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The purpose of this study is to investigate the best fleet for a new purchase based on multi-objective optimization on the basis of ratio (MOORA), reference point and multi-MOORA methods. This study further identifies critical parameters for fleet performance monitoring and exploring optimum range of critical parameters using Monte Carlo simulation. At the end of this study, fleet maintenance management and operations have been discussed in the perspectives of risk management.

Fleet categories and fleet performance monitoring parameters have been identified using the literature survey and Delphi method. Further, real-time data has been analyzed using MOORA, reference point and multi-MOORA methods. Taguchi and full factorial design of experiment (DOE) are used to investigate critical parameters for fleet performance monitoring.

Fleet performance monitoring is done based on fuel consumption (FC), CO₂ emission (CE), coolant temperature (CT), fleet rating, revenue generation (RG), fleet utilization, total weight and ambient temperature. MOORA, reference point and multi-MOORA methods suggested the common best alternative for a particular category of the fleet (compact, hatchback and sedan). FC and RG are the critical parameters for monitoring the fleet performance.

The geographical aspects have not been considered for this study.

A pilot run of 300 fleets shows saving of Rs. 2,611,013/- (US$36,264.065), which comprises total maintenance cost [Rs. 1,749,033/- (US$24,292.125)] and FC cost [Rs. 861,980/- (US$11,971.94)] annually.

Reduction in CE (4.83%) creates a positive impact on human health. The reduction in the breakdown maintenance of fleet improves the reliability of fleet services.

This study investigates the most useful parameters for fleet management are FC, CE, CT. Taguchi DOE and full factorial DOE have identified FC and RG as a most critical parameters for fleet health/performance monitoring.

Selection of fabrics for particular purposy-e has created lots of research interest over the years, and the said problems have been addressed by several researchers using various multi-criteria decision-making (MCDM) methods. The main purpose of this paper is to highlight a maiden approach to handle one such fabric selection problem using multi-objective optimization by ratio analysis (MOORA) as a simple yet potent MCDM tool.

Two approaches of MOORA method (namely, ratio system and reference point) have been demonstrated for ranking of 13 candidate fabrics based on four fabric attributes, namely, fabric cover, thickness, areal density and porosity.

In both the approaches, candidate fabric F3 secures rank 1 (the best alternative) whereas fabric F6 occupies rank 13 (the worst alternative). Moreover, ranking orders of these two approaches are alike and also show very high level of congruence with those of other approaches reported by earlier researchers, as evidenced from extremely high rank correlation coefficients (R_s > 0.89). During sensitivity analysis, each of the ranking results obtained from the four simulated weight sets do demonstrate very high degree of correlation (R_s > 0.90 for ratio system, and R_s > 0.81 for reference point). Besides, no occurrence of rank reversal is observed even when the initial decision-making matrix is changed.

Most the methods adopted so far for fabric selection purpose involve huge mathematical equations, complex computation and/or logic. The uniqueness of the MOORA method is that it involves minimal and thus very simple mathematical operations although possesses higher level of robustness and reliability.

In today's competitive global marketplace, performance management has been identified as a key strategic consideration towards achieving an efficient supply chain management. The task of estimating supply chain performance extent is seemed a complex problem entitled with multiple subjective performance measures and metrics; subjected to decision-making environment which involves an inherent vagueness, inconsistency and incompleteness associated with decision-makers (DMs) (expert panel) commitment towards assessment of various subjective (quantitative) evaluation indices. Consequently, it becomes difficult towards making a comparative study on performances of alternative supply chains. It is, therefore, indeed essential to conceptualize and develop an efficient appraisement platform helpful for benchmarking of alternative supply chains based on their performance extent. The paper aims to discuss these issues.

The work explores the concept of grey numbers combined with multi-objective optimization by ratio analysis (MOORA) in perceptive to evaluate best alternative from among available alternative supply chains.

The method has been found fruitful to facilitate such a multi-criteria group decision-making (MCGDM) problem under uncertain environment and provides an appropriate compromise ranking order with respect to available possible alternatives.

Supply chain performance appraisement provides necessary means by which an organization can assess whether its supply chain is performing well, whether it has been improved or degraded as compared to the past record. The purpose of this research is to develop and to empirically test a multiple-indices hierarchical appraisement model for benchmarking of supply chain performance and its impact on competitiveness of manufacturing industries.

The primitive purpose of this manuscript is to develop an effective and efficient computer numerical control (CNC) machine tool evaluation index from the perspective of appraisal and benchmarking of preferred candidate machine tool in subjective information scenario. In this reporting, manager has been facilitated from the decision making tool and methodology in order to evaluate the best one and benchmarking the preferred candidate alternative machine tool under the subjective criterion circumstances.

A MULTI-MOORA (multi-objective optimization by ratio analysis) methodology conjunction with grey number has fruitfully applied in evaluated subjective information against criterion module plate form from the prospectus of handling the vagueness, inconsistency, impreciseness and in order to appraisal and benchmarking of the candidate CNC machine tool alternatives.

In today scenario, the subjective evaluation criterion has even ran over the CNC machine tool evaluation module (index) except other valuable area on account of an abatement of consistent data. The authors found out the subjective information is even necessary/mandatory to handling and tackle such an inconsistent, vagueness, impreciseness which associated uncertain criterion. So, the authors found out that the application of grey number conjunction with MULTI-MOORA decision methodology from the prospectus of appraisal and benchmarking of preferred candidate alternatives machine tool.

The major contribution of this manuscript to exploration of grey number set conjunction with MULTI-MOORA methodology toward appraisal and benchmarking of preferred CNC candidate machine tool alternative, handled and tackled the evaluated subjective information from expert panel against subjective criterion environment, facilitates the multi-criterion decision making (MCDM) module from the prospectus of best one and ranking order the candidate machine tool alternative under the similar subjective criterion circumstances.

Due to the increasing demand of customer and competitive market pressure, manufacturing organizations should be modernized in strategies, production operations, processes and its procedures to remain competitive. So, a flexible manufacturing system (FMS) was adopted by the manufacturing system to fight with competitive pressure. The purpose of this paper is to enhance the performance of manufacturing system, with a focus on its factors.

In this research, the ranking of the performance factor of FMSs is done by using multiple attribute decision-making (MADM) methods as multi-objective optimization on the basis of ratio analysis (MOORA) and preference selection index (PSI). Weights of attributes are defined by the AHP method.

Ranking of performance factor is done on the basis of six variables which affect three elements of performance of FMS, i.e. productivity, flexibility and quality. MOORA is applied in three ways such as the ratio-based, reference point and full multiplicative method. In the MOORA method, ranking was done considering weights of attributes and also without it. A PSI method is used to find the best factor among the factors. The results of these methodologies, i.e. MOORA and PSI, are same, i.e. productivity is the primary factor in the manufacturing system. The ranking is validated by the result of different methodology used in this research.

This research has evaluated the important factors and performance variables which can enhance the performance of manufacturing organizations. So, the manufacturing persons can focus on these to enhance its performance.

Combined MADM methods, i.e. MOORA and PSI methodologies, are used in this paper to deal with the ranking of performance factors of the FMS considering qualitative characteristics. These approaches show the conversion of a qualitative attribute to quantitative attributes by using fuzzy logic.

In the context of sharing economy, the superhost program of Airbnb emerges as a phenomenal success story that has transformed the tourism industry and garnered humongous popularity. Proper performance evaluation and classification of the superhosts are crucial to incentivize superhosts to maintain higher service quality. The main objective of this paper is to design an integrated multicriteria decision-making (MCDM) method-based performance evaluation and classification framework for the superhosts of Airbnb and to study the variation in various contextual factors such as price, number of listings and cancelation policy across the superhosts.

This work considers three weighting techniques, mean, entropy and CRITIC-based methods to determine the weights of factors. For each of the weighting techniques, an integrated TOPSIS-MOORA-based performance evaluation method and classification framework have been developed. The proposed methodology has been applied for the performance evaluation of the superhosts (7,308) of New York City using real data from Airbnb.

From the perspective of performance evaluation, the importance of devising an integrated methodology instead of adopting a single approach has been highlighted using a nonparametric Wilcoxon signed-rank test. As per the context-specific findings, it has been observed that the price and the number of listings are the highest for the superhosts in the topmost category.

The proposed methodology facilitates the design of a leaderboard to motivate service providers to perform better. Also, it can be applicable in other accommodation-sharing economy platforms and ride-sharing platforms.

This is the first work that proposes a performance evaluation and classification framework for the service providers of the sharing economy in the context of tourism industry.

In most developing countries riveting, upset forging and punching operations among others are performed using manual hammering technique. The use of the manual method increases production time and reduces efficiency. The use of the manual approach is predominantly due to the high cost of imported automated hammering machines (AHM) which the majority of the end-users are incapable of acquiring. The purpose of this paper, therefore, is to produce an AHM that is affordable using an effective material selection methodology in the design and fabrication process.

The material selection methodology proposed is the fuzzy multi-objective optimisation on the basis of the ratio analysis (MOORA) method. The tool was used to evaluate and determine the optimum material for the major of the components of the AHM from amongst alternative materials while considering several decision criteria. A case study of the shaft was applied to demonstrate the suitability of the proposed technique. The AHM components design is then carried out and machine fabricated and tested to ascertain performance effectiveness.

The result of the fuzzy MOORA evaluation showed that alloy steel is the optimal material for the shaft. The fuzzy MOORA approach was compared with the fuzzy Vlsekriterijumska Optimizacija Ikompromisno Resenje (VIKOR) and fuzzy grey relational analysis (GRA) methods to validate the proposed method. The fuzzy MOORA method produces completely the same result with the fuzzy VIKOR and fuzzy GRA methods. The machine was then designed, constructed and tested and found to be effective for the purpose of the design.

This is significant as no such study has been published by any other researcher to the best of our knowledge in this area.

The recent global market trend is seemed enforcing existing manufacturing organizations (as well as service sectors) to improve existing supply chain systems or to take up/adapt advanced manufacturing strategies for being competitive. The concept of the agile supply chain (ASC) has become increasingly important as a means of achieving a competitive edge in highly turbulent business environments. An ASC is a dynamic alliance of member enterprises, the formation of which is likely to introduce velocity, responsiveness, and flexibility into the manufacturing system. In ASC management, supplier/partner selection is a key strategic concern. Apart from traditional supplier/partner selection criteria; different agility-related criteria/attributes need to be taken under consideration while selecting an appropriate supplier in an ASC. The paper aims to discuss these issues.

Therefore, evaluation and selection of potential supplier in an ASC have become an important multi-criteria decision making problem. Most of the evaluation criteria being subjective in nature; traditional decision-making approaches (mostly dealing with objective data) fail to solve this problem. However, fuzzy set theory appears an important mean to tackle with vague and imprecise data given by the experts. In this work, application potential of the fuzzy multi-level multi-criteria decision making (FMLMCDM) approach proposed by Chu and Velásquez (2009) and Chu and Varma (2012) has been examined and compared to that of Fuzzy-techniques for order preference by similarity to ideal solution (TOPSIS) and Fuzzy-MOORA in the context of supplier selection in ASC.

It has been observed that similar ranking order appears in FMLMCDM as well as Fuzzy-TOPSIS. In Fuzzy-MOORA, the best alternative appears same as in case of FMLMCDM as well as Fuzzy-TOPSIS; but for other alternatives ranking order differs. A comparative analysis has also been made in view of working principles of FMLMCDM, Fuzzy-TOPSIS as well as Fuzzy-MOORA.

Application feasibility of FMLMCDM approach has been verified in comparison with Fuzzy-TOPSIS and Fuzzy-MOORA in the context of agile supplier selection.

This study aims to identify and prioritize the key practices and strategies for effective global sourcing and supply chain management (SCM).

The study uses a combination of Pareto analysis and multi-objective optimization based on ratio analysis research methodology to analyze and establish the relationships among the identified key practices and strategies. Pareto analysis enables organization to prioritize organizational efforts and resources by focusing on the most critical factors.

The study shows that the “eco-friendly sourcing strategy”, “lean manufacturing” and “tool cost analysis” are the top critical practices and strategy variables for global sourcing and SCM, whereas the “risk management”, “procurement strategy” and “leverage digital solutions” are the critical practices and strategy variables.

The findings of this research can also assist organizations in making informed decisions to optimize their global sourcing and supply chain operations.

By using these methods, this research paper gives valuable insights into the critical practices and strategies that can enhance efficiency, mitigate risks and drive success in global sourcing and SCM. The subjects and elements this study identified will serve as a framework and suggestions for further theoretical investigation and real-world implementations.

The advent of globalization not only made the manufacturing sector highly competitive but also facilitated best-quality products. The trend is further augmented by e-Commerce which increases the penetration to the targeted customer with the easy availability of customized product. In this backdrop, Indian manufacturing industries are striving hard to seek out best systems which will yield maximum profitability. Time is ripe to realize the true potential of agile manufacturing system (AMS). Infusion of AMS in manufacturing industry will bring forth the elusive mix of customer needs and products at lowest possible cost. But adoption and implementation of AMS is a challenging task in itself. There are certain facilitators and criteria which not only facilitate the system but also help in the effective and smooth implementation of this system. The purpose of this paper is to identify the criteria to weightage and ranking to AMS facilitators. This study was carried out by different approaches, namely, entropy approach, multi-objective optimization on the basis of ratio analysis (MOORA) method, Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) analysis and a cross-sectional survey of manufacturing firms in India.

The criteria and facilitators are identified followed by the application of entropy approach, MOORA method and VIKOR analysis to study and analyze the criteria weight and provide the ranking to AMS facilitators.

The result of the entropy approach concludes that beneficiary and non-beneficiary criteria carry 48.43 and 51.56 percent weight, respectively. MOORA method and VIKOR analysis conclude that organization structure and virtual enterprise facilitators are carrying the first and second rank, respectively.

This study is completed on the basis of responses from few experts from industry and academicians who may not reflect the attitude of entire industry community.

This research is expected to facilitate policy makers in government and industries to frame policies for optimum utilization of resources and infrastructure for better performance. This paper helps the researcher to do a case study on the implementation of AMS and then finally helps to society in getting the high-quality product in an easy way.

Integration of entropy approach, MOORA method and VIKOR analysis with identification of AMS criteria weightage and ranking to AMS facilitators has been recommended for an industry which is an innovative effort for the execution of AMS.