scholarly journals APPLICATION OF FUZZY MULTIPLE CRITERIA DECISION MAKING (MCDM) IN SELECTION OF PROSPECTIVE EMPLOYEES

JOURNAL ASRO ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 10
Author(s):  
Sutrisno Sutrisno ◽  
Sutikno Wahyu Hidayat ◽  
Avando Bastari ◽  
Okol Sri Suharyo
Keyword(s):  
Decision Making ◽  
Selection Process ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Recruitment Process ◽  
Weighting Method ◽  
Test Application ◽  
Selection Test ◽  
Selection Factors ◽  
A Company

The recruitment process is the initial process that determines the sustainability and success of a company. In the process, effective and efficient selection tests are the key. The level of professionalism and academic ability of prospective employees are two things that are very much needed as a reference and criteria that are used as selection factors in the recruitment process. This study uses the Fuzzy Multiple Criteria Decision Making (MCDM) method by solving problems using the Simple Addictive Weighting Method (SAW). The use of this method is expected to produce an electronic selection test application that can help the recruitment team in carrying out the selection process at PT. X. The results of the research are in the form of prospective employee selection test applications to simplify the process of selecting prospective employees according to their needs.  Keywords: Selection Test, Application, FMCDM, SAW

A New Optimization Method for Supplier Evaluation in the Context of Volume Discount

2011 ◽  
pp. 64-75
Author(s):  
Reza Farzipoor Saen
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Selection Process ◽  
Multiple Criteria Decision Making ◽  
Optimization Method ◽  
Multiple Criteria ◽  
Supplier Evaluation ◽  
Imprecise Data ◽  
Common Criteria ◽  
Decision Making Problem

Supplier selection is a multiple criteria decision making problem that the selection process mainly involves evaluating a number of suppliers according to a set of common criteria for selecting suppliers to meet business needs. Suppliers usually offer volume discounts to encourage the buyers to order more. To select suppliers in the presence of both volume discounts and imprecise data, this chapter proposes an optimization method. A numerical example demonstrates the application of the proposed method.

scholarly journals Optimization of a Multiple Injection System in a Marine Diesel Engine through a Multiple-Criteria Decision-Making Approach

2020 ◽  
Vol 8 (11) ◽  
pp. 946 ◽  
Author(s):  
Maria Isabel Lamas ◽  
Laura Castro-Santos ◽  
Carlos G. Rodriguez
Keyword(s):  
Decision Making ◽  
Standard Deviation ◽  
Diesel Engine ◽  
Multiple Criteria Decision Making ◽  
Entropy Method ◽  
Multiple Criteria ◽  
Injection System ◽  
Marine Diesel Engine ◽  
Weighting Method ◽  
Marine Diesel

In this work, a numerical model was developed to analyze the performance and emissions of a marine diesel engine, the Wärtsilä 6L 46. This model was validated using experimental measurements and was employed to analyze several pre-injection parameters such as pre-injection rate, duration, and starting instant. The modification of these parameters may lead to opposite effects on consumption and/or emissions of nitrogen oxides (NOx), carbon monoxide (CO), and hydrocarbons (HC). According to this, the main goal of the present work is to employ a multiple-criteria decision-making (MCDM) approach to characterize the most appropriate injection pattern. Since determining the criteria weights significantly influences the overall result of a MCDM problem, a subjective weighting method was compared with four objective weighting methods: entropy, CRITIC (CRiteria Importance Through Intercriteria Correlation), variance, and standard deviation. The results showed the importance of subjectivism over objectivism in MCDM analyses. The CRITIC, variance, and standard deviation methods assigned more importance to NOx emissions and provided similar results. Nevertheless, the entropy method assigned more importance to consumption and provided a different injection pattern.

scholarly journals A  linear programming technique to solve fuzzy multiple criteria decision making problems with an application

2020 ◽  
Author(s):  
S.A. Sadabadi ◽  
A. Hadi-Vencheh ◽  
A. Jamshidi ◽  
M. Jalali
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Linear Models ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Radius Of Gyration ◽  
Programming Technique ◽  
Weighting Method ◽  
Practical Applications ◽  
Simple Additive Weighting

Generally, in real world multiple criteria decision making (MCDM) problems, we concern with inaccurate data. This paper transforms a fuzzy multiple criteria decision making (FMCDM) problem into three linear programming models based on simple additive weighting method (SAW). The new linear models calculate fuzzy performance scores for each alternative. To rank the alternatives, the numerical value of the area between the Radius of Gyration (ROG) and original points of the given fuzzy numbers is used. Finally, we illustrate the practical applications of the proposed method in selection an industrial zone for construct dairy products factory.

An Analysis for Selecting Best Smartphone Model by AHP-TOPSIS Decision-Making Methodology

2021 ◽  
Vol 12 (3) ◽  
pp. 116-137
Author(s):  
Shankha Shubhra Goswami ◽  
Dhiren Kumar Behera
Keyword(s):  
Decision Making ◽  
Selection Process ◽  
Real Life ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Preference Ranking ◽  
Price Range ◽  
Internal Storage ◽  
Ranking Order

This article presents the detailed study of integrated AHP-TOPSIS multiple-criteria decision-making (MCDM) methodology. For these purposes, a real-life example is taken where the best smartphone mobile model is proposed among 10 different available models by implementing integrated AHP-TOPSIS methodology. The 10 mobile models selected for this analysis are presently available in the market and are from different brands having different specifications and price range. The selection process is done based on four major criteria (i.e., price, internal storage, RAM, and brand). AHP is applied for the criteria weightage's calculation, whereas TOPSIS is adopted for selecting the best alternative and make a preference ranking order indicating the best model to the worst. The final result shows that Samsung J7 is the best smartphone model followed by Redmi 7A, and Redmi K20 pro occupies the last position; thus, it is the worst model among the group.

scholarly journals Multiobjective optimization and decision making in engineering sciences

2021 ◽  
Author(s):  
Jussi Hakanen ◽  
Richard Allmendinger
Keyword(s):  
Decision Making ◽  
Multiobjective Optimization ◽  
Data Science ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Special Issue ◽  
Environmental Aspects ◽  
Effective Combination ◽  
Engineering Sciences ◽  
A Company

AbstractReal-world decision making problems in various fields including engineering sciences are becoming ever more challenging to address. The consideration of various competing criteria related to, for example, business, technical, workforce, safety and environmental aspects increases the complexity of decision making and leads to problems that feature multiple competing criteria. A key challenge in such problems is the identification of the most preferred trade-off solution(s) with respect to the competing criteria. Therefore, the effective combination of data, skills, and advanced engineering and management technologies is becoming a key asset to a company urging the need to rethink how to tackle modern decision making problems. This special issue focuses on the intersection between engineering, multiple criteria decision making, multiobjective optimization, and data science. The development of new models and algorithmic methods to solve such problems is in the focus as much as the application of these concepts to real problems. This special issue was motivated by the 25th International Conference on Multiple Criteria Decision Making (MCDM2019) held in Istanbul, Turkey, in 2019.

scholarly journals INTEGRATED FUZZY MULTIPLE CRITERIA DECISION MAKING MODEL FOR ARCHITECT SELECTION / INTEGRUOTAS NERAIŠKUSIS DAUGIATIKSLIS SPRENDIMŲ PRIĖMIMO MODELIS ARCHITEKTUI ATRINKTI

2012 ◽  
Vol 17 (4) ◽  
pp. 645-666 ◽  
Author(s):  
Violeta Keršulienė ◽  
Zenonas Turskis
Keyword(s):  
Decision Making ◽  
Personnel Selection ◽  
Selection Process ◽  
Multiple Criteria Decision Making ◽  
Computational Procedure ◽  
Multiple Criteria ◽  
Ratio Analysis ◽  
Decision Making Problem ◽  
Linguistic Term ◽  
Decision Making Model

The philosophy of decision making in economics is to assess and select the most preferable solution, implement it and to gain the biggest profit. Important issues such as competitive market, changing technical, political and social environment have a key role in personnel selection. It is the crucial task which determines the company's present and future. Many decisions made cannot be accurately forecast or assessed. Understanding of the multiple criteria method and knowledge to calculate the algorithm of the method allows a decision maker to trust solutions offered by solution support systems to a greater extent. Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, additive ratio assessment (ARAS) method with fuzzy numbers (ARAS-F) and step-wise weight assessment ratio analysis (SWARA) technique are integrated. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with a group of information sources. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set. The computational procedure of the proposed framework is illustrated through an architect's selection problem. Santrauka Sprendimų priėmimas ekonomikoje pagrįstas galimų sprendinių įvertinimu, tinkamiausio sprendinio atrinkimu, įgyvendinimu ir didžiausio pelno gavimu. Tokie svarbūs klausimai, kaip užsitikrinti vietą konkurencingoje rinkoje, besikeičianti techninė, politinė ir socialinė aplinka, yra vieni svarbiausių parenkant personalą. Tai labai svarbus uždavinys, tiesiogiai veikiantis bendrovės gyvavimą dabar ir ateityje. Daug sprendinių negali būti tiksliai prognozuojami arba įvertinti. Supratimas apie daugiatikslius metodus ir skaičiavimo metodo algoritmo išmanymas yra prielaidos sprendimų priėmėjui pasitikėti sprendiniais, kuriuos pateikia sprendimų priėmimo sistemos. Yra pateikiama daug atskirų rodiklių personalui atrinkti: organizaciniai gebėjimai, kūrybiškumas, asmeninės ir lyderio savybės. Visi šie rodikliai turi vieną bendrą savybę – jie negali būti tiksliai apirėžti. Tokiems uždaviniams spręsti neraiškiųjų aibių teorija gali pateikti sprendimo būdus, kurie įvertina netikslumus, būdingus personalo atrankos procesui. Šiame straipsnyje neraiškusis daugiatikslis sprendimų priėmimo (MCDM) algoritmas, taikant neraiškiosios informacijos sintezės principus, suminį santykinių dydžių vertinimo (ARAS) metodą, kurio reikšmės aprašomos neraiškiaisiais skaičiais (ARAS-F), ir laipsnišką rodiklių svorio santykinių dydžių analizės (SWARA) metodą, yra integruotas. Siūlomas metodas tinkamas informacijai, vertinamai tiek žodžiais, tiek skaitmenimis, išreiškiamoms skalėms, uždaviniui, kurio informacija surenkama iš grupės informacijos šaltinių, apdoroti. Sujungimo procesas grindžiamas informacija, taikant neraiškiųjų aibių teoriją pagrindinėms žodžiais aprašomoms reikšmėms pakeisti. Siūlomo algoritmo taikymas pavaizduotas sprendžiant architekto parinkimo uždavinį.

scholarly journals A New Grey Approach for Using SWARA and PIPRECIA Methods in a Group Decision-Making Environment

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1554
Author(s):  
Dragiša Stanujkić ◽  
Darjan Karabašević ◽  
Gabrijela Popović ◽  
Predrag S. Stanimirović ◽  
Muzafer Saračević ◽  
...  
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Multiple Criteria Decision Making ◽  
Grey System Theory ◽  
Multiple Criteria ◽  
Decision Making Process ◽  
Grey System ◽  
Criteria Weights

The environment in which the decision-making process takes place is often characterized by uncertainty and vagueness and, because of that, sometimes it is very hard to express the criteria weights with crisp numbers. Therefore, the application of the Grey System Theory, i.e., grey numbers, in this case, is very convenient when it comes to determination of the criteria weights with partially known information. Besides, the criteria weights have a significant role in the multiple criteria decision-making process. Many ordinary multiple criteria decision-making methods are adapted for using grey numbers, and this is the case in this article as well. A new grey extension of the certain multiple criteria decision-making methods for the determination of the criteria weights is proposed. Therefore, the article aims to propose a new extension of the Step-wise Weight Assessment Ratio Analysis (SWARA) and PIvot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods adapted for group decision-making. In the proposed approach, attitudes of decision-makers are transformed into grey group attitudes, which allows taking advantage of the benefit that grey numbers provide over crisp numbers. The main advantage of the proposed approach in relation to the use of crisp numbers is the ability to conduct different analyses, i.e., considering different scenarios, such as pessimistic, optimistic, and so on. By varying the value of the whitening coefficient, different weights of the criteria can be obtained, and it should be emphasized that this approach gives the same weights as in the case of crisp numbers when the whitening coefficient has a value of 0.5. In addition, in this approach, the grey number was formed based on the median value of collected responses because it better maintains the deviation from the normal distribution of the collected responses. The application of the proposed approach was considered through two numerical illustrations, based on which appropriate conclusions were drawn.

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