A Novel Approach Based on Combining ANFIS, Genetic Algorithm and Fuzzy c-Means Methods for Multiple Criteria Inventory Classification

İsen, Elif; Boran, Semra

doi:10.1007/s13369-017-2987-z

Cited by 15 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The neuro-fuzzy models with the subtractive clustering membership function trained by the hybrid (ANFIS2-H) and backpropagation (ANFIS2-BP) algorithms have also been applied in the current study 54 . The last intelligent tools used in the present research are the neuro-fuzzy models with the C-means clustering membership function trained by hybrid (ANFIS3-H) and backpropagation (ANFIS3-BP) optimization strategies 55 .…”

Section: Methodsmentioning

confidence: 99%

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

Rezaei

Nazarpour

Shahini

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Understanding the drug solubility behavior is likely the first essential requirement for designing the supercritical technology for pharmaceutical processing. Therefore, this study utilizes different machine learning scenarios to simulate the solubility of twelve non-steroidal anti-inflammatory drugs (NSAIDs) in the supercritical carbon dioxide (SCCO2). The considered NSAIDs are Fenoprofen, Flurbiprofen, Ibuprofen, Ketoprofen, Loxoprofen, Nabumetone, Naproxen, Nimesulide, Phenylbutazone, Piroxicam, Salicylamide, and Tolmetin. Physical characteristics of the drugs (molecular weight and melting temperature), operating conditions (pressure and temperature), and solvent property (SCCO2 density) are effectively used to estimate the drug solubility. Monitoring and comparing the prediction accuracy of twelve intelligent paradigms from three categories (artificial neural networks, support vector regression, and hybrid neuro-fuzzy) approves that adaptive neuro-fuzzy inference is the best tool for the considered task. The hybrid optimization strategy adjusts the cluster radius of the subtractive clustering membership function to 0.6111. This model estimates 254 laboratory-measured solubility data with the AAPRE = 3.13%, MSE = 2.58 × 10–9, and R2 = 0.99919. The leverage technique confirms that outliers may poison less than four percent of the experimental data. In addition, the proposed hybrid paradigm is more reliable than the equations of state and available correlations in the literature. Experimental measurements, model predictions, and relevancy analyses justified that the drug solubility in SCCO2 increases by increasing temperature and pressure. The results show that Ibuprofen and Naproxen are the most soluble and insoluble drugs in SCCO2, respectively.

show abstract

Section: Methodsmentioning

confidence: 99%

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

Rezaei

Nazarpour

Shahini

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…GA has been widely used in research to predict inventory and improve inventory management. Isen and Boran [44] proposed a hybrid model generated byGA, fuzzy C-means (FCM), and adaptive neuro-fuzzy reasoning system (ANFIS) for inventory classification, which has great application value. Bhunia and Kundu [45] use a hybrid tournament genetic algorithm (TGA) to solve partial backlogged shortages where a deterioration rate of items was investigated.…”

Section: Demand Forecasting and Inventory Optimizationmentioning

confidence: 99%

Integrated Smart Warehouse and Manufacturing Management with Demand Forecasting in Small-Scale Cyclical Industries

Tang

Lau

et al. 2022

Machines

View full text Add to dashboard Cite

In the context of the global economic slowdown, demand forecasting, and inventory and production management have long been important topics to the industries. With the support of smart warehouses, big data analytics, and optimization algorithms, enterprises can achieve economies of scale, and balance supply and demand. Smart warehouse and manufacturing management is considered the culmination of recently advanced technologies. It is important to enhance the scalability and extendibility of the industry. Despite many researchers having developed frameworks for smart warehouse and manufacturing management for various fields, most of these models are mainly focused on the logistics of the product and are not generalized to tackle the specific manufacturing problem facing in the cyclical industry. Indeed, the cyclical industry has a key problem: the big risk which high sensitivity poses to the business cycle and economic recession, which is difficult to foresee. Despite many inventory optimization approaches being proposed to optimize the inventory level in the warehouse and facilitate production management, the demand forecasting technique is seldom focused on the cyclic industry. On the other hand, management approaches are usually based on the complex logistics process instead of integrating the inventory level of the stock, which is very crucial to composing smart warehouses and manufacturing. This research study proposed a digital twin framework by integrating the smart warehouse and manufacturing with the roulette genetic algorithm for demand forecasting in the cyclical industry. We also demonstrate how this algorithm is practically implemented for forecasting the demand, sustaining manufacturing optimization, and achieving inventory optimization. We adopted a small-scale textile company case study to demonstrate the proposed digital framework in the warehouse and demonstrate the results of demand forecasting and inventory optimization. Various scenarios were conducted to simulate the results for the digital twin. The proposed digital twin framework and results help manufacturers and logistics companies to improve inventory management. This study has important theoretical and practical significance for the management of the cyclical industry.

show abstract

“…Another related technique is the Fuzzy Neural network (FNN) which Kumari et al (2013) describe as a hybrid neuro-fuzzy technique that combines the fuzzy structures' human-like reasoning alongside the learning and connection potential of the ANN to form a married model. Hence, FNN, in its architecture, forms a hybrid learning algorithm that adopts the IF-THEN rule capable of dealing with qualitative and quantitative information (İsen & Boran, 2018). It is particularly useful as it replaces the crisp figures of the ANN where knowledge is stored in weight with MF found in FL technique which minimises the subjectivity tendencies of the valuers (Król et al, 2016).…”

Section: Related Researchesmentioning

confidence: 99%

An Integrated Approach Based on Artificial Intelligence Using Anfis and Ann for Multiple Criteria Real Estate Price Prediction

Yakub

Ali

Achu

et al. 2021

View full text Add to dashboard Cite

A relatively high level of precision is required in real estate valuation for investment purposes. Such estimates of value which is carried out by real estate professionals are relied upon by the end-users of such financial information having paid a certain fee for consultation hence leaving little room for errors. However, valuation reports are often criticised for their inability to be replicated by two or more valuers. Hence, stirring to a keen interest within the academic cycle leading to the need for exploring avenues to improve the price prediction ability of the professional valuer. This study, therefore, focuses on overcoming these challenges by introducing an integrated approach that combines ANFIS with ANN termed ANFIS-AN, thereby having a reiteration in terms of ANN application to fortify price predictability. Using 255 property data alongside 12 variables, the ANFIS-AN model was adopted and its outcome was compared with that of ANN. Finally, the results were subjected to 3 different error testing models using the same training and learning benchmarks. The proposed model’s RMSE gave 1.413169, while that of ANN showed 9.942206. Similarly, using MAPE, ANN recorded 0.256438 while ANFIS-AN had 0.208324. Hence, ANFIS-AN’s performance is laudable, thus a better tool over ANN.

show abstract

A Novel Approach Based on Combining ANFIS, Genetic Algorithm and Fuzzy c-Means Methods for Multiple Criteria Inventory Classification

Cited by 15 publications

References 48 publications

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

Integrated Smart Warehouse and Manufacturing Management with Demand Forecasting in Small-Scale Cyclical Industries

An Integrated Approach Based on Artificial Intelligence Using Anfis and Ann for Multiple Criteria Real Estate Price Prediction

Contact Info

Product

Resources

About