scholarly journals A Comparative Study on Decision Making Methods with Interval Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Aditya Chauhan ◽  
Rahul Vaish
Keyword(s):  
Decision Making ◽  
Input Data ◽  
Multiple Criteria Decision Making ◽  
Interval Data ◽  
Cross Entropy ◽  
Ideal Solution ◽  
Order Preference ◽  
Topsis Technique ◽  
Selection Of ◽  
Novel Algorithm

Multiple Criteria Decision Making (MCDM) models are used to solve a number of decision making problems universally. Most of these methods require the use of integers as input data. However, there are problems which have indeterminate values or data intervals which need to be analysed. In order to solve problems with interval data, many methods have been reported. Through this study an attempt has been made to compare and analyse the popular decision making tools for interval data problems. Namely, I-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), DI-TOPSIS, cross entropy, and interval VIKOR (VlseKriterijumska Optimiza-cija I Kompromisno Resenje) have been compared and a novel algorithm has been proposed. The new algorithm makes use of basic TOPSIS technique to overcome the limitations of known methods. To compare the effectiveness of the various methods, an example problem has been used where selection of best material family for the capacitor application has to be made. It was observed that the proposed algorithm is able to overcome the known limitations of the previous techniques. Thus, it can be easily and efficiently applied to various decision making problems with interval data.

scholarly journals Multi-Criteria Decision Making Using TOPSIS Method Under Fuzzy Environment. Application in Spillway Selection

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 637 ◽  
Author(s):  
Vasiliki Balioti ◽  
Christos Tzimopoulos ◽  
Christos Evangelides
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Topsis Method ◽  
Northern Greece ◽  
Analytic Hierarchy ◽  
Order Preference ◽  
Optimum Type ◽  
Topsis Technique ◽  
Selection Of

The selection of an appropriate spillway has a significant effect to the construction of a dam and several procedures and considerations are needed. In the past, this selection of the type of the spillway was arbitrary and sometimes with bad results. Recently the Multiple Criteria Decision Making theory has given the possibility to make a decision about the optimum form of a spillway under complex circumstances. In this paper, the above method is used and especially the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for the selection of a spillway for a dam in the district of Kilkis in Northern Greece—‘Dam Pigi’. As the criteria were fuzzy and uncertain, the Fuzzy TOPSIS method is introduced together with the AHP (Analytic Hierarchy Process), which is used for the evaluation of criteria and weights. Five types of spillways were selected as alternatives and nine criteria. The criteria are expressed as triangular fuzzy numbers in order to formulate the problem. Finally, using the Fuzzy TOPSIS method, the alternatives were ranked and the optimum type of spillway was obtained.

MULTIPLE CRITERIA CONSTRUCTION MANAGEMENT DECISIONS CONSIDERING RELATIONS BETWEEN CRITERIA / DAUGIATIKSLIAI STATYBOS VALDYMO SPRENDIMAI ATSIŽVELGIANT Į RODIKLIŲ TARPUSAVIO PRIKLAUSOMYBĘ

2010 ◽  
Vol 16 (1) ◽  
pp. 109-125 ◽  
Author(s):  
Jurgita Antuchevičienė ◽  
Edmundas Kazimieras Zavadskas ◽  
Algimantas Zakarevičius
Keyword(s):  
Decision Making ◽  
Mahalanobis Distance ◽  
Construction Management ◽  
Distance Measure ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Ideal Solution ◽  
Order Preference ◽  
Topsis Technique

Decision making in construction management has been always complicated especially if there were more than one criterion under consideration. Multiple criteria decision making (MCDM) has been often applied for complex decisions in construction when a lot of criteria were involved. Traditional MCDM methods, however, operate with independent and conflicting criteria. While in every day problems a decision maker often faces interactive and interrelated criteria. Accordingly, the need of improving and supplementing the methodology of compromise decisions arose. It was proposed to supplement TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) method and integrate the Mahalanobis distance in the usual algorythm of TOPSIS. Mahalanobis distance measure offered an option to take the correlations between the criteria into considerations while making the decision. A case study of building redevelopment in Lithuanian rural areas was presented that demonstrated the application of the proposed methodology. The case study proved that the proposed TOPSIS‐M (TOPSIS applying Mahalanobis distance measure) method could have substantial influence in carrying the proper decision. Santrauka Statybos valdymo spendimų priėmimas visuomet yra komplikuotas, ypač jei turime atsižvelgti į daugelį rodiklių. Kompleksiniams statybos sprendimams, kurie apibūdinami daugeliu rodiklių, taikomi daugiatiksliai sprendimų priėmimo metodai (MCDM ‐ Multiple Criteria Decision Making). Šie metodai skirti sprendimams priimti tuomet, kai vertinami konfliktuojantys bei nepriklausomi rodikliai. Tačiau realiose situacijose, priešingai, nuolat susiduriame su saveikaujančiais ir tarpusavio priklausomybę turinčiais rodikliais. Dėl šios priežasties kyla poreikis patobulinti sprendimų metodologiją. Straipsnyje siūloma papildyti variantų racionalumo nustatymo metoda TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution), taikant Mahalanobio metoda atstumams nustatyti. Mahalanobio atstumų nustatymo metodas suteikia galimybę įvertinti koreliacinės rodiklių priklausomybės priimant daugiatikslį sprendimą. Siūlomos metodologijos taikymas įliustruojamas sprendžiant apleistų pastatų Lietuvos kaimo vietovėse racionalaus sutvarkymo uždavinį. Pateiktas pavyzdys patvirtina, kad TOPSIS‐M metodo (t. y. TOPSIS naudojant Mahalanobio atstuma) taikymas gali turėti esminę įtaka priimant sprendimą.

scholarly journals Supplier selection using different metric functions

2015 ◽  
Vol 25 (3) ◽  
pp. 413-423 ◽  
Author(s):  
S.E. Omosigho ◽  
Dickson Omorogbe
Keyword(s):  
Supplier Selection ◽  
Selection Process ◽  
Fuzzy Topsis ◽  
Ideal Solution ◽  
Order Preference ◽  
Metric Functions ◽  
Metric Function ◽  
Topsis Technique ◽  
Intuitionistic Fuzzy Topsis ◽  
Selection Of

Supplier selection is an important component of supply chain management in today?s global competitive environment. Hence, the evaluation and selection of suppliers have received considerable attention in the literature. Many attributes of suppliers, other than cost, are considered in the evaluation and selection process. Therefore, the process of evaluation and selection of suppliers is a multi-criteria decision making process. The methodology adopted to solve the supplier selection problem is intuitionistic fuzzy TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution). Generally, TOPSIS is based on the concept of minimum distance from the positive ideal solution and maximum distance from the negative ideal solution. We examine the deficiencies of using only one metric function in TOPSIS and propose the use of spherical metric function in addition to the commonly used metric functions. For empirical supplier selection problems, more than one metric function should be used.

scholarly journals A Decision-Making Model of Technological-Focused Government Agency Selection of Technological Start-Up Businesses

2022 ◽  
Vol 19 (1) ◽  
pp. 1749
Author(s):  
Amnard Taweesangrungroj ◽  
Roongkiat Rattanabanchuen ◽  
Sukree Sinthupinyo
Keyword(s):  
Decision Making ◽  
Private Equity ◽  
Multiple Criteria Decision Making ◽  
Government Agencies ◽  
Ideal Solution ◽  
Start Up ◽  
Order Preference ◽  
Fuzzy Mcdm ◽  
Decision Making Framework ◽  
The Government

In developing countries, the government has played an important role in supporting startup businesses in various aspects, primarily through tech-focused government agencies. With a limited budget, the government agencies are critical to select plenty of tech startups for funding, leaving only promising tech startups. Consequently, government agencies inevitably face decision-making problems under uncertain circumstances, like private equity investment situations. Reviewing the relevant decision-making frameworks has identified that a classical multiple criteria decision-making (MCDM) approach is currently used, assuming decision-makers acquire complete information that is not realistic. Moreover, both qualitative and quantitative criteria used in evaluating startup businesses cannot represent the uncertainty which is the fundamental nature of the decision-making circumstance. Thus, this article presents a decision-making framework of tech-focused government agencies for selecting startup businesses based on a fuzzy MCDM of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Besides, it identifies selection criteria with mixed research methodologies and determines weights of importance criteria by the Delphi method. Finally, the proposed framework results are fairness, transparency, and eliminating bias in decision-making, including more efficiency when the framework’s ranking orders significantly correspond with actual performances. HIGHLIGHTS Criteria for selecting start-up businesses in technological-focused government agencies A decision-making framework of tech-focused government agencies for selecting startup businesses based on a fuzzy MCDM of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) The performance of the decision-making framework in selecting startup businesses to acquire high potential tech startups to drive the national economy GRAPHICAL ABSTRACT

scholarly journals APLEISTŲ PASTATŲ RACIONALAUS NAUDOJIMO MODELIAVIMAS TAIKANT GEOINFORMACINES SISTEMAS

2012 ◽  
Vol 31 (4) ◽  
pp. 129-133 ◽  
Author(s):  
Jurgita Antuchevičienė
Keyword(s):  
Decision Making ◽  
Ideal Solution ◽  
Order Preference ◽  
Multi Attribute Decision Making ◽  
Topsis Technique

Priimant erdvinius sprendimus geografinių informacinių sistemų (GIS) galimybės taikomos apleistų pastatų racionalaus naudojimo problemoms aplinkos ir visuomenės darnos požiūriu spręsti. Pasiūlytas pradinių duomenų parengimo ir jų taikymo skaičiavimams daugiatiksliais sprendimų priėmimo (Multi-attribute Decision Making – MADM) metodais modelis. Pateikta apleistų pastatų duomenų bazės struktūra ir parengtas šios bazės pildymo duomenimis apie nenaudojamus Lietuvos kaimo statinius pavyzdys. Sudaryti darnią ūkio plėtrą šalyje nusakančių socialinių, ekonominių ir aplinkos rodiklių sluoksniai. Numatytos duomenų analizės galimybės. Pateiktas GIS ir daugiatikslio sprendimų priėmimo metodo TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) integravimo racionaliam apleistų pastatų Lietuvos kaimo vietovėse naudojimui modeliuoti pavyzdys.

scholarly journals Penerapan Metode Topsis Untuk Pemilihan Vendor Terbaik

2021 ◽  
Vol 11 (2) ◽  
pp. 19-30
Author(s):  
Derman Janner Lubis ◽  
Nur Amalina Anindita
Keyword(s):  
Calculation Method ◽  
Research Method ◽  
Ideal Solution ◽  
Topsis Method ◽  
Decision Matrix ◽  
Relative Closeness ◽  
The Matrix ◽  
Order Preference ◽  
Topsis Technique ◽  
Selection Of

The selection of vendors to work on a project is an activity that must be carried out effectively and precisely so that the project is carried out in accordance with business needs and does not suffer losses. To get the best vendor ranking, you can use the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) calculation method. TOPSIS method is a method that generates rankings by calculating the distance between the best solution and the worst solution. The steps to calculate using TOPSIS are identification of alternatives and their values, create a decision matrix, normalize the matrix, calculate the normalization matrix, look for positive and negative solutions, calculate the distance between positive and negative solutions, and calculate relative closeness and sort preferences. In this study using 8 criteria and 5 alternative vendors. Research method using research and development. This method will produce a prototype. The results of the calculation of TOPSIS obtained vendor c who gets the highest score and vendor b with the lowest rank

Supplier Selection using MADM Method Under Uncertainty

2016 ◽  
Vol 5 (3) ◽  
Author(s):  
Mohammad Azadfallah
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Selection Process ◽  
Interval Data ◽  
Entropy Method ◽  
Ideal Solution ◽  
Topsis Method ◽  
Powerful Technique ◽  
Order Preference

In existing literature, there are several studies on supplier selection process, which opine that the suppliers information is usually incomplete and uncertain. Several methods have been proposed for solving this problem, one of which is the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method with interval data. There is no doubt that the TOPSIS with interval data method is a powerful technique in uncertain decision-making context. Despite its usefulness, it is logical that when data are imprecise, weight is imprecise too. To overcome this limit, the extended Shannons Entropy method with interval data is used. The main findings of this study confirm the effectiveness of the hybrid proposed models.

scholarly journals A Multiple Criteria Decision Making Method Based on Relative Value Distances

2015 ◽  
Vol 40 (4) ◽  
pp. 299-315 ◽  
Author(s):  
Huan-jyh Shyur ◽  
Liang Yin ◽  
Hsu-shih Shih ◽  
Chi-bin Cheng
Keyword(s):  
Decision Making ◽  
Solution Method ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Relative Value ◽  
Seeking Behavior ◽  
Order Preference ◽  
Topsis Technique ◽  
The Ideal

Abstract This paper proposes a new multiple criteria decision-making method called ERVD (election based on relative value distances). The s-shape value function is adopted to replace the expected utility function to describe the risk-averse and risk-seeking behavior of decision makers. Comparisons and experiments contrasting with the TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method are carried out to verify the feasibility of using the proposed method to represent the decision makers’ preference in the decision making process. Our experimental results show that the proposed approach is an appropriate and effective MCDM method.

scholarly journals Supplier Selection Very Small Aperture Terminal using AHP-TOPSIS Framework

2020 ◽  
Vol 1 (2) ◽  
pp. 39
Author(s):  
Sumanto Sumanto ◽  
Karlena Indriani ◽  
Lita Sari Marita ◽  
Ade Christian
Keyword(s):  
Decision Making ◽  
Analytical Hierarchy Process ◽  
Supplier Selection ◽  
Assessment Criteria ◽  
Ideal Solution ◽  
Small Aperture ◽  
Order Preference ◽  
Decision Making Model ◽  
Hierarchy Process ◽  
Selection Of

There are several methods of decision making VSAT IT goods suppliers such as: Promethee, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Analytical Hierarchy Process (AHP). Decision-making in the selection of the best suppliers, we have the basis of assessment criteria, and we will also be faced with more than one alternative. If alternatives are only two, maybe still easy for us to choose, but if the alternative is a lot of choice, it is quite difficult for us to decide. Analytical Hierarchy Process (AHP) is a technique that was developed to help overcome this difficulty, because the Analytical Hierarchy Process (AHP) is a form of decision-making model with many criteria. One of the reliability of the Analytical Hierarchy Process (AHP) is able to perform simultaneous analysis and integrated between the parameters of qualitative or quantitative. In this study the authors use six criteria and alternatives 6, the results of these alternatives will be obtained perangkingan alternative used as a reference supplier selection VSAT IT goods company Total EP Indonesie

scholarly journals INTEGRATED APPROACH OF FUZZY MULTI-ATTRIBUTE DECISION MAKING AND DATA MINING FOR CUSTOMER SEGMENTATION

2021 ◽  
Vol 24 (4) ◽  
pp. 174-188
Author(s):  
Manidatta Ray ◽  
Mamata Ray ◽  
Kamalakanta Muduli ◽  
Audrius Banaitis ◽  
Anil Kumar
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Fuzzy Ahp ◽  
Integrated Approach ◽  
Fuzzy Topsis ◽  
Ideal Solution ◽  
Fuzzy C Means ◽  
Order Preference ◽  
Multi Attribute Decision Making ◽  
Topsis Technique

This research work focuses on integrating the multi attribute decision making with data mining in a fuzzy decision environment for customer relationship management. The main objective is to analyse the relation between multi attribute decision making and data mining considering a complex problem of ordering customers segments, which is based on four criteria of customer’s life time value, viz. length (L), recency (R), frequency (F) and monetary value (M). The proposed integrated approach involves fuzzy C-means (FCM) cluster analysis as data mining tool. The experiment conducted using MATLAB 12.0 for identifying eight clusters of customers. The two multi attribute decision making tools i.e., fuzzy AHP (Analytic Hierarchy Process) and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) are used for ranking these identified clusters. The applicability of the integrated decision making technique is also demonstrated in this paper considering the case of Indian retail sector. This research collected responses from nine experts from Indian retail industry regarding their perception of relative importance of four criteria of customer life value and evaluated weights of each criterion using fuzzy AHP. Transaction data of 18 months of the case retail store was analysed to segment 1,600 customers into eight clusters using fuzzy c-means clustering analysis technique. Finally, these eight clusters were ranked using fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The findings of this research could be helpful for firms in identifying the more valuable customers for them and allocate more resources to satisfy them. The findings will be also helpful in developing different loyalty program strategies for customers of different clusters.

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