Selection of an Optimal Maintenance Strategy Under Uncertain Conditions: An Interval Type-2 Fuzzy AHP-TOPSIS Method

Mathew, Manoj; Chakrabortty, Ripon K.; Ryan, Michael J.

doi:10.1109/tem.2020.2977141

Cited by 75 publications

(24 citation statements)

References 43 publications

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“…MCDM approaches like AHP, Fuzzy AHP and DEMATEL have been employed to rank the criteria in various research studies such as behavioural finance, banking, financial reporting, taxation and industrial asset maintenance (Antony & Joseph, 2017; Gupta et al, 2020; Manda & Bansal, 2020; Mathew et al, 2020). The study uses AHP to rank the investment avenues in India.…”

Section: Methodsmentioning

confidence: 99%

Impact of COVID‐19 on portfolio allocation decisions of individual investors

Himanshu¹,

Ritika

Mushir³

et al. 2021

Journal of Public Affairs

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Covid‐19 has impacted the financial markets dramatically. The risk and return expectations of investors have changed, leading them to reallocate their portfolios. This paper aims to analyse the impact of Covid‐19 on the portfolio allocation decisions of individual investors. The study examines the perceptions of investors about various investment avenues before and during the period of extreme uncertainty caused by the COVID‐19 pandemic. The data were collected from individual investors residing in Delhi and Mumbai. AHP is used to rank the investment preferences of the respondents. The results show that due to the present financial crisis pertaining to COVID‐19, investors have started reallocating their portfolios. Since the returns on risky assets are not as expected, investors are moving towards a conservative portfolio. However, the case of transition from risky to risk‐free assets is not the same in the case of all investors.

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Section: Methodsmentioning

confidence: 99%

Impact of COVID‐19 on portfolio allocation decisions of individual investors

Himanshu¹,

Ritika

Mushir³

et al. 2021

Journal of Public Affairs

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“…Hence, TOPSIS is not an appropriate method for this study AHP is considered better than other MCDM methods as it easier to use and has wide applicability (Harputlugil et al, 2011). It is a well-known and widely used decision making tool in business organizations (Govindan et al, 2014;Mathew et al, 2020). AHP decomposes, organizes and analyzes the problem, and converts the problem into a hierarchical structure, which allows decision maker to pay attention on criterion and sub-criterion during the process of weight allocation (Ishizaka and Labib, 2011).…”

Section: Solution Methodologymentioning

confidence: 99%

Analysis of Barriers to Closed-Loop Supply Chain: A Case of the Indian Automotive Industry

Bhatia

Jakhar

Dora

2022

IEEE Trans. Eng. Manage.

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Closed loop supply chain (CLSC) implementation has become imperative for organizations owing to resource scarcity and generation of toxic waste. However, CLSC implementation can be very challenging due to several barriers, especially in an emerging manufacturing hub such as India. This research identifies and analyzes the prominent barriers to CLSC implementation in the Indian automotive sector. Further, the paper also examines causal relationships between the barriers. A total of 22 barriers to CLSC implementation are identified from literature and subsequently, validated by industry experts. The finalized barriers are then evaluated by Analytical Hierarchy Process (AHP) and grey-Decision Making Trial and Evaluation Laboratory (DEMATEL) techniques. The study provides managers and policy makers with insights on the most prominent and causal barriers, and recommends strategies to overcome these barriers. The findings will help managers to frame short-term and long-term strategies to mitigate barriers to CLSC and contribute towards successful implementation of CLSC.

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“…For checking the robustness of the method, a sensitivity analysis (Mathew, Chakrabortty, & Ryan, 2020) is done by changing the weights of the criteria and checking the effect of weight change in final ranking of the alternative (banks). The user defined weights obtained from AHP is shown in set 1, while the altered weights are given in set 2 to set‐7, which is shown in Table 8.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking the private sector banks in India using MCDM approach

Gupta

Mathew²,

Gupta

et al. 2020

Journal of Public Affairs

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The study aims to rank the performance of Indian private sector banks on the basis of their financial capability. The study is focussed on those banks which are listed in the Bombay Stock Exchange (BSE), for the period from 2014–15 to 2018–19. The financial performance of selected Indian private banks is ranked using a hybrid Multi‐Criteria Decision Making (MCDM) technique, that is, AHP‐TOPSIS. The Analytical Hierarchy Process (AHP) is used to determine the weights of 10 financial performance pointers, and further Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to provide rankings and benchmarking of the private sector banks. Thereafter, overall ranking is given to the selected banks using interval‐valued TOPSIS (IV‐TOPSIS) for a combined period of 5 years. Sensitivity analysis has been performed to check the robustness of the method and its impact on the final ranking of the banks. Results showed that HDFC bank is the top performer and is ranked first for all the years consecutively during the period 2014–2019 and has set a financial indicator benchmark for other competitor banks. South Indian Bank showed the worst results in each year and stood last in the overall ranking. The IndusInd bank is followed after HDFC bank by improving its performance from 2014–19. With a slight margin, Kotak Mahindra Bank ranked after IndusInd. Through the AHP‐TOPSIS technique, each year the private sector banks ranking differs among themselves depending upon the financial performance pointers. Based on interval‐valued TOPSIS (IV‐TOPSIS), it was found that the overall HDFC was the top‐performing bank and South Indian Bank was the worst‐performing bank among all other banks. The results obtained from sensitivity analysis were found to be consistent with IV‐TOPSIS. The paper used the AHP technique, which is subjective in nature, and the results depend on the opinion of the experts. This study will assist the investors who want to channelize their funds in private banks having shares enlisted in BSE. It will accommodate banks to compare and judge their financial performance along with improving their performance and planning their future strategy. AHP‐TOPSIS utilized by past researchers is unable to bring out the cumulative performance for banks, but in this paper, IV‐TOPSIS is utilized, which can be effectively used to evaluate the cumulative performance of banks. The study tries to fill the gap in the literature by providing the overall ranking to private sector banks in India on the basis of financial indicators.

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Selection of an Optimal Maintenance Strategy Under Uncertain Conditions: An Interval Type-2 Fuzzy AHP-TOPSIS Method

Cited by 75 publications

References 43 publications

Impact of COVID‐19 on portfolio allocation decisions of individual investors

Impact of COVID‐19 on portfolio allocation decisions of individual investors

Analysis of Barriers to Closed-Loop Supply Chain: A Case of the Indian Automotive Industry

Benchmarking the private sector banks in India using MCDM approach

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