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The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.

Scatter in the outcome of repeated experiments is unavoidable due to measurement errors in addition to the non-linear nature of the output responses with unknown influential input parameters. It is a standard practice to select an orthogonal array in the Taguchi approach for tracing optimum input parameters by conducting a few number of experiments and confirm them through additional experimentation (if necessary). The purpose of this paper is to present a simple methodology and its validation with existing test results in finding the expected range of the output response by suggesting modifications in the Taguchi method.

The modified Taguchi approach is proposed to find the optimum process parameters and the expected range of the output response.

This paper presents a simple methodology and its validation with existing test results in finding the expected range of the output response by suggesting modifications in the Taguchi method.

Adequacy of this methodology should be examined by considering the test data on different materials and structures.

The introduction of Chauvenet’s criterion and opposing the signal-to-noise ratio transformation on repeated experiments of each test run will provide fruitful results and less computation burden.

This paper adopts a modified Taguchi approach to develop empirical relationships to the performance characteristics (output responses) in terms of process variables and demonstrated their validity through comparison of test data. The method suggests a few tests as per the orthogonal array and provides complete information for all combinations of levels and process variables. This method also provides the estimated range of output responses so that the scatter in the repeated tests can be assessed prior to the tests.

In order to obtain defect-free products meeting the required specifications, researchers have conducted extensive experiments using powder bed fusion (PBF) process measuring the performance indicators (namely, relative density, surface roughness and hardness) to specify a set of printing parameters (namely, laser power, scanning speed and hatch spacing). A simple and reliable multi-objective optimization method is considered in this paper for specifying a set of optimal process parameters with SS316 L powder. It was reported that test samples printed even with optimal set of input variables revealed irregular shaped, microscopic porosities and improper melt pool formation.

Finally, based on detailed analysis, it is concluded that it is impossible to express the performance indicators, explicitly in terms of equivalent energy density (E_0ˆ*), which is a combination of multiple sets of selective laser melting (SLM) process parameters, with different performance indicators. Empirical relations for the performance indicators are developed in terms of SLM process parameters. Test data are within/close to the expected range.

Based on extensive analysis of the SS316 L data using modified Taguchi approach, the optimized process parameters are laser power = 298 W, scanning speed = 900 mm/s and hatch distance = 0.075 mm, for which the results of surface roughness = 2.77 Ra, relative density = 99.24%, hardness = 334 Hv and equivalent energy density is 4.062. The estimated data for the same are surface roughness is 3.733 Ra, relative density is 99.926%, hardness is 213.64 Hv and equivalent energy density is 3.677.

Even though equivalent energy density represents the energy input to the process, the findings of this paper conclude that energy density should no longer be considered as a dependent process parameter, as it provides multiple results for the specified energy density. This aspect has been successfully demonstrated in this paper using test data.

This study aims to provide a bibliometric analysis and systematic literature review of Industry 4.0 (I4.0) research in the supply chain (SC) area and to understand related contemporary research trends. I4.0 has the potential to change the way goods are manufactured, distributed and made available to customers through the digitalisation of SC. Although I4.0 originated in 2011 in Germany, its application in managing the SC has only recently started gaining momentum. Therefore, it is essential to understand the research progress and identify the current trends of I4.0 application in the SC field.

A bibliometric analysis was conducted to empirically analyse the literature related to I4.0 implementation in the SC. This study retrieved papers from the Scopus database, reviewing 1,155 articles from the period 2016 to 2023 (November) for bibliometric analysis. Bibliometrix, using R software, was used for the bibliometric analysis, and VOSviewer was used for network analysis.

The findings provide an overview of the most relevant journals, most productive scholars, top academic institutions and top countries contributing to I4.0 research in the SC context. The results show that the most recent research contributions are related to the topics of SC performance, sustainability, digitalisation and digital transformation. Furthermore, a detailed review of articles published in the three and above-rated journals in the Chartered Association of Business Schools list is presented.

The novelty of this study lies in identifying the current research trends and themes of I4.0 research in the SC area. This research benefits researchers by identifying potential research areas for I4.0 implementation in the SC and providing directions for future research.

The purpose of this paper is to adopt the multi-objective optimization technique for identifying a set of optimum abrasive water jet machining (AWJM) parameters to achieve maximum material removal rate (MRR) and minimum surface roughness.

Data of a few experiments as per the Taguchi’s orthogonal array are considered for achieving maximum MRR and minimum surface roughness (Ra) of the Inconel718. Analysis of variance is performed to understand the statistical significance of AWJM input process parameters.

Empirical relations are developed for MRR and Ra in terms of the AWJM process parameters and demonstrated their adequacy through comparison of test results.

The signal-to-noise ratio transformation should be applied to take in to account the scatter in the repetition of tests in each test run. But, many researchers have adopted this transformation on a single output response of each test run, which has no added advantage other than additional computational task. This paper explains the impact of insignificant process parameter in selection of optimal process parameters. This paper demands drawbacks and complexity in existing theories prior to use new algorithms.

Taguchi approach is quite simple and easy to handle optimization problems, which has no practical implications (if it handles properly). There is no necessity to hunt for new algorithms for obtaining solution for multi-objective optimization AWJM process.

This paper deals with a case study, which demonstrates the simplicity of the Taguchi approach in solving multi-objective optimization problems with a few number of experiments.

The purpose of this paper is to optimize the fusion zone grain size and hardness using Hooke and Jeeves Algorithm.

Experiments are conducted as per four factors, five levels response surface method based central composite design matrix. Empirical relations for predicting grain size and harness are developed. The effect of welding variables on grain size and hardness are studies. Grain size and hardness are optimised using Hooke and Jeeves Algorithm.

The developed empirical relations can be effectively used to predict grain size and hardness values of micro plasma arc welded Inconel 625 sheets. The values of grain size and hardness obtained by Hooke and Jeeves Algorithm matches with experimental values with great accuracy.

The developed mathematical models are valid for 0.25 mm thick Inconel 625 sheets only.

In the present paper only four important factors namely peak current, back current, pulse rate and pulse width are considered, however one may consider other parameters like plasma gas flow rate, shielding gas flow rate, etc.

The present work is very much useful to sheet metal industries manufacturing metal bellows, diaphragms, etc.

Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.

Cloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.

The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.

This paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

The purpose of this paper is to review the nature and extent of foodborne diseases in India due to chemical and microbial agents.

The scientific investigations/reports on outbreak of foodborne diseases in India for the past 29 (1980‐2009) years due to adulteration, chemical, and microbiological contamination have been reviewed. Reported scientific information on foodborne pathogens detected and quantified in Indian foods has also been reviewed.

A total of 37 outbreaks involving 3,485 persons who have been affected due to food poisoning have been reported in India. Although the common forms of foodborne diseases are those due to bacterial contamination of foods, however, higher numbers of deaths have been observed due to chemical contaminants in foods.

A national foodborne disease surveillance system needs to be developed in India in order to enable effective detection, control and prevention of foodborne disease outbreaks.

Determining fiber orientations around geometric discontinuities is challenging and simultaneously crucial when designing laminates against failure. The purpose of this paper is to present an approach for selecting the fiber orientations in the vicinity of a geometric discontinuity; more specifically round holes with edge cracks. Maximum stresses in the discontinuity region are calculated using Classical Lamination Theory (CLT) and the stress concentration factor for the aforementioned condition. The minimum moment to cause failure in a lamina is estimated using the Tsai–Hill and Tsai–Wu failure theories for a symmetric general stacking laminate. Fiber orientations around the discontinuity are obtained using the Tsai–Hill failure theory.

The current research focuses on a general stacking sequence laminate under three-point bending conditions. The laminate material is S2 fiber glass/epoxy. The concepts of mode I stress intensity factor and plastic zone radius are applied to decide the radius of the plastic zone, and stress concentration factor that multiplies the CLT stress distribution in the vicinity of the discontinuity. The magnitude of the minimum moment to cause failure in each ply is then estimated using the Tsai–Hill and Tsai–Wu failure theories, under the aforementioned stress concentration.

The findings of the study are as follows: it confirms the conclusions of previous research that the size and shape of the discontinuity have a significant effect on determining such orientations; the dimensions of the laminate and laminae not only affect the CLT results, but also the effect of the discontinuity in these results; and each lamina depending on its position in the laminate will have a different minimum load to cause failure and consequently, a different fiber orientation around the geometric discontinuity.

This paper discusses an important topic for the manufacturing and design against failure of Glass Fiber Reinforced Plastic (GFRP) laminated structures. The topic of introducing geometric discontinuities in unidirectional GFRP laminates is still a challenging one. This paper addresses these issues under 3pt bending conditions, a load condition rarely approached in literature. Therefore, it presents a fairly simple approach to strengthen geometric discontinuity regions without discontinuing fibers.

The purpose of this study is to reveal the friction reduction performance and mechanism of granular flow lubrication during the milling of difficult-to-machining materials and provide a high-performance lubrication method for the precision cutting of nickel-based alloys.

The milling tests for Inconel 718 superalloy under dry cutting, flood lubrication and granular flow lubrication were carried out, and the milling force and machined surface quality were used to evaluate their friction reduction effect. Furthermore, based on the energy dispersive spectrometer (EDS) spectrums and the topographical features of machined surface, the lubrication mechanism of different granular mediums was explored during granular flow lubrication.

Compared with flood lubrication, the granular flow lubrication had a significant force reduction effect, and the maximum milling force was reduced by about 30%. At the same time, the granular flow lubrication was more conducive to reducing the tool trace size, repressing surface damage and thus achieving better surface quality. The soft particles had better friction reduction performance than the hard particles with the same particle size, and the friction reduction performance of nanoscale hard particles was superior to that of microscale hard particles. The friction reduction mechanism of MoS₂ and WS₂ soft particles is the mending effect and adsorption film effect, whereas that of SiO₂ and Al₂O₃ hard particles is mainly manifested as the rolling and polishing effect.

Granular flow lubrication was applied in the precision milling of Inconel 718 superalloy, and a comparative study was conducted on the friction reduction performance of soft particles (MoS₂, WS₂) and hard particles (SiO₂, Al₂O₃). Based on the EDS spectrums and topographical features of machined surface, the friction reduction mechanism of soft and hard particles was explored.