Application of Simple Average Weighting Optimization Method in the Selection of Best Desktop Computer Model

Authors

  • Soupaya Mitra Department of Mechanical Engineering, Jalpaiguri Government Engineering College
  • Shankha Shubhra Goswami Department of Mechanical Engineering, Jalpaiguri Government Engineering College https://orcid.org/0000-0002-0033-3089

DOI:

https://doi.org/10.21467/ajgr.6.1.60-68

Abstract

Multi-Criteria Decision Making (MCDM) is one of the most emerging concepts in today’s world which enables a decision maker to select the best strategies among different available alternatives. MCDM technique helps to remove the biasness and confusion while selecting a product or process. In recent few years different MCDM methodologies finds wide area of applications in industries as well as in our daily life. In this paper, such one type of application is broadly described. One example is taken from our daily life, which is generally faced by most of the students while purchasing a desktop computer. The main objective of this paper is to select the best desktop computer models among five different models actual available in the market having different configurations. For this analysis, 100 computer users have been surveyed to know their relative preferences and choices, which of the computer specifications is most important to them. For this present analysis few numbers of criteria have been considered and also there are number of sub-criteria within each criterion (for example, the processor may be different for different models like I3, I5, I7 etc.). The MCDM methodology which is adopted for this selection process is known as Simple Average Weighting (SAW) method.

Keywords:

Multi-Criteria Decision Making, Simple Average Weighting, Desktop Computers, Criteria, Sub-Criteria

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References

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Published

2019-07-22

Issue

Section

Graduate Survey Articles

How to Cite

[1]
S. Mitra and S. S. Goswami, “Application of Simple Average Weighting Optimization Method in the Selection of Best Desktop Computer Model”, Adv. J. Grad. Res., vol. 6, no. 1, pp. 60–68, Jul. 2019.