scholarly journals COMBINING DIFFERENT MCDM METHODS WITH THE COPELAND METHOD: AN INVESTIGATION ON MOTORCYCLE SELECTION

2021 ◽  
Vol 9 (3-4) ◽  
pp. 13-27
Author(s):  
Aşkın Özdağoğlu ◽  
Murat Kemal Keleş ◽  
Anıl Altınata ◽  
Alptekin Ulutaş
Keyword(s):  
Decision Making ◽  
Real Data ◽  
Multi Criteria Decision Making ◽  
Data Set ◽  
Technical Data ◽  
Measurement Units ◽  
Individual Needs ◽  
Copeland Method

There are many different multi-criteria decision making methods in the literature. These methods, which enable criteria with different measurement units to be examined together, allow choosing between alternatives. However, different methods can produce different results depending on the data set. The aim of this study is to combine the results obtained by applying different methods to the data set with the Copeland method. To this end, a problem with real data was first addressed. Technical data of motorcycle alternatives that can be preferred for individual needs were collected in terms of different criteria. The weights of these criteria were found by the PIPRECIA method. Six different multi-criteria decision making methods were used to evaluate motorcycle alternatives. These methods are MOPA, MOOSRA, COPRAS, SAW, WPM and ROV. The sequencing results obtained from these methods were combined with the Copeland method and the results were discussed.

scholarly journals Combining different MCDM methods with the Copeland method: An investigation on motorcycle selection

2021 ◽  
Vol 9 (3-4) ◽  
pp. 13-27
Author(s):  
A§kin Ozdagoglu ◽  
Murat Kele§ ◽  
Anil Altinata ◽  
Alptekin Uluta§
Keyword(s):  
Decision Making ◽  
Real Data ◽  
Multi Criteria Decision Making ◽  
Data Set ◽  
Technical Data ◽  
Measurement Units ◽  
Individual Needs ◽  
Copeland Method

There are many different multi-criteria decision making methods in the literature. These methods, which enable criteria with different measurement units to be examined together, allow choosing between alternatives. However, different methods can produce different results depending on the data set. The aim of this study is to combine the results obtained by applying different methods to the data set with the Copeland method. To this end, a problem with real data was first addressed. Technical data of motorcycle alternatives that can be preferred for individual needs were collected in terms of different criteria. The weights of these criteria were found by the PIPRECIA method. Six different multi-criteria decision making methods were used to evaluate motorcycle alternatives. These methods are MOPA, MOOSRA, COPRAS, SAW, WPM and ROV. The sequencing results obtained from these methods were combined with the Copeland method and the results were discussed.

scholarly journals Redistributed manufacturing system under uncertain evaluation using multi criteria decision making

2021 ◽  
Vol 12 (3) ◽  
pp. 3895-3908
Author(s):  
Ponugupati Narendra Mohan Et.al
Keyword(s):  
Decision Making ◽  
Research Work ◽  
Dynamic Programming Algorithm ◽  
Big Data Analytics ◽  
Global Market ◽  
Programming Algorithm ◽  
Multi Criteria Decision Making ◽  
Firm Level ◽  
Data Set ◽  
Value Analysis

Man In recent day’s occurrence of a global crisis in Environmental (Emission of pollutants) and in Health (Pandemic COVID-19) created a recession in all sectors. The innovations in technology lead to heavy competition in global market forcing to develop new variants especially in the automobile sector. This creates more turbulence in demand at the production of new models, maintenance of existing models that are obsolete while implementation of Bharat Standard automobile regulatory authority BS-VI of India. In this research work developed a novel model of value analysis is integrated by multi-objective function with multi-criteria decision-making analysis by incorporating the big data analytics with green supply chain management to bridge the gap in demand to an Indian manufacturing sector using a firm-level data set using matrix chain multiplication dynamic programming algorithm and the computational results illustrates that the algorithm proposed is effective.

scholarly journals A Decision-Making Algorithm Based on the Average Table and Antitheses Table for Interval-Valued Fuzzy Soft Set

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1131
Author(s):  
Xiuqin Ma ◽  
Yanan Wang ◽  
Hongwu Qin ◽  
Jin Wang
Keyword(s):  
Mathematical Model ◽  
Decision Making ◽  
Real Data ◽  
Soft Set ◽  
Fuzzy Soft Set ◽  
Data Set ◽  
Realistic Situation ◽  
Effective Decision ◽  
Working Out ◽  
Interval Valued

Interval-valued fuzzy soft set is one efficient mathematical model employed to handle the uncertainty of data. At present, there exist two interval-valued fuzzy soft set-based decision-making algorithms. However, the two existing algorithms are not applicable in some cases. Therefore, for the purpose of working out this problem, we propose a new decision-making algorithm, based on the average table and the antitheses table, for this mathematical model. Here, the antitheses table has symmetry between the objects. At the same time, an example is designed to prove the availability of our algorithm. Later, we compare our proposed algorithm with the two existing decision-making algorithms in several cases. The comparison result shows that only our proposed algorithm can make an effective decision in exceptional cases, and the other two methods cannot make decisions. It is therefore obvious that our algorithm has a stronger decision-making ability, thus further demonstrating the feasibility of our algorithm. In addition, a real data set of the homestays in Siming District, Xiamen is provided to further corroborate the practicability of our algorithm in a realistic situation.

Proposing a new hybrid multi-criteria decision-making approach for road maintenance prioritization

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shakiba Sayadinia ◽  
Mohammad Ali Beheshtinia
Keyword(s):  
Decision Making ◽  
New Combination ◽  
Multi Criteria Decision Making ◽  
Important Criterion ◽  
Road Maintenance ◽  
Content Type ◽  
Electre Iii ◽  
Limited Budget ◽  
Copeland Method

PurposeThe purpose of this paper is to present a hybrid multi-criteria decision-making (MCDM) method, by combining the AHP, ELECTRE II, ELECTRE III, ELECTRE IV and Copeland techniques for road maintenance prioritization, in which the roads are evaluated and ranked based on various criteria. The proposed method is applied to four streets in Tehran, as a case study.Design/methodology/approachFirst, a set of criteria for road maintenance was determined and their weights were obtained using the AHP method. Four streets in Tehran, Iran were considered as alternatives and prioritized using the ELECTRE II, ELECTRE III and ELECTRE IV methods. Finally, the results of employing the three methods were integrated using the Copeland method and a final result was obtained.FindingsThe findings of the study suggested that “road safety” is the most important criterion in maintenance and “traffic volume” and “pavement quality index (PCI)” have the second and third rank in importance. Moreover, “The width of the street” is the least important criterion in road maintenance. Additionally, the streets' final ranking was obtained using the proposed method.Research limitations/implicationsThe proposed method helps managers effectively assign their limited budget and resources to roads with higher maintenance priority and as the result, increase the roads efficiency.Originality/valueIn this research, eight main criteria were collected using previous researches and experts' opinions. Also, a new combination of different MCDM techniques is proposed in this research.

Composite Material Selection Using Multi Criteria Decision Making for Automobile Torsion Bar

2014 ◽  
Vol 573 ◽  
pp. 649-654 ◽  
Author(s):  
V.S. Chandrasekar ◽  
K. Raja ◽  
P. Marimuthu
Keyword(s):  
Decision Making ◽  
Composite Material ◽  
Composite Materials ◽  
Material Selection ◽  
Weight Ratio ◽  
Real Data ◽  
Multi Criteria Decision Making ◽  
Preference Relations ◽  
Torsion Bar ◽  
Selection Of

Automotive components made from composite materials can result in significant weight savings over steel and Aluminum. The main purpose of this research is to study about the selection of suitable composite material for automobile torsion bar which possesses good strength to weight ratio and yield considerable weight savings. This paper involves identification of potential composite materials, selection of evaluation criteria, use of fuzzy theory to quantify criteria values under uncertainty and application of fuzzy Linguistics to evaluate and select the best material for replacing conventional steel material with composite material used in automobile torsion bar. The strength of the proposed paper is the ability to deal with uncertainty arising due to the lack of real data in material selection for replacing the conventional material.Keywords:- Composite material, Incomplete linguistic preference relations, AHP, Decision analysis, Consistent fuzzy preference relations, Multi-criteria decision making

scholarly journals Discriminating evidence accumulation from urgency signals in speeded decision making

2015 ◽  
Vol 114 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Guy E. Hawkins ◽  
Eric-Jan Wagenmakers ◽  
Roger Ratcliff ◽  
Scott D. Brown
Keyword(s):  
Decision Making ◽  
Empirical Support ◽  
Real Data ◽  
Positive Outcome ◽  
Future Research ◽  
Recent Theory ◽  
Data Set ◽  
Evidence Accumulation ◽  
Gating Model ◽  
Noisy Information

The dominant theoretical paradigm in explaining decision making throughout both neuroscience and cognitive science is known as “evidence accumulation”—the core idea being that decisions are reached by a gradual accumulation of noisy information. Although this notion has been supported by hundreds of experiments over decades of study, a recent theory proposes that the fundamental assumption of evidence accumulation requires revision. The “urgency gating” model assumes decisions are made without accumulating evidence, using only moment-by-moment information. Under this assumption, the successful history of evidence accumulation models is explained by asserting that the two models are mathematically identical in standard experimental procedures. We demonstrate that this proof of equivalence is incorrect, and that the models are not identical, even when both models are augmented with realistic extra assumptions. We also demonstrate that the two models can be perfectly distinguished in realistic simulated experimental designs, and in two real data sets; the evidence accumulation model provided the best account for one data set, and the urgency gating model for the other. A positive outcome is that the opposing modeling approaches can be fruitfully investigated without wholesale change to the standard experimental paradigms. We conclude that future research must establish whether the urgency gating model enjoys the same empirical support in the standard experimental paradigms that evidence accumulation models have gathered over decades of study.

scholarly journals INNOVATION IN EASTERN EUROPE & CENTRAL ASIA: A MULTI-CRITERIA DECISION-MAKING APPROACH

2019 ◽  
Vol 7 (3) ◽  
pp. 98-121
Author(s):  
Özgür KABADURMUŞ ◽  
Fatma Nur Karaman KABADURMUŞ
Keyword(s):  
Decision Making ◽  
Multi Criteria Decision Making ◽  
Eastern European ◽  
Competitive Advantages ◽  
Firm Level ◽  
Data Set ◽  
Process Innovations ◽  
Central Asian ◽  
Level Data ◽  
Criteria Weights

In today’s intense competition environment, innovation levels of countries determine their competitive advantages. This study compares the innovation levels of Eastern European and Central Asian (EECA) countries using multi-criteria decision-making methods. The firm-level data set of the World Bank on innovation (BEEPS data) is used to evaluate innovation levels and capabilities of the countries in the region. In our proposed TOPSIS based methodology, countries are compared in terms of four different innovation types (New Product, New Organization, New Marketing, and New Process Innovations). Also, we provide an extensive sensitivity analysis to show the changes in the innovation rankings of the countries wıth different criteria weights.

TOPSIS vs. VIKOR

2020 ◽  
pp. 225-250
Author(s):  
Semra Erpolat Taşabat ◽  
Tuğba Kıral Özkan
Keyword(s):  
Decision Making ◽  
Human Development ◽  
Human Development Index ◽  
Decision Makers ◽  
Multi Criteria Decision Making ◽  
Vikor Method ◽  
Data Set ◽  
Development Index ◽  
Multidisciplinary Nature ◽  
Development Levels

Evaluating multiple criteria and selecting and/or ranking alternatives is called Multi Criteria Decision Making (MCDM). These methods which are considered important decision-making tools for decision makers due to their multidisciplinary nature have been developed over the years. As a result, there are many MCDM methods in the literature. In this chapter, TOPSIS and VIKOR, widely used in the literature, will be discussed. The major reason for examining these two methods is that the aggregating function used by both methods is similar because VIKOR method uses linear normalization and TOPSIS method uses vector normalization. The process of the methods is shown on a data set that includes the Human Development Index (HDI) indicators, which have been developed to measure the development levels of countries as well as the unemployment indicator. It was observed that the methods yielded similar results.

scholarly journals An innovative picture fuzzy distance measure and novel multi-attribute decision-making method

2021 ◽  
Author(s):  
Abdul Haseeb Ganie ◽  
Surender Singh
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Distance Measure ◽  
Real Data ◽  
Distance Measures ◽  
Classification Problems ◽  
Data Set ◽  
The Real ◽  
Picture Fuzzy Set ◽  
Multi Attribute Decision Making

AbstractPicture fuzzy set (PFS) is a direct generalization of the fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs). The concept of PFS is suitable to model the situations that involve more answers of the type yes, no, abstain, and refuse. In this study, we introduce a novel picture fuzzy (PF) distance measure on the basis of direct operation on the functions of membership, non-membership, neutrality, refusal, and the upper bound of the function of membership of two PFSs. We contrast the proposed PF distance measure with the existing PF distance measures and discuss the advantages in the pattern classification problems. The application of fuzzy and non-standard fuzzy models in the real data is very challenging as real data is always found in crisp form. Here, we also derive some conversion formulae to apply proposed method in the real data set. Moreover, we introduce a new multi-attribute decision-making (MADM) method using the proposed PF distance measure. In addition, we justify necessity of the newly proposed MADM method using appropriate counterintuitive examples. Finally, we contrast the performance of the proposed MADM method with the classical MADM methods in the PF environment.

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