Selection of an ideal MQL-assisted milling condition: an NSGA-II-coupled TOPSIS approach for improving machinability of Inconel 690

2019 ◽  
Vol 103 (5-8) ◽  
pp. 1811-1829 ◽  
Author(s):  
Binayak Sen ◽  
Syed Abou Iltaf Hussain ◽  
Mozammel Mia ◽  
Uttam Kumar Mandal ◽  
Sankar Prasad Mondal
Keyword(s):  
Nsga Ii ◽  
Inconel 690 ◽  
Milling Condition ◽  
Topsis Approach ◽  
Selection Of

Multi-Criteria Selection of Wind Turbine Using TOPSIS Approach

2020 ◽  
Author(s):  
Shafiqur Rehman ◽  
Salman A. Khan ◽  
Luai M. Alhems
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Selection Process ◽  
Geographical Location ◽  
Real Data ◽  
Maximum Energy ◽  
Decision Criteria ◽  
Topsis Approach ◽  
Selection Of

Abstract The recent revolution in the use of renewable energy worldwide has opened many dimensions of research and development for sustainable energy. In this context, the use of wind energy has received notable attention. One critical decision in the development of a wind farm is the selection of the most appropriate turbine compatible with the characteristics of the geographical location under consideration in order to harness maximum energy. This selection process considers multiple decision criteria which are often in conflict with each other, as improving one criterion negatively affects one or more other criteria. Therefore, it is desired to find a tradeoff solution where all selection criteria are simultaneously optimized to the best possible level. This paper proposes a TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) based approach for multi-criteria selection of wind turbine. Three decision criteria, namely, hub height, wind speed, and net capacity factor are used in the decision process. A case study is shown on real data collected from the Aljouf region located at an altitude of 753 meters above sea level in the northern part of Saudi Arabia. Seventeen turbines with rated capacities ranging from 1.5 GW to 3 GW from various manufacturers are evaluated. Results indicate that Vestas V110 turned out to be the most appropriate turbine for the underlying site.

CHECKING ORTHOGONAL TRANSFORMATIONS AND GENETIC ALGORITHMS FOR SELECTION OF FUZZY RULES BASED ON INTERPRETABILITY-ACCURACY CONCEPTS

2012 ◽  
Vol 20 (supp02) ◽  
pp. 159-186 ◽  
Author(s):  
M. ISABEL REY ◽  
MARTA GALENDE ◽  
M. J. FUENTE ◽  
GREGORIO I. SAINZ-PALMERO
Keyword(s):  
Fuzzy System ◽  
Poor Performance ◽  
Fuzzy Modeling ◽  
Nsga Ii ◽  
Trade Off ◽  
Rule Selection ◽  
Fuzzy Models ◽  
Neuro Fuzzy ◽  
Firing Strength ◽  
Selection Of

Fuzzy modeling is one of the most known and used techniques in different areas to model the behavior of systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high performance from the point of view of accuracy, but from other points of view, such as complexity or interpretability, they can present a poor performance. Several approaches are found in the bibliography to reduce the complexity and improve the interpretability of the fuzzy models. In this paper, a post-processing approach is carried out via rule selection, whose aim is to choose the most relevant rules for working together on the well-known accuracy-interpretability trade-off. The rule relevancy is based on Orthogonal Transformations, such as the SVD-QR rank revealing approach, the P-QR and OLS transformations. Rule selection is carried out using a genetic algorithm that takes into account the information obtained by the Orthogonal Transformations. The main objective is to check the true significance, drawbacks and advantages of the rule selection based on the orthogonal transformations via the rule firing strength matrix. In order to carry out this aim, a neuro-fuzzy system, FasArt (Fuzzy Adaptive System ART based), and several case studies, data sets from the KEEL Project Repository, are used to tune and check this selection of rules based on orthogonal transformations, genetic selection and accuracy-interpretability trade-off. This neuro-fuzzy system generates Mamdani fuzzy rule based systems (FRBSs), in an approximative way. NSGA-II is the MOEA tool used to tune the proposed rule selection.

scholarly journals Efficient Selection of QoS Based Web Services using Modified TOPSIS Method

2019 ◽  
Vol 8 (2) ◽  
pp. 5732-5738
Keyword(s):  
Web Services ◽  
Web Service ◽  
Internet Technology ◽  
Service Oriented ◽  
User Expectations ◽  
Modified Topsis ◽  
Topsis Approach ◽  
The Web ◽  
Selection Of ◽  
Oriented System

Nowadays, due to the ease of availability of internet technology large numbers of people are using the World Wide Web. The companies are changing their way to do business. They are shifting from a data-oriented system to a service-oriented system. Now companies are able to depict their business in the form of web services and make them available on the internet. Due to this number of web services are available for satisfying the user’s need. But to select the best web service that satisfies user specification is a challenging issue. So, it is necessary to consider not only the functional requirement of the web services but also the nonfunctional requirements of the web services. On the other hand, users are not able to specify the exact nonfunctional parameter requirements so, there is a need for QoS processor which can understand the user's need and can extract the parameters for QoS. In this paper, a modified TOPSIS approach based on MCDM is proposed for the selection of efficient web service. The web services that are near to user expectations are selected out using the proposed method. Experimental outcomes show that the proposed approach determines the most promising results.

Enhancement of TOPSIS for Evaluating the Web-Sources to Select as External Source for Web-Warehousing

2018 ◽  
Vol 5 (1) ◽  
pp. 117-130
Author(s):  
Hariom Sharan Sinha
Keyword(s):  
Euclidean Distance ◽  
External Source ◽  
Main Concern ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Multiple Features ◽  
Order Preference ◽  
Topsis Approach ◽  
The Web ◽  
Selection Of

In this paper, the main concern is to evaluate the web-sources, which are to be selected as an external source for web-warehousing. In order to identify the web sources, they are evaluated on the basis of their multiple features. For it, Multi-Criteria Decision Making (MCDM) approach is used. In this paper, among all the MCDM approach, the focus is on “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) approach and proposing an enhancement in this method. The traditional TOPSIS approach uses Euclidean Distance to measure the similarity. Here, Jeffrey Divergence has been proposed instead of Euclidean Distance to compute the similarity measure which includes asymmetric and symmetric distances during computation. Experimental analysis of both the variations of TOPSIS approach have been conducted and the result shows the enhancement in the selection of web sources.

Service supplier selection under fuzzy and stochastic uncertain environments

2021 ◽  
pp. 1-15
Author(s):  
Meng Liu ◽  
Xiaolin Wang ◽  
Yupeng Li
Keyword(s):  
Supplier Selection ◽  
Uncertain Environments ◽  
Selection Problems ◽  
Order Preference ◽  
Effectiveness And Efficiency ◽  
Topsis Approach ◽  
Interval Valued ◽  
Selection Of ◽  
The Ideal

Owing to the heterogeneity and inherent uncertainty of services, the selection of service suppliers is a complicated multi-attribute group decision-making (MAGDM) problem in which fuzzy criteria and stochastic criteria coexist. During the past few decades, many real-world supplier selection problems have been resolved using MAGDM methods. Nevertheless, extant research on supplier selection considers either fuzzy criteria or stochastic criteria, and hence most of these methods cannot address the complex and unstructured nature of contemporary service supplier selection problems. In this study, a novel technique for order preference by similarity to the ideal solution (TOPSIS) approach, integrating both fuzzy criteria and stochastic criteria, is developed; in this approach, the interval-valued intuitionistic fuzzy (IVIF) cross-entropy for fuzzy criteria and the Euclidean distance for stochastic criteria are used to acquire the rankings of alternatives. Moreover, a sensitivity analysis is conducted for a case study of hoisting service supplier selection, and a comparative analysis with other existing methods is performed to confirm the effectiveness and efficiency of the proposed approach.

A Non-Dominant Sorting Genetic Algorithm for Optimization of a Product Design and Selection of Its Suppliers

2020 ◽  
Vol 19 (01) ◽  
pp. 167-188
Author(s):  
Oulfa Labbi ◽  
Abdeslam Ahmadi ◽  
Latifa Ouzizi ◽  
Mohammed Douimi
Keyword(s):  
Genetic Algorithm ◽  
Product Design ◽  
Computational Time ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Solution Quality ◽  
Holding Costs ◽  
Weighted Sum Method ◽  
The Impact ◽  
Selection Of

The aim of this paper is to address the problem of supplier selection in a context of an integrated product design. Indeed, the product specificities and the suppliers’ constraints are both integrated into product design phase. We consider the case of improving the design of an existing product and study the selection of its suppliers adopting a bi-objective optimization approach. Considering multi-products, multi-suppliers and multi-periods, the mathematical model proposed aims to minimize supplying, transport and holding costs of product components as well as quality rejected items. To solve the bi-objective problem, an evolutionary algorithm namely, non-dominant sorting genetic algorithm (NSGA-II) is employed. The algorithm provides a set of Pareto front solutions optimizing the two objective functions at once. Since parameters values of genetic algorithms have a significant impact on their efficiency, we have proposed to study the impact of each parameter on the fitness functions in order to determine the optimal combination of these parameters. Thus, a number of simulations evaluating the effects of crossover rate, mutation rate and number of generations on Pareto fronts are presented. To evaluate performance of the algorithm, results are compared to those obtained by the weighted sum method through a numerical experiment. According to the computational results, the non-dominant sorting genetic algorithm outperforms the CPLEX MIP solver in both solution quality and computational time.

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