Industrial Problems Research Articles

Investigation of Performance Properties of Wool Fabrics Treated with Bromelain from Pineapple Peel Wastes

The pilling and shrinkage of wool fabrics are major problems in the textile industry. Chemical treatments are used to improve the performance properties of wool fabrics. These processes severely pollute the ecosystem. This study is aimed to use bromelain isolated from pineapple peel waste instead of toxic chemicals used during pretreatments to prevent shrinkage and minimize pilling in the woolen textile industry. Bromelain was isolated from pineapple peels using different techniques and isolated bromelain to be used was also encapsulated. Encapsulation was preferred to increase enzyme stability and reusability and to reduce cost. Area shrinkage, pilling, tensile strength, elongation, and weight loss tests were performed on the treated fabrics. According to the findings of this study, the isolated and encapsulated bromelain from pineapple peel wastes improved the washability of the wool fabric and eliminated the pilling problem. This developed method is sustainable, low cost, high added value, innovative, and environmentally friendly.

First findings of Sarcocystis species in game deer and feral pigs in Australia

Several wild game meat species, including deer and feral pigs are hunted and consumed in Australia. Feral pigs and deer are not indigenous to Australia, but they have proliferated extensively and established their presence in every state and territory. Following the report of a sambar deer displaying Sarcocystis like white cysts in its rump muscles, the present study was conducted to explore the prevalence of Sarcocystis infections in wild deer and feral pigs in the southeastern regions of Australia. Oesophagus, diaphragm, and heart tissue from 90 deer and eight feral pigs were examined visually for sarcocysts. All results were negative. PCR testing of randomly selected deer and feral pigs yielded positive results, which were subsequently supported by histopathology. This is the first study to report the presence of Sarcocystis spp. in deer and feral pigs in Australia. As no visual cysts were found on the heart or oesophagus that came back positive with PCR, infected animals, particularly those reared free-range, could be passing through meat quality checks unidentified. If people consume this meat without cooking it properly, it may lead to a human infection of Sarcocystis. However, a more targeted study focused on determining the parasite's prevalence and assessing its risks is necessary to determine if it constitutes a food safety issue. As this species has been found not only in feral pigs but also in domestic pigs, the potential for infection spreading between feral pigs and pigs in free-range livestock systems is high, potentially posing a large problem for the Australian pork industry, particularly with the increased emphasis on free-range pig husbandry. Future studies should concentrate on determining the species of Sarcocystis in feral animals commonly consumed as game meat to determine potential zoonotic risks. This could also include a more in-depth look at the prevalence of Sarcocystis infections in other game animals. Identifying where these parasites are present and to what extent, are important areas for future studies.

Current landscape and emerging trends in academic development of traditional Chinese medicine in new era

The discipline development is the pillar for the development of traditional Chinese medicine( TCM). The academic progress in TCM is the commanding height of the discipline development of TCM. To lead and promote the development and academic progress of TCM, the China Association of Chinese Medicine has summarized the Top Ten Academic Achievements in Traditional Chinese Medicine during 2020-2022, the Major Scientific Problems, Engineering Technical Problems, and Industrial Technical Problems in Traditional Chinese Medicine during 2019-2023, and the Remarkable Research Achievements of Traditional Chinese Medicine during 2012-2022. Based on the above research reports and the research achievements awarded the national science and technology prizes in TCM in the last 20 years and according to the current situation and layout of TCM discipline development, this paper reviews the major research achievements of TCM in the last two decades and the latest research progress in TCM during 2020-2023. The major scientific, engineering technical, and industrial technical problems in TCM are analyzed and the emerging trends of TCM are prospected in accordance with the development laws and characteristics of TCM. This review provides new ideas and reference for the high-quality development of TCM in the new era.

Declined card transactions: How online merchants can address the potential €15bn revenue hole caused by declines

Declined card transactions present a significant problem for the payment industry, causing inconvenience for consumers, lost sales for merchants and lost revenue for card issuers. An analysis of card-based e-commerce across the EEA and UK indicates a decline rate of 10 per cent for domestic transactions and 18 per cent for cross-border transactions, leading to a total declined value of €60bn per annum. Using data obtained from different sources, including merchants’ own records, and gateway, acquirer and scheme data, this paper identifies the main reasons why transactions are rejected, grouping them into four categories: valid business declines, avoidable business declines, risk-based declines, and technical declines. The study argues that merchants should focus on reducing decline rates in the latter three categories. Approaches might include use of the schemes’ account updater service; localisation of acquiring to the same country as the card issuer, where possible; smart routing to acquirers with higher success rates for the given transaction attributes; use of network tokens; leveraging of strong customer authentication exemption rules; and correction of technical issues in authorisation requests, such as incorrect setting of flags indicating the acceptance environment. According to PSE’s analysis, actionable codes account for around 25 per cent of all declines, totalling €15bn of transaction value (and €74m lost interchange for issuers). The paper therefore suggests that those merchants that are prepared to investigate and analyse the underlying causes of their declines have much to gain.

Influence of glycol ether additive with low molecular weight on the interactions between CO2 and oil: Applications for enhanced shale oil recovery

The high-efficient development of shale oil is one of the urgent problems in the petroleum industry. The technology of CO2 enhanced oil recovery (EOR) has shown significant effects in developing shale oil. The effects of several glycol ether additives with low molecular weight on the interactions between CO2 and oil were investigated here. The solubility of glycol ether additive in CO2 was firstly characterized. Then, the effects of glycol ether additives on the interfacial tension (IFT) between CO2 and hexadecane and the volume expansion and extraction performance between CO2 and hexadecane under different pressures was investigated. The experimental results show that diethylene glycol dimethyl ether (DEG), triethylene glycol dimethyl ether (TEG), and tetraethylene glycol dimethyl ether (TTEG) all have low cloud point pressure and high affinity with CO2. Under the same mass fraction, DGE has the best effect to reduce the IFT between hexadecane and CO2 by more than 30.0%, while an overall reduction of 20.0%–30.0% for TEG and 10.0%–20.0% for TTEG. A new method to measure the extraction and expansion rates has been established and can calculate the swelling factor accurately. After adding 1.0% DEG, the expansion and extraction amounts of CO2 for hexadecane are respectively increased to 1.75 times and 2.25 times. The results show that glycol ether additives assisted CO2 have potential application for EOR. This study can provide theoretical guidance for the optimization of CO2 composite systems for oil displacement.

Innovative Date Fruit Classifier Based on Scatter Wavelet and Stacking Ensemble

Dates are essential fruits loaded with vital nutrients that keep bones healthy and prevent bone-related disorders. Approximately 8.46 million tons of different types of dates are cultivated and produced annually around the globe. There are more than 400 types of dates that are time-consuming and expensive to produce. Classifying them using conventional methods is labor-intensive, and this is one of the biggest problems for the date industry. Dataset fruit classification plays a vital role in the food industry. Dates can be classified from a luxury class to a less quality class. Accordingly, the food industry needs an automotive date fruit classifier that can work in food factories. This study proposes a pioneering method to classify date fruit that relies on extracting features from the texture of dates using Scattering Wavelet Transformation (SWT). The SWT yields in numeric coefficients were found to be immune to the deformation of invariants. This feature set trains an ensemble classifier that combines a voting mechanism to eliminate overfitting. The ensemble classifier consists of a random forest, a support vector machine classifier, and a logistic regression hyper-learner. Our novel approach was tested on two benchmarked datasets. The first data set scored F1 between 0.95 and 1.0 at the same time. The second dataset registered F1 between 0.96 and 1.0 in each of the 20 date classes. Some dates are close to each other in texture, resulting in high false positives or recall, causing a lower F1 score accuracy degree. The novelty of this approach comes from the featured representative of each date class, relying on the texture of the fruit as a discriminative feature, not on the fruit shape or color, which may not be robust enough as distinguishable features, especially in date classes that are close to each other in shape. Doi: 10.28991/HIJ-2024-05-02-010 Full Text: PDF

A comprehensive energy flow analysis method based on hydrogen energy to electric energy conversion

Abstract With increasing global concerns about climate change, more and more countries and regions are turning to sustainable energy, and hydrogen energy, as one of the representatives of clean energy, is gradually becoming one of the key areas for development in various countries. Due to its high energy density, easy storage, and pollution-free characteristics, hydrogen energy has been widely used in fields such as automobiles, aviation, and petrochemicals. In the automotive industry, hydrogen energy, as a renewable and clean energy source, has become one of the development directions. Compared to traditional fuel, hydrogen-powered vehicles not only achieve zero emissions but also have higher energy efficiency and can achieve longer range. Currently, multiple countries around the world are accelerating the promotion of hydrogen-powered vehicles and building related infrastructure such as hydrogen refueling stations. In the aviation field, hydrogen energy also has broad application prospects. Traditional aviation fuel contains a lot of hydrocarbons, so the pollution problem of the aviation industry has been a concern. As a clean and renewable energy source, hydrogen energy not only solves the pollution problem in the aviation industry but also has a very high energy density, providing sufficient power to support aircraft flight. In addition to the automotive and aviation fields, hydrogen energy can also be widely used in the petrochemical industry. Currently, traditional petrochemical production processes emit many harmful substances such as carbon dioxide, greatly affecting environmental quality. By using hydrogen energy, harmful substances such as carbon dioxide generated in traditional petrochemical processes can be converted into renewable resources, thus achieving an environmentally friendly, efficient, and sustainable production model.

Prediction of the process capability for compression rubber part forming in the automotive supply chain

The paper proposes predicting production process capability for the compression rubber part in automotive supply chain management. Delivery of parts to tier 1 and OEM on time is the most important part of supply chain management, together with the delivery of on-quality and on-cost control to maintain the competitiveness of the supply chain. There are many suppliers to produce many automotive parts for tier 1. Therefore, the simulation approach properly predicts and prevents the process from getting into trouble during the actual production time. Production process quality control is critical to ensure that the good quality of the parts purchased can be delivered on time. Rubber parts are used widely in automotive, motorcycles, trucks, and other types of vehicles, with small sizes but in huge quantities to support general OEM brands and specific parts. The rubber part manufacturing process is complex and uncertain with compression moulding and rubber curing conditions. Therefore, good conditions can predict the production process's capability to commission and deliver on schedule.A neuro-fuzzy system is adopted and developed to deal with the uncertain process capability under multi-criteria decision-making.The methodology development can be used in the actual rubber part manufacturing supply chain environment and can predict uncertain problems that might occur in the subcontractor factories.The prediction of the production process capability of the rubber part supply chain might be more effective on the real-time monitoring control system and can be tracking location part progress for further planning both success or rescheduling.The platform can be applied to audit and test the actual industrial supply chain, and problem and research questions are brought about from the real supply chain in the local country.The methodology development was originally created for the particular supply chain in rubber automotive parts that can replace the existing system to obtain a more efficient performance evaluation process.

Association between hoof trimming frequency in primiparous cows and hoof health and survival in second lactation

Lameness, mostly resulting from claw lesions, causes major welfare problems in the dairy industry. One way to prevent claw lesions is hoof trimming, but the overall effect and the optimal hoof trimming frequency are unknown. In a retrospective cohort study, we investigated the association between hoof trimming frequency in primiparous cows and hoof health and culling in second lactation. We based our analysis on breed, calving and culling data in the period 2015-2018 for 30,613 primiparous cows in 202 Swedish dairy herds. Of the cows enrolled, 20% were not hoof-trimmed, 30% were trimmed once, 34% were trimmed twice, 13% were trimmed 3 times, and 2% were trimmed ≥ 4 times. We used multivariable mixed-effect logistic and multivariable multinomial logistic regression models to investigate the association between trimming frequency and 2 outcomes: hoof lesions at first trimming occasion within 90 d from second calving and culling in second lactation within 300 d after second calving. In general, cows trimmed 2 or 3 times during first lactation were at lower odds of having claw lesions compared with cows that were not trimmed (Odds Ratio (OR) = 0.66, 95% CI = 0.62-0.71 and 0.60, 95% CI = 0.55-0.66, respectively) or cows trimmed once (OR = 0.80, 95% CI = 0.75-0.85 and OR = 0.72, 95% CI = 0.66-0.79, respectively), and this beneficial effect was observed for most types of claw lesions. Moreover, cows trimmed 2, 3, or ≥ 4 times were at lower odds of being culled compared with cows that were not trimmed (OR = 0.71, 95% CI = 0.65-0.77, 0.68, 95% CI = 0.61-0.76 and 0.70, 95% CI = 0.54-0.90, respectively) or trimmed once (OR = 0.77, 95% CI = 0.71-0.83, OR = 0.74, 95% CI = 0.66-0.82 and OR = 0.75, 95% CI = 0.59-0.97, respectively). In particular, 2 trimmings, compared with 1 or no trimming, lowered the relative risk-ratio of dying or being euthanized on-farm, or being culled due to claw and leg disorders. More than 1 hoof trimming in first lactation also reduced the relative-risk ratio of being culled for other reasons. In conclusion, 2 or 3 hoof trimmings during first lactation were generally beneficial for hoof health in early second lactation and survival in second lactation. These findings can help improve animal welfare and production by reducing claw lesions, and thereby lameness among dairy cows, which would increase the longevity of dairy cows and the sustainability of the dairy industry.

Learning to generalize with latent embedding optimization for few- and zero-shot cross domain fault diagnosis

Ensuring the safety and reliability of rotating machinery in modern industrial production and intelligent manufacturing is of paramount importance. While deep learning-based fault diagnosis methods offer promise, the scarcity of fault samples and the variations in distributions between training and test data due to variable working conditions make it difficult for these methods to be applied in industrial scenarios. To surmount these obstacles, we present a novel solution: learning to generalize with latent embedding optimization. Our proposed method, tailored for few-shot and zero-shot cross domain fault diagnosis, shows promise in addressing the industrial fault diagnosis problems under small samples and various working condition. The proposed method builds upon the latent embedding optimization algorithm which capitalizes on the essence of meta-learning, effectively addressing few-shot challenges. Additionally, we harness an efficient pretraining model, enhancing feature extraction and domain adaptation, effectively handling the scarcity of fault data. In tackling the cross domain issue, we introduce an innovative meta-task organization and amplify the episode training strategy within meta-learning. These enhancements empower the model to develop the ability to generalize effectively. The proposed approach is substantiated through comprehensive case studies in bearing and gearbox fault diagnosis. The results demonstrate its exceptional efficacy in both few-shot and zero-shot cross domain fault diagnosis scenarios.

ADVANCEMENTS IN GREEN CHEMISTRY: DEVELOPING SUSTAINABLE CATALYSTS FOR ECO-FRIENDLY INDUSTRIAL PROCESSES

Green chemistry has become an important technique for solving environmental problems in the chemical industry, with an aim of developing sustainable catalysts for eco-friendly industrial processes. This review considers recent advancements in green chemistry particularly regarding development and application of sustainable catalysts. The study examines different varieties of sustainable catalysts from renewable materials, mild and energy-saving operational conditions as well as minimizing waste and enhancing recyclability. Furthermore, it also discusses key industrial applications like ammonia and hydrogen peroxide production using more sustainable processes. In addition, there is a growing embrace of green chemistry principles across other areas such as fine chemicals and pharmaceuticals. Nonetheless, some significant challenges still exist including need for more generalized and scalable means of discovery and optimization of catalysts. The paper underscores that bridging academia and industry gaps is crucial to successfully adopting green chemistry research in industrial contexts. For instance, accelerated discovery and optimization of sustainable catalysts depend heavily on advancements in data-driven strategies such as high-throughput experimentation as well as computational modeling.

Detection and Classification on Plant Disease using Deep Learning Techniques

Plant diseases are a major problem for the agriculture industry because they can cause large crop losses and jeopardize food security. Deep learning approaches have demonstrated encouraging results in automating plant disease diagnosis and detection in recent years. In the context of plant disease diagnosis, this study examines the efficacy of two well-known convolutional neural network architectures: DenseNet121 and VGG16. Plant Village datasets are used for pretrained and fine-tuning of the DenseNet121 and VGG16 architectures, respectively. The dataset includes Images of both healthy and sick plants. To guarantee the models' resilience and generalizability, the dataset include 15 different classes and 4 types of plants namely Tomato, Potato and Pepper Bell. We compare the accuracy, precision, recall, and F1-score of DenseNet121 and VGG16 for plant disease classification through extensive testing and analysis. To determine if they are practically feasible for use in real-world applications, we also examine their model complexity and computing efficiency. Our findings show that DenseNet121 and VGG16 can both correctly diagnose plant diseases in a variety of species. Although DenseNet121 outperforms VGG16 in terms of overall accuracy and computational efficiency, both models obtain high accuracy rates. Additionally, DenseNet121 has superior generalization performance, especially in identifying uncommon or underrepresented illness classes. All things considered, this work emphasizes the promise of deep learning models-DenseNet121 in particular-as useful instruments for automated plant disease identification and points to directions for further investigation to improve the efficiency and scalability of such systems for real-world use in agriculture.

Development and assessment of an integrated wind energy system for green steelmaking based on electric arc furnace route

Optimizing energy structure is important for coping with the increasing environmental problems in iron and steel industry. Wind energy is one of typical zero-carbon, inexhaustible, and accessible energy source for alleviating energy dilemma. In this paper, the electric arc furnace steelmaking processes integrated with wind energy system (EAF-WES) is developed. The input-output data of 1214 and 1536 heats with 30 % and 50 % hot metal ratio are traced and collected. Life cycle assessment and techno-economic analysis of EAF route integrated with traditional energy system (EAF-TES) and EAF-WES are further researched and compared from perspectives of life cycle carbon footprint (LCCF), production cost, wind energy price (WEP), carbon efficiency, carbon reduction benefit (CRB), and carbon tax. The results indicate that EAF-WES enjoys lower LCCF but higher production cost and carbon efficiency than EAF-TES. Considering the price fluctuations, the optimal EAF steelmaking pattern with lowest production cost under different extent of WEP and carbon tax is suggested. When the electricity price is 0.043 and 0.046 $/kWh, the CRB is balanced. It is concluded that EAF-WES is a low-carbon, high-productive, environmentally friendly, and economical route. This research is expected to supply a technical idea for energy conservation and emission reduction in future steel production.

Development of a Waste Management Strategy in a Steel Company

The management of waste, especially ferrous waste, poses great problems in the steel industry due to strict regulations on preventing, reducing, or even eliminating the factors that generate a high degree of environmental pollution (landfills resulting from the steel industry and adjacent industries—mining, energy, etc.). The present paper presents a synthesis of the specialized literature regarding the processes used, both worldwide and nationally, regarding the transformation of raw materials (ores or concentrates) and iron-containing waste (steel mill dust, mill scale and scale, sludge from agglomeration factories, sideritic waste, etc.) into by-products that can be used in the steel industry. For technological reasons, the option of pelletizing powdered waste was applied—in laboratory conditions, according to its own recipes, with results that justify the application of the technology on an industrial scale (appearance after hardening; drop resistance). The aim of the paper was to identify a practical solution; based on this solution, original conceptual models of organizational strategies (management and processing, respectively, recovery of ferrous waste within steel companies) were developed, such as a concentration strategy, diversification, vertical integration, etc. Within graphical representations of the proposed strategies, other processing variants were mentioned: agglomeration; briquetting.

Comparison of various drying methods on color, texture, nutritional components, and antioxidant activity of tumorous stem mustard

Exploring new dehydration technology can help to deal with the challenge of quality preservation and high salt waste brine problem in pickled mustard industry. In the current study, effects of vacuum freeze-drying (FD), heat pump drying (HPD), air-impingement jet drying (AIJD), and hot air drying (HD), on color, texture, nutritional component, and antioxidant capacity of tumorous stem mustard were determined. All drying methods significantly affected color and texture. The ascorbic acid (Vc), reducing sugar, protein, total phenolic content (TPC), ABTS, DPPH, and FRAP level of tumorous stem mustard were 123.20–768.74 mg/100 g, 2.49–17.90 g/100 g, 30.74–53.03 g/100 g, 1.98–4.13 mg gallic acid equivalent (GAE)/g, 1.42–8.28 mg Vc/g, 1.04–9.73 mg Vc/g, and 2.11–6.83 mg Vc/g, respectively. The content of individual phenolics were influenced by drying methods, and ferulic acid was determined as a major phenolic compound for all samples (8.15–65.43 μg/g). The content of total phenolic and monomeric polyphenols except for ferulic acid, positively correlated to their antioxidant activity. Furthermore, HD, FD, and AIJD were beneficial to promote the production of umami amino acids and reduce bitter amino acids, thus improving the flavor of tumorous stem mustard. The quality of tumorous stem mustard of FD was closest to that of fresh tumorous stem mustard. Overall, the drying technology is promising to replace the salt infiltration process to realize the dehydration of tumorous stem mustard in the manufacturing of pickled mustard.

Synthesis of rice husk ash based alkaline activators for geopolymer binder systems: A review

At present, geopolymer binders have been considered to be one of the most promising low-carbon building materials. Apart from low carbon emissions, the production of geopolymer materials also helps in mitigating several industrial solid waste management problems. The geopolymer binder can be produced using any aluminosilicate waste material followed by mixing with an alkaline activator. However, the major limitation associated with geopolymers is their production cost which is slightly higher than traditional cement-based binders. Out of all the ingredients, sodium silicate is found to be the most expensive and least environment friendly material. Several researchers have therefore utilized many silica-rich waste products such as rice husk ash (RHA) based silicate solution as a low-cost alternative to commercial sodium silicate solution. In this regard, this paper provides an in-depth review of the past literature to understand the properties of geopolymer binders produced using RHA-derived alkaline activator. The production process, and the factors affecting dissolution of RHA have been elaborated. Further, the properties such as compressive strength, and microstructure have been detailed. Lastly, the sustainability and lifecycle assessment of geopolymers synthesized using RHA-based sodium silicate solution are presented. The information provided in this study shall encourage the scientific community to adopt RHA-based sodium silicate as a cost-effective, and environment friendly activator to produce geopolymers.

New energy vehicle technology research - Charging technology

In recent years, Chinas economic development is becoming more and more rapid, leading to the increasingly serious pollution emission problem of traditional fuel vehicles. In order to solve the environmental problems, the transformation of automobiles has become an urgent problem. At the present stage, electric vehicles are also widely concerned by all sectors of society because of their low carbon emissions, energy saving and low use cost. With the rapid growth of electric vehicle sales, the demand for charging piles is rising, and the mainstream charging facilities for new energy vehicles are still in short supply. Therefore, we need to focus on efficient wireless charging to solve the major problems in the current electric vehicle industry. This paper analyzes the wireless dynamic energy transmission technology, extending the service life of the battery, how to reduce the cost, the optimization of coil parameters design, the offset of transmission coil and receiving coil, foreign body detection and overheating problems.

Characterization of camphor: thymol or dl-menthol eutectic mixtures; Structure, thermophysical properties, and lidocaine solubility

The low solubility of drugs in aqueous media is a major problem for the pharmaceutical industry. Among the solutions proposed, the use of eutectic mixtures can be highlighted. In them, the components used can also increase the therapeutic advantages of the active principle. In this work, we analyzed the solubility of lidocaine in mixtures of camphor + thymol or dl-menthol. These binary eutectic solvents were also characterized. For all this, several NMR techniques were used and different thermophysical properties (density, speed of sound, refraction index, isobaric molar heat capacity, surface tension, and kinematic viscosity) were measured. The results indicated that the interaction of camphor with thymol was stronger than with dl-menthol, providing a more compact fluid. On the contrary, the steric hindrance was greater in the mixture with dl-menthol, giving rise to a more viscous liquid. Furthermore, the lidocaine was significantly more soluble (up to 540 times) in these mixtures than in water. The lidocaine showed the strongest interactions with thymol molecules, somewhat weaker with dl-menthol, and the weakest were with camphor ones.

Combinatorial optimization methods for yarn dyeing planning

AbstractManaging yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack, bin packing, and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However, several practical factors such as physical attributes of customer orders, dyeing machine eligibility conditions like flotte, color type, chemical recipe, and volume capacity of dye make this problem significantly unique. Furthermore, alongside its economic aspects, minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective, which adds another complexity for planners. Hence, in this paper, we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research, including a mixed-integer programming model for a novel industrial problem, providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly, and addressing the inefficiencies of manual scheduling in textile companies; reducing a time-consuming planning task from hours to minutes.

A novel fuzzy scenario-based stochastic general best-worst method

The best-worst method (BWM) is a popular multi-criteria decision-making (MCDM) method known for the low number of pairwise comparisons and high consistency. General BWM (GBWM) is a new version of BWM that considers the interdependencies between interwind factors in MCDM problems. This study proposes a fuzzy stochastic GBWM for weighting intertwined factors using a scenario-based approach in complex intertwined or hierarchical networks under uncertainty. Fuzzy stochastic GBWM provides decision-makers with a wide range of weights, from fuzzy-stochastic weights to stochastic weights (defuzzified weights), fuzzy weights, and deterministic weights to use with different assumptions in the decision-making process. We demonstrate the efficacy and applicability of the proposed method with a well-known car-buying problem in the literature and a real-world problem in the transportation industry.