Manoj Mathew

Bio: Manoj Mathew is an academic researcher from Shri Shankaracharya Institute of Professional Management and Technology. The author has contributed to research in topics: Multiple-criteria decision analysis & TOPSIS. The author has an hindex of 7, co-authored 15 publications receiving 164 citations.

Comparison of new multi-criteria decision making methods for material handling equipment selection

Abstract: Article history: Received: November 26, 2017 Received in revised format: November 26, 2017 Accepted: January 28, 2018 Available online: January 30, 2018 Conveyors and automated guided vehicles (AGVs) are important mechanical handling equipment used to transport loads from one place to another. Selection of proper material handling equipment is an intricate process. In the present paper two material handling equipment selection problem was solved using various newly developed multi criteria decision making (MCDM) methods. First a conveyor selection problem with six conflicting criteria and four alternatives was solved using four MCDM methods, i.e. combinative distance based assessment (CODAS) method, evaluation based on distance from average solution (EDAS) method, weighted aggregated sum product assessment (WASPAS) method and multi-objective optimization on the basis of ratio analysis (MOORA) method. Second an automated guided vehicles selection problem with six conflicting criteria and eight alternatives was solved using CODAS, EDAS, WASPAS and MOORA methods. Spearman rank correlation coefficient was calculated between the ranks obtained by various methods. The ranks obtained by these methods were even compared with the ranks of other MCDM methods and it was found that the relatively new methods CODAS, EDAS and WASPAS were in good agreement with each other. © 2018 by the authors; licensee Growing Science, Canada

A comprehensive MCDM-based approach using TOPSIS and EDAS as an auxiliary tool for pyrolysis material selection and its application

Abstract: In the current period of energy development, it is very complex to produce energy from agricultural wastes due to the involvement of multiple criteria such as social, economical and environmental factors. In this study, a hybrid multi-criteria decision-making (MCDM) model based on the weight obtained from analytical hierarchy process (FAHP) has been utilized for ranking. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation based on Distance from Average Solution (EDAS) are proposed to evaluate the possibilities of utilizing locally available biomass. For this purpose, a number of criteria are defined from the viewpoint of yielding maximum bio-oil during pyrolysis. The proposed methods are having excellent agreement with each other, and they are exactly matched with the experimental results. This study consists of seven biomass alternatives with seven evaluation criteria. Out of seven selected biomass materials, sugarcane bagasse is ranked top. The experimental results confirmed the prediction with maximum bio-oil yield of 48.5 wt% obtained from sugarcane bagasse. At the end of the study, the obtained bio-oil from top ranked biomass material was analysed for physical, elemental and chemical compositions using Fourier-transform infrared (FTIR) spectroscopy and gas chromatography (GC) for its utility assessment. This study gives new insights into decision-making, specifically thermochemical conversion process.

Prioritizing intentions behind investment in cryptocurrency: a fuzzy analytical framework

Abstract: The primary objective of this study is to prioritize the main intentions behind investment in cryptocurrency, in spite of its volatile nature and no regulatory framework.,This research paper has worked on collective constructs of the unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM) and social support theory with an added construct of financial literacy. A fuzzy analytical framework has been applied to prioritize the intentions of investors.,The result indicates that “Social Influence (SI)” is the most influencing factor, while “Effort Expectancy (EE)” is the least influencing factor considered by investors. The subdimensions ranked in the top priority by investors are as follows: “I want to invest in cryptocurrencies because I have a good level of financial knowledge (FL1)”; “The people who are important to me will think that I should use cryptocurrencies (SI2)”; “I have the necessary resources to use cryptocurrencies (FC2).” The least importance is given to “It will be easy for me to become an expert in the use of cryptocurrencies (EE3).”,Few of the constructs of the UTAUT, the TAM and social support theory have been considered while prioritizing intentions. Different other intentions also prevail under different theories that need to be researched further.,Unlike previous studies, this research adds the archetype of social commerce, social support and utility theories to analyze and prioritize the behavioral perspective of using cryptocurrencies in digital transactions.,This paper fills the gap in the research study, along with assisting the regulators and cryptocurrency practitioners to widen their knowledge base and to recognize the prioritized intentions.

Fintech investments in European banks: a hybrid IT2 fuzzy multidimensional decision-making approach

Abstract: Financial technology (Fintech) makes a significant contribution to the financial system by reducing costs, providing higher quality services and increasing customer satisfaction. Hence, new studies play an essential role to improve Fintech investments. This study evaluates Fintech-based investments of European banking services with an application of an original methodology that considers interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory and IT2 fuzzy TOPSIS models. Empirical findings are controlled for consistency by applying the VIKOR method. Moreover, we conduct a sensitivity analysis by considering six distinct cases. This study contributes to the existing literature by identifying the most important Fintech-based investment alternatives to improve the financial performance of European banks. Our empirical findings illustrate that results are coherent, reliable, and identify “competitive advantage” as the most important factor among Fintech-based determinants. Moreover, “payment and money transferring systems” are the most important Fintech-based investment alternatives. It is recommended that, among Fintech-based investments, European banks should mainly focus on payment and money transferring alternatives to attract the attention of customers and satisfy their expectations. This is also believed to have a positive impact on the ease of bank’ receivable collection. Another important point is that Fintech-based investments in money transferring systems could help to decrease costs.

State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms

Abstract: The durability and reliability of battery management systems in electric vehicles to forecast the state of charge (SoC) is a tedious task. As the process of battery degradation is usually non-linear, it is extremely cumbersome work to predict SoC estimation with substantially less degradation. This paper presents the SoC estimation of lithium-ion battery systems using six machine learning algorithms for electric vehicles application. The employed algorithms are artificial neural network (ANN), support vector machine (SVM), linear regression (LR), Gaussian process regression (GPR), ensemble bagging (EBa), and ensemble boosting (EBo). Error analysis of the model is carried out to optimize the battery’s performance parameter. Finally, all six algorithms are compared using performance indices. ANN and GPR are found to be the best methods based on MSE and RMSE of (0.0004, 0.00170) and (0.023, 0.04118), respectively.