportation problem (MOTP) as a practical problem. Furthermore, impacts of IFS parameters are analyzed based on Taguchi method to demonstrate their effects on the BCS. Finally, this integration depicts a new philosophy in the mathematical programming field due to its interesting principles.</p></abstract>

Decision Support System to Determine the Best Employees at Yogi Banyumas Repair Shop using Topsis Method

The assessment system to determine the best employees at the Yogi shop has not been systemized. It is still limited to the regular assessment assessing the best employees to get an annual bonus based on the owner and manager's assessment, so it seems less accurate and less targeted towards employees who will be given annual bonuses. This study aims to build a decision support system to determine the best employees using the TOPSIS method to make it easier for owners and managers to determine the best employees. The data processing method in this study is the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The criteria used in this research are attendance, work attitude, responsibility, discipline, cooperation and creativity. From the results of the tests, it concluded that the blackbox testing process was successful. The results of this study are a decision support system to determine the best employees at the Yogi Banyumas store.

FMEA Based Fuzzy Topsis for Assessment of Quality Problems in Telescopic Platform Production

In order to increase process and product quality fast and least costly, it is very useful to focus on the process that causes the important quality problems. In this study, quality problems and related processes in a company that produces on-vehicle equipment in the automotive industry are examined. The purpose of this study is to provide a quality improvement perspective to reduce process and product defects. Fault Mode and Effects Analysis (FMEA) is applied to detect faults, determine their causes and prioritize them. Prioritization of critical quality errors is revealed using traditional FMEA and FMEA-based fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The FMEA-based fuzzy TOPSIS method has been proposed because of its ability to handle uncertainty arising from the subjective judgments of decision makers. The results are compared and the preventions are suggested to avoid from the most important process that causes the quality problem.

Penerapan Metode ROC-TOPSIS dalam Keputusan Penerima Program Keluarga Harapan

Poverty is one of the most difficult social problems to be solved by the Indonesian government, even in other words, the problem cannot be eliminated. So that the Indonesian government seeks to reduce the level of poverty by making social assistance programs to people who have economic problems. There are many aid programs set by the Indonesian government, one of which is the Family Hope Program (PKH). The Decision Support System is applied in this study as a system for the decision of the recipient of the family of hope. In determining the decision of the recipient of the right family program, it must meet the criteria for Poor Card Ownership, Employment, Income, Number of Dependents and Home Ownership Status. Therefore, a Decision Support System (DSS) is needed in solving existing problems by applying the ROC (Rank Order Centroid) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods which can generate weight and preference values ​​from the first ranked alternative. . So that the recipient of the family of hope program lies in alternative A2 on behalf of Joko Sutirno with a value of 1.0000.

The TOPSIS method to identify the optimal machining condition with reduced particle emission during machining of the Al-Si based 1%SiC reinforced nanocomposite material

The machining was done following the Taguchi’s L9 orthogonal array. The work - piece was Al-Si based silicon carbide nanoparticle reinforced nanocomposite material. The objective of the present study is to identify the machining condition such that the emission of the particle during the machining of the nanocomposite is minimized. The health problem arises due to the emission of the particle during the machining of the nanocomposite. The machining was performed on the lathe by using the Tungaloy made carbide tool insert. The speed (v), feed (f) and depth of cut (DOC, d) were the machining parameters. The chip reduction coefficient (CRC) and surface roughness (Ra) were the measured output responses. The scanning electron microscopy was performed for the chip surfaces. The TOPSIS (Technique for order preference by similarity to ideal solution) method was applied to optimize the machining parameters. The method is very effective to optimize the machining parameters. The analysis of variance (ANOVA) was done to find out the influential parameters for CRC and Ra minimization. The optimal solution was found for v = 122.02 m/min, feed = 0.06 mm/rev., and d = 1.5 mm. The optimal solution was validated with a qualitative assessment. This ensured the novelty of the work. The studies on chip types, machined surfaces etc. further indicated that the TOPSIS method was very effective in identifying the optimal parameters with reduced particle emission.

Complex spherical fuzzy TOPSIS based on entropy

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a traditional approach, which is quite powerful since it shows the best and worst alternatives at the same time, for solving multi-criteria group decision-making (MCGDM) problems. On the other hand, the theory of complex spherical fuzzy set (CSFS) has a noticeable advantage over the other set theories which process the uncertain data since this theory includes the abstinence part of uncertain information in addition to satisfaction and dissatisfaction in two-dimensional data. This paper proposes to merge the feasible and remarkable structure of the CSFS with the excellent tendencies of the traditional TOPSIS where the information about the weights of both the decision-makers (DMs) and criteria are completely unknown. So, we establish a novel TOPSIS method under the complex spherical fuzzy environment by calculating the weights of both the DMs and criteria objectively with the novel entropy measure function that we constructed for this method. Furthermore, we give a numerical example to explain the proposed method step by step. Finally, we compare the solutions to the current problem with the various existing MCGDM methods to provide the capabilities and validity of the presented method. We also give a sensitivity analysis by changing the entropy to demonstrate the robustness of the weights of the criteria under the prevailing entropy measure function.

Pre-assessment for ice disaster in Ning-Meng reaches of the Yellow river based on improved TOPSIS under three-parameter interval grey number

The occurrence of ice disasters in Ning-Meng Reaches of the Yellow River in China is affected by a variety of factors. The interaction between the factors has a certain degree of uncertainty. It is a complex multi-factor interaction process. So pre-disaster assessment to improve the effectiveness of prevention and mitigation is essential. To address such a challenging problem, three critical contributions are provided in this paper. First, according to the value characteristics of the three-parameter interval grey number, aiming at the grey characteristics of ice disasters, the concept of compatibility degree of the three-parameter interval grey number is defined. Second, according to the “new information priority principle” of grey system theory, an optimized model based on variable coefficient and time degree is built to determine the weight of the time point. Finally, the shortcomings of the classical Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method are analyzed. Combined with the compatibility degree of the three-parameter interval grey number, the weighted relative compatibility degree of the scheme is defined; the scheme is sorted according to the weighted relative compatibility degree. To verify the efficacy and practicality of this proposed model, data sets from the ice disaster data (2014–2020) are employed in the experiments. The experimental results show that the decision results of the improved TOPSIS method are more consistent with the actual situation than the results of the classical TOPSIS method. Moreover, the improved TOPSIS method can be applied to the case where the decision information is a three-parameter interval grey number and to the case where the decision information is a real number. It shows that the modified TOPSIS method has a wider application range than the classical TOPSIS method. The rationality of the results is analyzed based on the evaluation results, which provides a better reference for improving the prevention and management of ice disasters.

A robust method for avoiding rank reversal in the TOPSIS

The ability to obtain robust ranking results is an important reference factor when choosing a method for multiple criteria decision-making (MCDM). However, the rank reversal phenomenon of MCDM makes its credibility greatly compromised, as represented by the technique for order preference by similarity to ideal solution (TOPSIS). Although some research papers have presented interesting ideas and solutions to minimize/void rank reversal in relation to the TOPSIS, there are still some gaps that need further research, such as do not utterly eliminate the rank reversal, need more additional decision information, cannot theoretically guarantees the stability of ranking. Therefore, an improved TOPSIS method, named IE-TOPSIS, is developed based on information expansion and virtual ideal points. An algorithm for the extreme value extension of criteria indicators is initially constructed, based on the existing decision-making information, which can effectively avoid the influence of the data standardization process on decision-making ranking. And then, the concept of a virtual ideal point is proposed, which can prevent the rank reversal caused by the change of ideal points in varying decision-making environments. At the same time, this paper proves that the IE-TOPSIS satisfies ranking stable according to the given definition of ranking robust, which theoretically guarantees that the rank reversal phenomenon does not exist. In the case where the IE-TOPSIS is established, a new ranking index is proposed that can better distinguish the evaluation objects and solve the problem that the classic TOPSIS ranking index cannot completely rank the decision objects. Finally, in the verification of examples, the ranking results show that the improved TOPSIS has strong consistent with the theoretical analysis, and is robust to handle rank reversal phenomenon in all the scenarios in comparison with other studies available in the literature.

Selection of Soybean Genotypes under Drought and Saline Stress Conditions Using Manhattan Distance and TOPSIS.

The search for soybean genotypes more adapted to abiotic stress conditions is essential to boost the development and yield of the crop in Brazil and worldwide. In this research, we propose a new approach using the concept of distance (or similarity) in a vector space that can quantify changes in the morphological traits of soybean seedlings exposed to stressful environments. Thus, this study was conducted to select soybean genotypes exposed to stressful environments (saline or drought) using similarity based on Manhattan distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. TOPSIS is a multi-criteria decision method for selecting the best alternative using the concept of distance. The use of TOPSIS is essential because the genotypes are not absolutely similar in both treatments. That is, just the distance measure is not enough to select the best genotype simultaneously in the two stress environments. Drought and saline stresses were induced by exposing seeds of 70 soybean genotypes to -0.20 MPa iso-osmotic solutions with polyethylene glycol-PEG 6000 (119.6 g L-1) or NaCl (2.36 g L-1) for 14 days at 25 °C. The germination rate, seedling length, and seedling dry matter were measured. We showed here how the genotypic stability of soybean plants could be quantified by TOPSIS when comparing drought and salinity conditions to a non-stressful environment (control) and how this method can be employed under different conditions. Based on the TOPSIS method, we can select the best soybean genotypes for environments with multiple abiotic stresses. Among the 70 tested soybean genotypes, RK 6813 RR, ST 777 IPRO, RK 7214 IPRO, TMG 2165 IPRO, 5G 830 RR, 98R35 IPRO, 98R31 IPRO, RK 8317 IPRO, CG 7464 RR, and LG 60177 IPRO are the 10 most stable genotypes under drought and saline stress conditions. Owing to high stability and gains with selection verified for these genotypes under salinity and drought conditions, they can be used as genitors in breeding programs to obtain offspring with higher resistance to antibiotic stresses.

DECISION SUPPORT SYSTEM FOR DETERMINING CUSTOMER FEASIBILITY TO GRANT CREDIT ON SAVING AND LOAN COOPERATIVES USING COMPARISONS OF TOPSIS AND SAW METHOD

Cooperative is one of the institutions engaged in the financial sector with the business of providing savings and loan funds to its members to improve the welfare of the community's economy however currently there are still many errors that occur in the calculation process and it takes a very long time, so we need a system that can simplify the selection process in determining a decision. This research aims to support a decision support system for determining the eligibility of customers by comparing the two, so that it can be determined which method is more relevant to be implemented in the case of customers selection in grant credit. In its application, this research uses a comparison of two methods, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) methods. At the system design stage, the design model and system development flow are used as an illustration in the operation of the decision support system that has been carried out based on system analysis. Based on the results of the research and discussion, it can be concluded that the comparison of TOPSIS and SAW methods has inequality in the final results that can be seen from the ranking process. The test results show that the SAW method produces a value of 0.82% for customers named lintang with the highest, while TOPSIS with a value of 0.65% for highest value, so that the SAW method is more recommended in providing solutions to the decision support system.

A robust TOPSIS method for decision making problems with hierarchical and non-monotonic criteria

This paper introduces an extension of a well-known Multiple Criteria Decision Aiding method, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Most of the TOPSIS applications assume that preferences are monotonic for each evaluation criterion and that qualitative scales are converted into quantitative ones before the method is applied. However, both assumptions have been subject of discussion and criticism in the literature. To this solution, this paper introduces a normalization technique based on simulations that permit taking into account non-monotonic preferences as well as qualitative criteria. An additional novelty lies in the integration of the Multiple Criteria Hierarchy Process, which extends the applicability of the method to problems in which criteria are hierarchically structured. To deal with robustness concerns, the Stochastic Multicriteria Acceptability Analysis will be used in the new proposal, giving information in statistical terms on the goodness of the considered alternatives. The new method has been applied to evaluate a set of banks listed in the LSE’s FTSE350 Index.

The evaluation of the effects of the COVID-19 pandemic on the Turkish manufacturing sector using AHP-TOPSIS methods

PurposeThe purpose of this study is to evaluate the effects of COVID-19 on the Turkish manufacturing sector by using analytical hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods on a sectoral basis. In this way, the sectors that had the highest negative effects of COVID-19 will be determined. Thanks to the findings to be obtained as a result of this study, it will be possible for decision-makers to develop strategies that may differ on a sectoral basis at the point of combating the pandemic.Design/methodology/approachThe negative effects of COVID-19 were revealed by a large literature search. The importance levels of the factors determined within the scope of finance, supply, production and workforce were determined by the AHP method. Then, the TOPSIS method was used to determine which sectors were more affected by the COVID-19 pandemic, based on these factors.FindingsThe findings of this study obtained reveal that the most important negative effect of COVID-19 was on businesses in terms of reduced sales caused by domestic sales and export. Again, the findings show that the sector most affected by COVID-19 is automotive, which shows that the least affected sector is the pharmaceutical and medical equipment sector.Practical implicationsPrimary data was used to determine the criteria weight. Therefore, there is a possibility that the answers are not objectively evaluated. The findings are limited to the criteria that was used, the views of the professionals and AHP-TOPSIS methods. Studies that use more criteria and different decision-making methods can have different results.Originality/valueThe manufacturing sector is at the forefront of the sectors affected by the global bottleneck caused by COVID-19. In this study, the effects of the COVID-19 pandemic on the Turkish manufacturing sector are discussed on a sectoral basis. Studies in the literature have revealed that the effects of pandemics such as COVID-19 vary between sectors. For this reason, it is important to evaluate the effects of COVID-19 from a sectoral perspective. Thanks to this study, it is thought that it will be possible to take measures that may differ between sectors to combat the negative effects of COVID-19.

A Novel Multi-Objective Optimal Design Method for Dry Iron Core Reactor by Incorporating NSGA-II, TOPSIS and Entropy Weight Method

Dry iron core reactors are widely used in various power quality applications. Manufacturers want to optimize the cost and loss simultaneously, which is normally achieved by the designers’ experience. This approach is highly subjective and can lead to a non-ideal product. Thus, an objective dry iron core reactor design approach to balance the cost and loss with a scientific basis is desired. In this paper, a multi-objective optimal design method is proposed to optimize both the cost and loss of the reactor, which provides an automatic and scientific design method. Specifically, a three-dimensional finite element model of dry iron core reactor is established, based on which the dependency of cost and loss upon the wire size of the reactor’s winding is studied by using joint Matlab-finite element method (FEM) simulation. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to search for the Pareto optimal solution set, out of which the optimal wire size of the reactor is determined by using the fusion of the technique for order preference by similarity to ideal solution (TOPSIS) method and the entropy weight method. TOPSIS helps the designer to balance the concern between cost and loss, while the entropy weight method can determine the weight information through the dispersion degree of cost and loss. This methodology can avoid personal random subjective opinion when selecting the design solution out of the Pareto set. The calculation shows that the cost and loss can be reduced by up to 17.85% and 19.45%, respectively, with the proposed method. Furthermore, the obtained optimal design is approved by experimental tests.

Spatial Differentiation and Driving Mechanism of Agricultural Multifunctions in Economically Developed Areas: A Case Study of Jiangsu Province, China

Revealing the spatial patterns of differentiation and the driving mechanism of agricultural multifunctional patterns is an important aspect of coordinating the functional optimisation and coordinated development of different agricultural regions. On the basis of understanding the connotation of agricultural multiple functions, this paper constructed an evaluation index system of agricultural multiple functions. Taking Jiangsu Province as a typical case, the spatial patterns of agricultural multifunctions in Jiangsu since 1978 were analysed by using the entropy weight TOPSIS (technique for order preference by similarity to ideal solution) method and ESDA (exploratory spatial data analysis) model, and the influencing mechanism of agricultural multifunction spatial differentiation was revealed by a geographic detector model. The results showed that (1) the cities with higher agricultural grain production functions were mainly concentrated in Yancheng and Huai’an; cities with higher agricultural economic development functions were mainly distributed in the coastal areas of Jiangsu; cities with higher agricultural social security functions were mainly concentrated in the Suzhou–Wuxi–Changzhou metropolitan area; and cities with higher agricultural ecotourism functions evolved from Nanjing–Zhenjiang to Suzhou–Wuxi–Changzhou. (2) The H–H (high–high) cluster pattern of the agricultural grain production function shifted from southern Jiangsu to northern Jiangsu. The H–H clusters of the agricultural economic development function and social security function were mainly distributed in Suzhou–Wuxi–Changzhou, while the L–L (low–low) cluster was mainly distributed in northern Jiangsu. The H–H cluster of agricultural ecotourism functions was mainly distributed in the areas with rich mountain and hill resources or dense water networks in Jiangsu. (3) The agricultural multifunction pattern differentiation was affected by the natural environment and economic and social comprehensive factors; the level of economic development and population employment structure were the leading factors of agricultural multifunction spatial differentiation; industry structure and people’s living conditions were the important driving forces of agricultural multifunction spatial differentiation; and the natural environment and population density were the basic factors underlying agricultural multifunction spatial differentiation.

Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91

Magnesium (Mg) alloys are extensively used in the automotive and aircraft industries due to their prominent properties. The selection of appropriate process parameters is an important decision to be made because of the cost reduction and quality improvement. This decision entails the selection of suitable process parameters concerning various conflicting factors, so it has to be addressed with the Multiple Criteria Decision Making (MCDM) method. Therefore, this work addresses the MCDM problem through the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and COPRAS (COmplex PRoportional ASsessment) methods. The assessment carried out in the material Mg AZ91 with the Solid Carbide (SC) drill bit. The dependent parameters like drilling time, burr height, burr thickness, and roughness are considered with the independent parameters like spindle speed and feed rate. Drilling alternatives are ranked using the above said two methods and the results are evaluated. The optimum combination was found on the basis of TOPSIS and COPRAS for simultaneous minimization of all the responses which is found with a spindle speed of 4540 rpm and a feed rate of 0.076 mm/rev. The identical sequencing order was observed in TOPSIS and COPRAS method. The empirical model was developed through Box-Behnken design for each response. Superior empirical model developed for drilling time which is 3.959 times accurate than the conventional equation. The trends of various dependents based on the heterogeneity of various independents are not identical, these complex mechanisms are identified and reported. The optimized results of the Desirability Function Approach are greater accordance with the TOPSIS and COPRAS top rank. The confirmation results are observed with lesser deviation suggesting the selection of the above independent parameters.

Fuzzy TOPSIS framework for promoting win-win project procurement negotiations.

In recent years, organizations worldwide have widely applied the project approach in business and value delivery. Negotiation is essential to the success of a project; however, it has not been explored systematically in the project context. A gap remains between knowledge and practical behavior during negotiation settlements throughout projects. Many project procurement (PP) negotiations do not work as expected. This study develops a practical framework using the scientific method to help close the gap and improve PP negotiations. The proposed framework uses the fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method to integrate the PP management process (PPMP) and the three-phase negotiating model. Through this approach, notable variables and potential solutions under uncertain negotiation situations are quantitatively examined in the early stage and managed until the completion of PP. Thus, expected agreements can be obtained in a timely and efficient manner, with negotiating parties committing to implementing what has been agreed on. Such a commitment facilitates win-win outcomes. An example is presented to demonstrate how the proposed framework operates, and practical implications for managers of project-based organizations are offered. This study provides researchers and practitioners with a foundation to study refined models to enhance project negotiations with interdisciplinary integration.

TOPSIS Model for evaluating the corporate environmental performance under intuitionistic fuzzy environment

With global resource waste and environmental pollution becoming increasingly serious, corporate environmental performance (CEP) has received much attention from researchers over the past decade. As an important part of economic development, enterprises also pay increasingly attention to environmental protection and pollution control. CEP is regarded as the result of corporate environmental management. Assessing CEP can not only make enterprises focus on the environmental protection and management, but also promote sustainable social development. And it is frequently viewed as a multi-attribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is needed to tackle it. Depending on the conventional TOPSIS (Technique for Order Preferenceby Similarity to Ideal Solution) method and intuitionistic fuzzy sets (IFSs), this essay design a novel intuitive distance based IF-TOPSIS method to assess CEP. First of all, a related literature review is conducted. What’s more, some necessary theories related to IFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria is decided objectively by utilizing CRITIC method. Afterwards, relying on novel distance measures between IFNs, the conventional TOPSIS method is extended to the intuitionistic fuzzy environment to calculate assessment score of each enterprise. Eventually, an application about CEP evaluation and some comparative analysis have been given to demonstrate the superiority of the designed method. The results illustrate that the designed framework is useful for assessing CEP.

EVT-Based Methods as Support for the Construction of a Synthetic Index of Socio-Economic Development for Territorial Units

Socio-economic development is an important multidimensional issue and its measurement is complex. This process reveals many problems that need to be resolved, such as data availability, selection of indicators and their measurement, selection of appropriate methods for normalization, weighing and aggregation of indicators. The main problem with the real data are atypical observations. In our research, we deal with the solution of this problem. We propose a comprehensive linear ordering procedure to assess the level of socio-economic development of various types of territorial units. It is based on the positional version of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and methods of Extreme Value Theory (EVT). In the first stage, we propose to use a two-step procedure based on six automatic methods to identify the tail of the data distribution (atypical observations). In the second stage, we suggest to use the positional TOPSIS method to aggregate indicators and eliminate the impact of asymmetry, mainly in the central part of the data distribution.

TOPSIS VE AHP ÇOK KRİTERLİ KARAR VERME YÖNTEMLERİNİN PERSONEL SEÇİM SÜRECİNE UYGULANMASI

İşletmelerde yönetim faaliyetinin temelini karar verme ve problem çözme süreci oluşturmaktadır. İlkel zamanlardan, günümüze kadar bireyler çeşitli durumlarda karar verme problemiyle karşı karşıya kalmışlardır. Karar verme, alternatifler arasından seçim yapma işidir. Geleneksel kararlarda, karar süreci ile ilgili veriler toplanır ve analiz edilerek sonuç değerlendirmeye gidilir. Ancak günlük hayatta insanların karşılaştıkları durumlar veya sorunlar ile ilgili kararlar genellikle birden fazla ve kendi içinde çelişen kriterlere sahiptir. Bu durum göz önüne alındığında, karara ulaşmak için sezgisel yöntemler kullanılması yetersiz kalmaktadır. Çok yönlü karar verme problemleri hayatın her alanında işletmeler ve/veya organizasyonların karşısına çıkmaktadır. Karar vericiler çoğunlukla birden fazla faktörün ve farklı ölçeklenebilirliğe sahip kriterlerin bulunduğu durumlarda karar vermektedir. Çok Kriterli Karar Verme (ÇKKV), karar vericinin sonlu ve sayılabilir yahut sayılamaz sayıda alternatiften oluşan bir küme içinde minimum iki kriter kullanarak yapmış olduğu seçim işlemi olarak tanımlanabilir. Özellikle stratejik seviyede bulunan yöneticilerin karşılaştığı karmaşık karar problemlerde en uygun kararı vermesi için Çok Kriterli Karar Verme süreçlerini işletmesi kolaylıklar sağlayacaktır. Çok kriterli karar verme teknikleri, farklı ölçeklenebilirliğe sahip birden fazla kriter kullanılarak “en uygun” çözüme ulaşmayı hedefleyen yöntemlerden oluşur. Bu çalışmada, iki farklı ÇKKV süreci birlikte işletilerek yöneticinin karar verme ihtiyacını karşılayan bir Karar Destek Sistemi önerilmektedir. Bu kapsamda, AHP (Analytic Hierarcy Process) ve TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) ÇKKV yöntemleri birlikte kullanılmıştır. Seçim kriterlerinin ağırlıklarının belirlenmesi için AHP yöntemi, belirlenen kriterler ışığında adayların sıralanması için TOPSIS yöntemi kullanılmıştır. Bahsi geçen her iki ÇKKV yöntemi de “Kıyaslanamazlığın dışlandığı tek ölçütlü sentez yaklaşımı” olarak nitelendirilmektedir.