Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

VIKOR Technique:A Systematic Review of the State of the Art Literature on Methodologies and Applications

The main objective of this paper is to present a systematic review of the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method in several application areas such as sustainability and renewable energy. This study reviewed a total of 176 papers, published in 2004 to 2015, from 83 high-ranking journals; most of which were related to Operational Research, Management Sciences, decision making, sustainability and renewable energy and were extracted from the “Web of Science and Scopus” databases. Papers were classified into 15 main application areas. Furthermore, papers were categorized based on the nationalities of authors, dates of publications, techniques and methods, type of studies, the names of the journals and studies purposes. The results of this study indicated that more papers on VIKOR technique were published in 2013 than in any other year. In addition, 13 papers were published about sustainability and renewable energy fields. Furthermore, VIKOR and fuzzy VIKOR methods, had the first rank in use. Additionally, the Journal of Expert Systems with Applications was the most significant journal in this study, with 27 publications on the topic. Finally, Taiwan had the first rank from 22 nationalities which used VIKOR technique

Estimating market share of white goods sector in Turkey with analytic network process

Bu çalışmada, analitik ağ süreci kullanılarak Türkiye’deki beyaz eşya sektöründe yer alan üç büyük firmanın pazar payları tahmin edilmeye çalışılmıştır. Bu firmalar beyaz eşya sektöründe son derece rekabetçi firmalardır. Yeni müşteriler çekmek ve piyasada kendi başlarına tutunmak için, makul fiyatlar belirleyerek, kaliteli ürünler üreterek ve servis ağlarını genişleterek rekabet etmek zorundadırlar. Analitik ağ sürecine uygun olarak ilk önce, pazar payı tahmin problemi yapılandırılmış ve modellenmiştir. Bir sonraki adımda, pazar payını etkileyen faktörlerin önemi belirlenmiş ve Türkiye’deki beyaz eşya firmalarının pazar payları analitik ağ süreci kullanılarak tahmin edilmiştir. Karar modelinin geçerliliği için, tahmin edilen pazar payı değerleri gerçekleşen değerlerle karşılaştırılmıştır.In this paper, it is tried to predict the market shares of the largest three companies in the white goods sector in Turkey through the use of the analytic network process. These companies are highly competitive in the white goods sector. To attract new customers and to retain the current ones, they have to compete by setting reasonable prices, produce high quality products and expand their service networks. In line with the sequence of analytic network process, first of all, an estimation of market share problem has been structured and modeled. Next, it is assessed the importance of the factors affected the market share and it is estimated the market shares of the white goods companies in Turkey using analytic network process. The estimated market share values have been compared with actual ones for the validation of the decision model

Analitik Ağ Süreci Yaklaşımı ile Türkiye’de Beyaz Eşya Sektörünün Pazar Payı Tahmini

In this paper, it is tried to predict the market shares of the largest three companies in the white goods sector in Turkey through the use of the analytic network process. These companies are highly competitive in the white goods sector. To attract new customers and to retain the current ones, they have to compete by setting reasonable prices, produce high quality products and expand their service networks. In line with the sequence of analytic network process, first of all, an estimation of market share problem has been structured and modeled. Next, it is assessed the importance of the factors affected the market share and it is estimated the market shares of the white goods companies in Turkey using analytic network process. The estimated market share values have been compared with actual ones for the validation of the decision model.Bu çalışmada, analitik ağ süreci kullanılarak Türkiye’deki beyaz eşya sektöründe yer alan üç büyük firmanın pazar payları tahmin edilmeye çalışılmıştır. Bu firmalar beyaz eşya sektöründe son derece rekabetçi firmalardır. Yeni müşteriler çekmek ve piyasada kendi başlarına tutunmak için, makul fiyatlar belirleyerek, kaliteli ürünler üreterek ve servis ağlarını genişleterek rekabet etmek zorundadırlar. Analitik ağ sürecine uygun olarak ilk önce, pazar payı tahmin problemi yapılandırılmış ve modellenmiştir. Bir sonraki adımda, pazar payını etkileyen faktörlerin önemi belirlenmiş ve Türkiye’deki beyaz eşya firmalarının pazar payları analitik ağ süreci kullanılarak tahmin edilmiştir. Karar modelinin geçerliliği için, tahmin edilen pazar payı değerleri gerçekleşen değerlerle karşılaştırılmıştır

Analitik Ağ Süreci Yaklaşımı ile Türkiye’de Beyaz Eşya Sektörünün Pazar Payı Tahmini

In this paper, it is tried to predict the market shares of the largest three companies in the white goods sector in Turkey through the use of the analytic network process. These companies are highly competitive in the white goods sector. To attract new customers and to retain the current ones, they have to compete by setting reasonable prices, produce high quality products and expand their service networks. In line with the sequence of analytic network process, first of all, an estimation of market share problem has been structured and modeled. Next, it is assessed the importance of the factors affected the market share and it is estimated the market shares of the white goods companies in Turkey using analytic network process. The estimated market share values have been compared with actual ones for the validation of the decision model.Bu çalışmada, analitik ağ süreci kullanılarak Türkiye’deki beyaz eşya sektöründe yer alan üç büyük firmanın pazar payları tahmin edilmeye çalışılmıştır. Bu firmalar beyaz eşya sektöründe son derece rekabetçi firmalardır. Yeni müşteriler çekmek ve piyasada kendi başlarına tutunmak için, makul fiyatlar belirleyerek, kaliteli ürünler üreterek ve servis ağlarını genişleterek rekabet etmek zorundadırlar. Analitik ağ sürecine uygun olarak ilk önce, pazar payı tahmin problemi yapılandırılmış ve modellenmiştir. Bir sonraki adımda, pazar payını etkileyen faktörlerin önemi belirlenmiş ve Türkiye’deki beyaz eşya firmalarının pazar payları analitik ağ süreci kullanılarak tahmin edilmiştir. Karar modelinin geçerliliği için, tahmin edilen pazar payı değerleri gerçekleşen değerlerle karşılaştırılmıştır

A fuzzy decision-making approach for evaluation and selection of third party reverse logistics provider using fuzzy ARAS

Business environment is full of ups and down and this makes companies to develop different ways of using resources. By expanding life cycle of products, these ways can be cost effective and not harmful for environment. As Reverse Logistics (RL) uses a product after end of its life, it reduces pollution, therefore it has been considered as a part of sustainable development. The core goal of current research is developing a framework by which it evaluates Third Party RL Provider (3rdPRLP) using Multi-Criteria Decision-Making (MCDM) based on Fuzzy Additive Ratio ASsessment (FARAS). Thirty-seven criteria were identified, which are classified into seven main criteria. The main criteria were ranked as follows: product lifecycle position C1, RL process function C2, organizational performance C3, organizational role of RL C4, IT system and communication C5, general company consideration C6, geographical location C7. Market coverage, destination, financial considerations, integrated system, reclaim, efficiency and quality, and growth are each group’s dominant sub-criteria. In addition, the current research helps the logistics managers to better understand the key attributes’ complex relationships in the environment of decision-making. First published online 21 January 202