scholarly journals SENSOR SELECTION COMPARISON BETWEEN FUZZY TOPSIS ALGORITHM AND SIMPLE ADDITIVE WEIGHTING ALGORITHM IN AUTOMATIC INFUSE MONITORING SYSTEM APPLICATION

SINERGI ◽  
2020 ◽  
Vol 24 (3) ◽  
pp. 207
Author(s):  
Setiyo Budiyanto ◽  
Galang Persada Nurani Hakim ◽  
Ahmad Firdausi ◽  
Fajar Rahayu I. M
Keyword(s):  
Decision Making ◽  
Monitoring System ◽  
System Application ◽  
Low Cost ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Multiple Criteria ◽  
Sensor Selection ◽  
Simple Additive Weighting

One of the critical equipment to support a patient in the hospital would be an infuse system. One of the main problems with the infuse system was manual monitoring. Few researchers try to build a low cost infuse system using a low-cost sensor and microcontroller. This paper proposes a fuzzy Topsis algorithm and Simple Additive Weighting (SAW) algorithm to choose the best sensor for a low cost to the infuse system, which is one of the Multiple Criteria Decision Making (MCDM) problems. Several simulations using three sensors, such as LDR (photoresistor), phototransistor, and photodiode, are performed. By using these two algorithms, it can be shown that the phototransistor emerges as the best sensor with value 1, even though it has the price six times higher from the LDR sensor and three times higher from the photodiode.

Multiple-criteria fuzzy group decision-making with multi-choice goal programming for supplier selection: A case study

2019 ◽  
Vol 11 (03) ◽  
pp. 1950029
Author(s):  
Ashoke Kumar Bera ◽  
Dipak Kumar Jana ◽  
Debamalya Banerjee ◽  
Titas Nandy
Keyword(s):  
Decision Making ◽  
Goal Programming ◽  
Supplier Selection ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Multiple Criteria ◽  
Linguistic Variables ◽  
Manufacturing Organization ◽  
Fuzzy Group Decision Making

In today’s highly turbulent and competitive environment, the success of the organization depends on the performance of its suppliers. However, supplier selection problems are complex as they involve a large number of criteria and, frequently, some of the criteria cannot be evaluated precisely. Moreover, fluctuations of supplier performances and unknown information always exist in real-world decision-making. It is a complex multiple-criteria decision-making (MCDM) problem as it involves a trade-off among various criteria with vagueness and imprecision and also involves a group of experts with diverse opinion. Therefore, to make more practical decisions, this paper is intended to propose an integrated technique for order preference by similarity to ideal solution (TOPSIS) in fuzzy environment with multi-choice goal programming (MCGP) to handle the supplier assessment and order allocation for a battery manufacturing organization. Using linguistic variables, the decision-makers assess the rating of suppliers as well as the importance of various factors. Linguistic variables are expressed in trapezoidal fuzzy numbers (TrFN). Fuzzy-TOPSIS method is proposed to obtain the rank of suppliers and MCGP method is used to allocate suitable orders to the selected suppliers. A case study is implemented to find the applicability and validity of the proposed model. Finally, sensitivity analysis is performed to observe the effect of weights of criteria on supplier evaluation problem.

scholarly journals A  linear programming technique to solve fuzzy multiple criteria decision making problems with an application

2020 ◽  
Author(s):  
S.A. Sadabadi ◽  
A. Hadi-Vencheh ◽  
A. Jamshidi ◽  
M. Jalali
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Linear Models ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Radius Of Gyration ◽  
Programming Technique ◽  
Weighting Method ◽  
Practical Applications ◽  
Simple Additive Weighting

Generally, in real world multiple criteria decision making (MCDM) problems, we concern with inaccurate data. This paper transforms a fuzzy multiple criteria decision making (FMCDM) problem into three linear programming models based on simple additive weighting method (SAW). The new linear models calculate fuzzy performance scores for each alternative. To rank the alternatives, the numerical value of the area between the Radius of Gyration (ROG) and original points of the given fuzzy numbers is used. Finally, we illustrate the practical applications of the proposed method in selection an industrial zone for construct dairy products factory.

A general fuzzy TOPSIS model in multiple criteria decision making

2009 ◽  
Vol 45 (3-4) ◽  
pp. 406-420 ◽  
Author(s):  
Iraj Mahdavi ◽  
Armaghan Heidarzade ◽  
Bahram Sadeghpour-Gildeh ◽  
Nezam Mahdavi-Amiri
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Multiple Criteria ◽  
Topsis Model

scholarly journals Internet Financial Investment Product Selection with Pythagorean Fuzzy DNMA Method

2020 ◽  
Vol 31 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Hongrun Zhang ◽  
Huchang Liao ◽  
Xingli Wu ◽  
Edmundas Kazimieras Zavadskas ◽  
Abdullah Al-Barakati
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Regulatory System ◽  
Fuzzy Topsis ◽  
Multiple Criteria ◽  
Pythagorean Fuzzy Set ◽  
Financial Investment ◽  
Financial Products ◽  
Fuzzy Environment ◽  
Internet Finance

Abstract   The number of products based on internet financial platform has increased dramatically, but due to the lack of effective regulatory system and the information barrier of investors, product returns have been greatly discounted and investment risks have been greatly increased. How to select high-quality products in internet finance based on several indicators is an important multiple criteria decision making problem. In this regard, this study develops a Pythagorean fuzzy double normalization-based multiple aggregation (PF-DNMA) method to solve the problem of selecting internet financial products. Firstly, the key factors for evaluating internet financial products are identified. Observing that the Pythagorean fuzzy set is an effective tool to express evaluation information, we then extend the original multiple criteria decision making method named the double normalization-based multiple aggregation method to Pythagorean fuzzy environment. The PF-DNMA method is characterized by two normalization techniques and three aggregation tools, and thus is effective and robust in solving multiple criteria decision making problems. We deal with an internet financial investment problem by the PL-DNMA method and provide some comparative analyses with the Pythagorean fuzzy TOPSIS and VIKOR methods to illustrate the effectiveness of the proposed method.

Designing a model of fuzzy TOPSIS in multiple criteria decision making

2008 ◽  
Vol 206 (2) ◽  
pp. 607-617 ◽  
Author(s):  
Iraj Mahdavi ◽  
Nezam Mahdavi-Amiri ◽  
Armaghan Heidarzade ◽  
Rahele Nourifar
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Multiple Criteria
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