scholarly journals Implementasi Metode Fuzzy TOPSIS untuk Seleksi Penerimaan Karyawan

2011 ◽  
Vol 5 (2) ◽  
Author(s):  
Sri Lestari ◽  
Widodo Priyodiprodjo
Keyword(s):  
Selection Process ◽  
Employee Selection ◽  
Fuzzy Topsis ◽  
Candidate Selection ◽  
Selection System ◽  
Topsis Method ◽  
Comparison Result ◽  
High Quality ◽  
Hiring Decision ◽  
Order Preference

Abstract —An emerging institution would continue to need qualified workers to produce good performances.  Seeing the importance of high quality employees, the candidate selection process became an important part and should be performed promptly.  It is also important to have candidates with desirable criteria fit to the institution. Many proposed methods can be adapted to help employee selection process based on criteria.  This research propose an employee selection system based on Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, because the proposed method capable to deal with multi dimensional problems in employees selection.  The system will produce ranks that can be used to help the hiring decision. This research also compares the results from TOPSIS method and WPM method.  The comparison result shows that both methods produce the same ranks for the chosen candidates.Keywords—  Fuzzy TOPSIS, WPM, Employee Selection.

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 Fuzzy Topsis in the Assessment of OHS Management System

2019 ◽  
Vol 1 (1) ◽  
pp. 18-25
Author(s):  
Joanna Tabor
Keyword(s):  
Health And Safety ◽  
Fuzzy Topsis ◽  
Point Of View ◽  
Assessment Criteria ◽  
Management Systems ◽  
Manufacturing Companies ◽  
Topsis Method ◽  
Furniture Manufacturing ◽  
Order Preference ◽  
Topsis Technique

AbstractOccupational health and safety (OHS) management is a cycle of decision-making processes, many of which are in fact multi-criterion processes in nature. Therefore, it is important to look for and develop tools to support decision-makers in their actions aimed at improving work safety levels. The objective of this paper is to propose and verify the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method applied to compare and assess the ways OHS management systems function in different companies. The fuzzy TOPSIS method has already been used for a number of years in assessments of alternative solutions in many different areas, but the application that uses ordered fuzzy numbers is quite original in nature. It is especially beneficial to use the fuzzy approach in OHS management systems, as it makes it possible for experts to assess different criteria using most frequently used linguistic variables. The adopted approach was verified in the study of OHS management systems in four furniture manufacturing companies. Assessment criteria were requirements of the PN-N 18001: 2004 Standard. Thanks to the ordered fuzzy TOPSIS method, the analysed OHS management systems were streamlined from the point of view of 24 assessment criteria, and the best and the worst functioning system was identified. The approach presented here may constitute a significant tool for improving OHS management systems.

scholarly journals MULTI-CRITERIA DECISION APPROACH WITH AHP AND IF-TOPSIS METHODS FOR R&D PROJECT SELECTION PROCESS

2019 ◽  
Vol 25 (3) ◽  
pp. 22-32
Author(s):  
EZGİ GÜLER ◽  
SELEN AVCI ◽  
ZERRİN ALADAĞ
Keyword(s):  
Selection Process ◽  
Fuzzy Topsis ◽  
Project Selection ◽  
Decision Makers ◽  
Topsis Method ◽  
Analytic Hierarchy ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Intuitionistic Fuzzy Topsis ◽  
Hierarchy Process

In this study, we examined the project selection process in a mould manufacturing company. We ranked 12 criteria via Analytic Hierarchy Process (AHP) and evaluated the most important 8 criteria. Then we applied Intuitionistic Fuzzy TOPSIS (IF-TOPSIS) method, which is the extended version of the TOPSIS method in intuitionistic fuzzy environment. After expressing the decision makers' evaluations in linguistic terms, we turned them into intuitive fuzzy numbers. In the last step, we obtained the project rankings by calculating the closeness coefficient for 5 projects.

scholarly journals A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 204 ◽  
Author(s):  
Paweł Ziemba ◽  
Aneta Becker ◽  
Jarosław Becker
Keyword(s):  
Fuzzy Numbers ◽  
Fuzzy Topsis ◽  
Topsis Method ◽  
Aggregation Method ◽  
Average Value ◽  
Expert Opinions ◽  
Order Preference ◽  
Consensus Measure ◽  
Individual Expert ◽  
The Ideal

In the case of many complex, real-world decision problems solved with the participation of a group of experts, it is important to capture the uncertainty of opinions and preferences expressed. In such situations, one can use many modifications of the technique for order preference by similarity to the ideal solution (TOPSIS) method, for example, based on fuzzy numbers. In fuzzy TOPSIS, two aggregation methods of fuzzy expert opinions dominate, the first based on the average value technique and the second one extended by the minimum and maximum functions for determining the support of the aggregated fuzzy number. An important disadvantage of both techniques is the fact that the agreement degree of expert opinions is not taken into account. This article proposes the inclusion of the modified procedure for aggregating individual expert opinions, taking into account the degree of agreement of their opinions (called the similarity aggregation method—SAM) and the ranking of experts into the fuzzy TOPSIS method. The fuzzy TOPSIS method extended in this way was used to solve the decision problem of recruiting employees by a group of experts. As part of the solution, the modified SAM was compared with aggregation procedures based on the average value and min-max (minimum and maximum) support. The results of the conducted research indicate that SAM allows fuzzy numbers to be obtained, characterized by less imprecision and greater stability than the other two considered aggregation procedures.

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.

A RISK-BASED DRILL CANDIDATE SELECTION SYSTEM FOR THE U.S. DEPARTMENT OF TRANSPORTATION, OFFICE OF PIPELINE SAFETY1

2001 ◽  
Vol 2001 (2) ◽  
pp. 1147-1151
Author(s):  
John Chang ◽  
James S. Taylor
Keyword(s):  
Crude Oil ◽  
Oil Pollution ◽  
Selection Process ◽  
Candidate Selection ◽  
Professional Judgment ◽  
Selection System ◽  
Department Of Transportation ◽  
Pipeline Safety ◽  
The U.S

ABSTRACT The Office of Pipeline Safety (OPS), U.S. Department of Transportation plans and conducts about 20 government-led tabletop exercises and two area exercises annually under its Oil Pollution Act of 1990 (OPA 90) drill program. Until recently, one of the main objectives in the drill candidate selection process each year was to select a mixture of operators whose pipeline facility spill response plans represented the range of plans required under the agency's OPA 90 regulation. The annual list of drill candidates represented large to small operators transporting crude oil and refined products in various regions of the country. While this approach has worked well, OPS wanted to ensure that the selection process was not inadvertently missing operators whose pipeline facility spills pose the greatest threats to safety and the environment. As a result, OPS developed a quantitative-based process to identify drill candidates. The process uses weighted factors, including input from the Regional Pipeline Safety Offices combined with professional judgment to produce a risk-based approach to help OPS select the operators to drill.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN SELEKSI TENAGA KERJA UNTUK SECURITY SERVICE MENGGUNAKAN METODE ARAS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Tetty Rosmaria Sitompul ◽  
Nelly Astuti Hasibuan
Keyword(s):  
Decision Support ◽  
Process Modeling ◽  
Process Model ◽  
Selection Process ◽  
Employee Selection ◽  
Candidate Selection ◽  
Modeling Data ◽  
Definition Of ◽  
Index Value ◽  
A Company

PT. ISS INDONESIA is a company that already has employees, which is very big. With seven branches spread across Indonesia, PT. ISS INDONESIA has more than 500 workers. Recruitment will continue as the opening of supermarkets or PT. ISS INDONESIA requires additional employees to develop or fill in empty formations. Subjects in this study are the application of decision support systems used to assist employee selection process in accordance with the criteria set by the management PT. ISS INDONESIA. System design with modified waterfall process model includes the definition of requirements required by the user in order to design the system for process modeling, data modeling, and user interface. In this research, it is expected to produce Decision Support Candidate Selection Software System With ARAS Method that can be used to manage employee candidate data and computerized criteria ranging from weighting, calculation of dominance value, arithmetic preference, calculation of index value and ARAS calculation.

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.

scholarly journals GUI MATLAB UNTUK METODE FUZZY SAW DAN FUZZY TOPSIS DALAM PEMILIHAN PENERIMA BEASISWA PPA DENGAN PEMBOBOTAN ENTROPI (Studi Kasus : Pemilihan Penerima Beasiswa PPA tahun 2017 Mahasiswa FSM UNDIP, Semarang)

2018 ◽  
Vol 7 (2) ◽  
pp. 187-199
Author(s):  
Ratna Rahmaniar ◽  
Tatik Widiharih ◽  
Dwi Ispriyanti
Keyword(s):  
Selection Process ◽  
Fuzzy Topsis ◽  
Grade Point ◽  
Scholarship Recipients ◽  
Order Preference ◽  
Calculation Results ◽  
Speed Up ◽  
Simple Additive Weighting ◽  
To Receive

For students, scholarships are important to ease the burden on parents, namely tuition fees.The large number of scholarship applicants is a challenge for FSM to be able to provide an appropriate, effective and efficient decision to manage data on scholarship recipients who are truly entitled to receive scholarships. Prospective scholarship recipients are selected based on the criteria determined by FSM.The criteria determined by the FSM are GPA (Grade Point Average), parent income, number of certificates, number of dependents of parents, semester, and electricity. The method applied to select 170 PPA scholarship recipients (Academic Achievement Improvement) is FSAW (Fuzzy Simple Additive Weighting) and FTOPSIS (Fuzzy Technique for Order Preference by Similarity to Ideal Solution) with entropy weighting. This entropy weighting does                                             a combination of the initial weight that has been determined by FSM and the calculation weight. This research was conducted with the help of MATLAB (Matrix Laboratory)  GUI (Graphical User Interface) as a computing tool. With the MATLAB GUI system built, it can simplify and speed up the selection process. FSAW and FTOPSIS calculation results are 96% the same, while FSAW with FSM is only 39% the same and FTOPSIS with FSM is only 42% the same.The FSAW and FTOPSIS methods are better used than the determination of the FSM, because the results of the FSM are not appropriate.FSM selects manually by looking at files collected by registrants. Keywords:Scholarship, FSAW, FTOPSIS, Entropy, GUI

scholarly journals A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method

2021 ◽  
Vol 13 (6) ◽  
pp. 3020
Author(s):  
Hadi Jaber ◽  
Franck Marle ◽  
Ludovic-Alexandre Vidal ◽  
Ilkan Sarigol ◽  
Lionel Didiez
Keyword(s):  
Research Work ◽  
Fuzzy Topsis ◽  
Warning Signs ◽  
Negative Consequences ◽  
Topsis Method ◽  
Practical Applications ◽  
Score Sheet ◽  
Project Complexity ◽  
Order Preference ◽  
Complexity Assessment

This work aims to help managers anticipate, detect, and keep under control complex situations before facing negative consequences. This article explores complexity modeling theory and develops a framework and associated score sheet to measure project complexity. A framework comprising ninety factors is presented and divided into seven categories: stakeholders, project team, project governance, product, project characteristics, resources, and environment. For the project complexity assessment grid, the project manager prioritizes and weighs its factors using linguistic variables. The score sheet is customizable in its handling of the factors and their weights. A critical state of the art on multi-criteria methodologies is presented, as well as reasons for using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. This method provides early-warning signs with the possibility of comparing multiple projects. It also enables one to measure and prioritize areas and domains where complexity may have the highest impact. Practical applications on three projects within an automotive manufacturer highlight the benefits of such an approach for managers. Project managers could use both a project complexity rating system and a measure of risk criticality to decide on the level of proactive actions needed. This research work differs from traditional approaches that have linked proactive actions to risk criticality but not project complexity.

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