Solving Material Handling Equipment Selection Problems in an Industry with the Help of Entropy Integrated COPRAS and ARAS MCDM techniques

Goswami, Shankha Shubhra; Behera, Dhiren Kumar

doi:10.1007/s41660-021-00192-5

Cited by 62 publications

(30 citation statements)

References 68 publications

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“…In the proposed method, firstly, a comprehensive weighting approach was used, combining subjective weights were obtained using the Fuzzy Decision Making Trail and Evaluating Laboratory (Fuzzy DEMATEL-FDEMATEL) and the objective weights were computed using Entropy; secondly, the "Later Defuzzification Vise Kriterijumsa Optimizacija I Kompromisno Resenje" method was applied to rank the alternatives [23] . Goswami et al [24] aimed to investigate the adequacy and applicability of two MCDM methods, namely Additive Ratio Assessment (ARAS) and Complex Proportional Assessment (COPRAS). In the study, three real-time material handling equipment selection problems (conveyor selection, automatic guided vehicle selection and robot selection) are discussed.…”

Section: Literature Review On Entropy Methodsmentioning

confidence: 99%

“…In these three MCDM problems, the alternatives were ranked using ARAS and COPRAS, and the objective criteria weights were obtained by the Entropy method. The resulting rankings were evaluated using six different MCDM methods and validated by comparison with the findings of previous researchers [24] . Salehi et al [25] aimed to evaluate the crisis management systems of five petrochemical plants from three aspects: organizational aspects, human aspects and technical aspects.…”

Section: Literature Review On Entropy Methodsmentioning

confidence: 99%

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Determining the Riskiest Production Area Using Integrated Criteria Weights

2022

J Mod Ind Manuf

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Objective: This study aims to prioritize seven production areas in an automotive glass manufacturing firm considering their risk levels by using integrated multi-criteria decision-making approach. Methods:The proposed approach includes two phases as determining risk criteria weights and ranking production areas according to their risk levels. In determining risk criteria weights, Criteria Importance through Inter-criteria Correlation (CRITIC), Entropy and Preference Selection Index (PSI) methods are used. Then, the different criteria weights obtained from these three approaches are aggregated with geometric mean operator. In ranking production areas, Multi-objective Optimization on the Basis of Simple Ratio Analysis is utilized and the production area which has the lowest risk level is determined considering the aggregated criteria weights obtained in the first phase. Results:The least risky and the riskiest areas were obtained as autoclave and tunnel kiln areas respectively for four different weighting approaches. CRITIC, PSI and Aggregated Weighting methods gave the same rankings of production areas. There are small differences between the rankings of Entropy and Aggregated Weighting Method for the third and sixth production areas. Conclusion:The data used in the analysis is obtained by real measurements. For this reason, the results obtained from the study are more accurate compared to the subjective weighting methods. The proposed approach provides authorities to see the results of the different approaches and aggregated rankings. Thus, authorities can decide for which production area measures should be taken first in a more flexible way.

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Section: Literature Review On Entropy Methodsmentioning

confidence: 99%

Section: Literature Review On Entropy Methodsmentioning

confidence: 99%

Determining the Riskiest Production Area Using Integrated Criteria Weights

2022

J Mod Ind Manuf

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“…Many models use methods such as TOPSIS, Fuzzy DEMATEL, COPRAS, ARAS combined with Entropy weights. For example, Goswami 53 integrates Entropy with Additive Ratio Assessment (ARAS) and Complex Proportional Assessment (COPRAS) MCDM techniques that solve problems in selecting of material handling equipment. On the other hand, HaiLi's study proposes a machine tool selection method based on a new hybrid MCDM model.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing higher education performance by entropy - TOPSIS method: A case study in Viet Nam private universities

Wang

Nguyễn

Phan

2022

Measurement and Control

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The development in both quantity and quality of the system of private universities over the past time has created great opportunities for people to study and improve their qualifications and working skills. It is also challenging for educational managers to find the right university development strategy in today's competitive environment. Therefore, evaluating the effectiveness of using private universities resource to analyze training performance is considered one of the issues. In this study, the Entropy method is used to determine the weight of the criteria. Then the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to determine the ranking order of the private universities. Next, Spearman's rank correlation coefficient was applied to evaluate the correlation between rank-ordered variables over the 2 years of analysis selection. Finally, ANOVA was used to compare the criteria between groups of universities. The combination of methods contributes to creating an objective environment in assessing the performance of each university. Thereby, universities can see the important criteria to pay attention to, creating a premise for universities to establish strategies for development and competition. At the same time, educational leaders have the right perspective in choosing key universities to invest in finance and facilities.

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“…Fuzzy COPRAS was used for multi-criteria selection of optimum material to improve braking system in the automotive industry, to ensure maximum safety and failure-free performance [ 31 ]. The authors of [ 32 ] presented the application of the COPRAS method in combination with the objective entropy technique to determine criteria weights in the problem of selecting conveying equipment in the industry sector. The references presented above prove the usefulness of the COPRAS method in multi-criteria problems in various domains, including those in which uncertainty occurs and in engineering problems in which the appropriate decision has an impact on safety and a lack of emergency performance.…”

Section: Literature Reviewmentioning

confidence: 99%

New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis

Gandotra

Kizielewicz

Anand

et al. 2021

Entropy

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The purpose of this paper is to propose a new Pythagorean fuzzy entropy for Pythagorean fuzzy sets, which is a continuation of the Pythagorean fuzzy entropy of intuitionistic sets. The Pythagorean fuzzy set continues the intuitionistic fuzzy set with the additional advantage that it is well equipped to overcome its imperfections. Its entropy determines the quantity of information in the Pythagorean fuzzy set. Thus, the proposed entropy provides a new flexible tool that is particularly useful in complex multi-criteria problems where uncertain data and inaccurate information are considered. The performance of the introduced method is illustrated in a real-life case study, including a multi-criteria company selection problem. In this example, we provide a numerical illustration to distinguish the entropy measure proposed from some existing entropies used for Pythagorean fuzzy sets and intuitionistic fuzzy sets. Statistical illustrations show that the proposed entropy measures are reliable for demonstrating the degree of fuzziness of both Pythagorean fuzzy set (PFS) and intuitionistic fuzzy sets (IFS). In addition, a multi-criteria decision-making method complex proportional assessment (COPRAS) was also proposed with weights calculated based on the proposed new entropy measure. Finally, to validate the reliability of the results obtained using the proposed entropy, a comparative analysis was performed with a set of carefully selected reference methods containing other generally used entropy measurement methods. The illustrated numerical example proves that the calculation results of the proposed new method are similar to those of several other up-to-date methods.

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Solving Material Handling Equipment Selection Problems in an Industry with the Help of Entropy Integrated COPRAS and ARAS MCDM techniques

Cited by 62 publications

References 68 publications

Determining the Riskiest Production Area Using Integrated Criteria Weights

Determining the Riskiest Production Area Using Integrated Criteria Weights

Analyzing higher education performance by entropy - TOPSIS method: A case study in Viet Nam private universities

New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis

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