SISTEM PENDUKUNG KEPUTUSAN SELEKSI PENERIMAAN CALON DOSEN DENGAN METODE TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS)

Tujuan penelitian ini adalah untuk penilaian penerimaan calon dosen. Dosen adalah seorang pendidik di lingkungan perguruan tinggi yang memegang peran utama dalam proses belajar mengajar, dosen sangat menentukan perkembangan dan kemampuan mahasiswa di bidang ilmu pengetahuan dan teknologi. Lembaga pendidikan yang dalam hal ini merupakan induk kerja dari para dosen, sangat berkepentingan dalam menjaga mutu para dosen dalam proses belajar mengajar. Salah satu cara dapat dilakukan dengan menyeleksi calon dosen yang akan direkrut menjadi tenaga pengajar dengan sangat selektif. Hal ini dapat dilakukan dengan cara mengembangkan aplikasi sistem pendukung keputusan seleksi penerimaan calon dosen dengan menggunakan metode technique for order preference by similarity to ideal solution (TOPSIS). Metode ini dipilih karena dapat menentukan rangking dari sejumlah alternatif dengan baik. Penelitian dilakukan untuk mencari rangking setiap alternatif dengan berdasarkan kriteria-kriteria yang telah ditentukan

An Application of TOPSIS Method for Financial Decision Making Process : A Research on Real Estate Investment Trusts Listed in Borsa Istanbul

Managerial decision-making involves a difficult and complex process. Since executives shape the future of the company by making strategic decisions, it is vital that the decisions taken are accurate and efficient in terms of sustainability in the long term. Many managerial decision-making methods have been developed for companies to make the most accurate selection under multiple parameters and alternatives. In this study, the TOPSIS method, which is one of these methods, will examine how investors will choose the most reasonable one for investment from multiple companies

Looking at the SDEWES Index from a Multi-Criterion Decision Analysis Perspective

The SDEWES Index is obtained by aggregating several numerical indicators related to sustainable development. In the context of Multi-Criterion Decision Analysis (MCDA) this index can be seen as the solution to the \u201cranking problematic\u201d for an underlying decisional problem. Accordingly, in this work we look at the SDEWES Index from an MCDA point of view. First, we consider some theoretical aspects, in particular the one usually referred to as \u201crank reversal\u201d. Then we consider some (classic as well as original) visual tools for decision aid, showing how they can be adapted and exploited

Evaluation of Product Quality in QFD using Multi Attribute Decision Making (MADM) Techniques in Manufacturing Industry

Every customer wants to purchase the best quality product but the price factor is only the reason due to which most of customers compromise with quality. The main purpose of our study is to find a best suitable method which will be helpful to design such product having good quality with affordable price. Quality Function Deployment (QFD) is the most powerful method for analyzing the customer demands and selection of most important or valuable voice which has to be corrected or modified. The integrated approach of QFD and Optimization techniques (i.e. AHP, TOPSIS, PROMETHEE, etc.) can be used to analyze the product quality manufacturing industry. A QFD optimization methodology is formulated in this study with suitable illustrations and tried to find a best method of product design. Keywords: Quality Function Deployment, PROMETHEE, AHP, TOPSIS, House of Quality

Industrial Wastes Risk Ranking with TOPSIS, Multi Criteria Decision Making Method

Today, various types of industrial waste are produced in different industries to meet human demands. Growth in quantity as well as complication in quality of these wastes are followed by the advance of technology. Management of such wastes need a proper identification and comprehensive understanding of the risk, emerging after the harmful characteristics of the wastes and negatively affect the human and environment health. Wastes risk ranking systems, in this regard, links between the industrial wastes indices and mathematical method/algorithm, being able at estimation of the risk level as well as comparison between the wastes of an industrial unit based on the risk level. Complexity of the method, high computational costs and lack of proper description of waste using selected indices in former studies has led to the proposal of an applicable and flexible method. In this study, the “TOPSIS Multi-Criteria Decision-Making (MCDM) method” was developed in order for ranking the risk of various industrial wastes. Totally, a number of 9 subsidiary indices on the human health and 11 subsidiary indices on the environment health was identified and employed. Finally, the proposed waste risk ranking system was used for ranking 9 types of identified industrial waste in three industrial section. Results show that the “TOPSIS MCDM”, due to the lack of complexities in method and limited computational costs, is an efficient and appropriate method for ranking industrial wastes

The impact of IT investment on firm performance based on MCDM techniques

In the recent past years, researchers have presented conflicting results regarding the impact of information technology investment on firm performance. Almost all studies on information technology productivity and it role for companies performance are based on data collected and meta-analysis and do not offer a methodology or prototype of analysis in any field This study presents an attempt to adopt a multi-criteria decision making approach to evaluate the non-financial performance of companies using two famous methods. Furthermore, our results try to investigate the effects of information technology investments on firms’ non-financial performance. Finding show that investment in information systems is not necessarily related to achieving a good non-financial performance at the firm level

The Macbeth Approach for Evaluation Offers in Ill–Structure Negotiations Problems

This paper described the main idea of the MACBETH approach and M-MACBETH software to multicriteria negotiation analysis. The MACBETH is based on the additive value model and requires only qualitative judgments about differences of attractiveness to help a decision maker quantify the relative value of options or criteria. The main goal of this procedure is to support interactive learning about evaluation problems and to provide the recommendations to select and rankordering options/criteria in decision making processes. We proposed to use MACBETH methodology as well M-MACBETH software to support ill-structure negotiation problems, i e. evaluation of negotiation offers in an environment with uncertain, subjective and imprecise information and not precisely defined decision makers preferences. An numerical example showing how M-MACBETH software can be implemented in practice, in order to help a negotiator to define numerical values of options/criteria based on verbal statements and next build a scoring system negotiation offers taking into account different types of issues in negotiation problems is presented. More detail we describe the main key points of M-MACBETH software related to structuring the negotiation model, building value scales for evaluation negotiation packages, weighting negotiation issues and selected elements of sensitivity analyzes.This work was supported by the grant from Polish National Science Center DEC 2011/03/B/HS4/03857e-mail: [email protected] of Economics and Management, University of Bialystok5(71)698

Multi-Weight Ranking for Multi-Criteria Decision Making

Cone distribution functions from statistics are turned into Multi-Criteria Decision Making tools. It is demonstrated that this procedure can be considered as an upgrade of the weighted sum scalarization insofar as it absorbs a whole collection of weighted sum scalarizations at once instead of fixing a particular one in advance. As examples show, this type of scalarization--in contrast to a pure weighted sum scalarization-is also able to detect ``non-convex" parts of the Pareto frontier. Situations are characterized in which different types of rank reversal occur, and it is explained why this might even be useful for analyzing the ranking procedure. The ranking functions are then extended to sets providing unary indicators for set preferences which establishes, for the first time, the link between set optimization methods and set-based multi-objective optimization. A potential application in machine learning is outlined.Comment: 20 pages, 12 figure

A fuzzy asymmetric TOPSIS model for optimizing investment in online advertising campaigns

The high penetration of the Internet and e-commerce in Spain during recent years has increased companies' interest in this medium for advertising planning. In this context Google offers a great advertising inventory and perfectly segmented content pages. This work is concerned with the optimization of online advertising investments based on pay-per-click campaigns. Our main goal is to rank and select different alternative keyword sets aimed at maximizing the awareness of and traffic to a company's website. The keyword selection problem with online advertising purposes is clearly a multiple-criteria decision-making problem additionally characterized by the imprecise, ambiguous and uncertain nature of the available data. To address this problem, we propose a technique for order of preference by similarity to ideal solution (TOPSIS)-based approach, which allows us to rank the alternative keyword sets, taking into account the fuzzy nature of the available data. The TOPSIS is based on the concept that the chosen alternative should have the shortest distance from the positive ideal solution and the longest distance from the negative ideal solution. In this work, due to the characteristics of the studied problem, we propose the use of an asymmetric distance, allowing us to work with ideal solutions that differ from the maximum or the minimum. The suitability of the proposed model is illustrated with an empirical case of a stock exchange broker's advertising investment problem aimed at generating awareness about the brand and increasing the traffic to the corporative website

An Experimental Factor Analysis Study Using SAW and TOPSIS to Select and Rank Organic Agriculture Cities in Turkey

The agriculture sector supports Turkey’s GDP portfolio economically and helps establish a sustainable labor force. Turkey has certain competitive advantages in terms of the organic production of agricultural goods like figs and hazelnuts. We conduct a factor analysis using Simple Additive Weighting (SAW) andTechnique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods combined with a 3-level set (export volume, export value, and adequacy rate) to rank 32 candidate cities of Turkey where organic agriculture activities should be given more emphasis to support overall production and export rates. 18 different sets of importance values were used for this purpose and their combinatorial effects on candidate cities were analyzed. The factor analysis results show that the cities Izmir, Aydin, Adiyaman, Gaziantep, Agri, Mus, and Van have the highest potentials among all Turkish cities in bothmethods, while Sanliurfa also shows high potential for organic agriculture in the TOPSIS method