Best Laptop Model Selection by Applying Integrated AHP-TOPSIS Methodology

Best Laptop Model Selection by Applying Integrated AHP-TOPSIS Methodology

Shankha Shubhra Goswami, Dhiren Kumar Behera
DOI: 10.4018/IJPMPA.2021070102
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Abstract

The main objective of this research article is to select the best laptop model among six models available in the market. For this analysis, six laptop models are selected from different online shopping websites having different specifications after seeing the customer ratings. After doing some research, it is found that these laptop models are presently in demand and mostly preferred by the customers. So, an initiative is taken to propose the best laptop among these six models by implementing multiple-criteria decision-making (MCDM) methodology. The selection process is done based on seven criteria (i.e., processor, hard disk capacity, operating system, RAM, screen size, brand, color). For this purpose, analytic hierarchy process (AHP) is adopted for calculating the weightages of the criteria, and TOPSIS is used for selecting the best model. A preference ranking order of the six models is also proposed at the end indicating the best model to the worst. From the whole analysis, it is found that Model 4 came out to be the best model followed by Model 5 and Model 3.
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Literature Review

In recent few years MCDM methodology (Bhole & Deshmukh, 2018; Velasquez & Hester, 2013) have become one of the most useful techniques and gained in popularity for decision making purposes. Many researchers adopted different types of MCDM techniques like AHP (Saaty, 1980, 2001, 2008, 2010; Saaty & Peniwati, 2008), VIKOR (Opricovic, 1990; Opricovic & Tzeng, 2004), PROMETHEE (Brans, Mareschal & Vincke, 1986; Brans and Vincke, 1985), COPRAS (Zavadskas, Kaklauskas, Turkskis & Tamosaitien, 2008), TOPSIS (Hwang et al., 1993; Hwang and Yoon, 1981; Yoon, 1987), ELECTRE (Roy, 1968), ARAS (Zavadskas, Turskis & Vilutiene, 2010; Zavadskas and Turkis, 2010), Fuzzy-MCDM (Abdulsalam, Ighravwe & Babatunde, 2018; Ayhan, 2013; Azizi, Aikhuele & Soulemen, 2015; Buckley, 1985; Buyukozkan and Cifci, 2012; Celik, Deha & Ozok, 2009; Chan and Kumar, 2007; Chang, 1996; Chatterjee and Bose, 2013; Joshi, 2017; Sun, 2010; Zadeh, 1965) etc. in different areas like energy management (Kowalski, Stagl, Madlener & Omann, 2009; Tsoutsos, Drandaki, Frantzeskaki, Iosifidis & Kiosses, 2009), water management (Chung and Lee, 2009; Qin, Huang, Chakma & Nie, 2008), environmental management (Geldermann, Spengler & Rentz, 2000; Marzouk and Abdelakder, 2017; Marzouk and Abdelakder, 2019), waste management (Chandrakar and Limje, 2018), educational field (Bhattacharya and Chakraborty, 2014; Melon, Aragones & Carmen, 2008), industries (Bulut, Duru, Kececi & Yoshida, 2012; Celik et al., 2009; Karande, Zavadskas & Chakraborty, 2016; Kundakci and Isik, 2016; Roistamzadeh and Sofian, 2011; Sri Krishna, Readdy & Vani, 2014; Yang), business and financial management (Korhonen, Koskinen & Voutilainen, 2006; Lee, Chen & Chang, 2008; Secme, Bayrakdaroglu & Kahraman, 2009), transport and logistics (Tzeng, Lin & Opricovic, 2005; Efendigil, Onut & Kongar, 2008; Xiaomin & Yi, 2017; Xiong, Zhao & Lan, 2019), agricultural sectors (Isik and Adali, 2016), medical fields (Buyukozkan and Cifci, 2012), supply chain management (Felice & Petrillo, 2013) etc.

Tzeng et al. (2005) carried out an analysis of alternative-fuel buses for public transportation by using AHP, TOPSIS and VIKOR. Chan and Kumar (2007) used fuzzy extended AHP-based approach for global supplier development by considering the risk factors. Chang et al. (2008) selected unstable slicing machine to control wafer slicing quality by using fuzzy AHP. Duran and Aguilo (2008) adopted fuzzy-AHP approach for the selection of computer-aided machine tool. Huang et al. (2008) applied FAHP in government sponsored R&D project selection. Lee et al. (2008) evaluated the performance of IT department in the manufacturing industry in Taiwan by using fuzzy AHP and BSC approach.

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