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Sustainable supply chain management (SSCM) is the integration of economic, social, and environmental concerns into the supply chain practices that plays an important role to minimize industrial pollution, global warming, and carbon emission and make a supply chain green. In the present article, a novel fuzzy analytical hierarchy process (AHP) as a fuzzy preference programming (FPP) approach has been combined with fuzzy inference system (FIS) for evaluating the performance of suppliers considering carbon management (CM) indicators. To do so, 12 indicators are classified in four basic perspectives applying the Delphi method. The importance weights of perspectives and their related indicators are calculated performing FPP. Accordingly, the weighted performance values that are inputs of FIS are calculated and finally, the weighted fuzzy system will be applied to obtain the total environmental performance of the suppliers. Moreover, we implemented our model within a home appliances manufacturing company in Iran to examine its efficiency. Results approve that the proposed approach can apply efficiently in real situations and are reliable-enough. Furthermore, findings provide more insight for the managers of the company toward the concept of the CM and make them able to find their weaknesses and work with their partners to fix them. [ABSTRACT FROM AUTHOR]

The importance of supplier selection is nowadays highlighted more than ever as companies have realized that efficient supplier selection can significantly improve the performance of their supply chain. In this paper, an integrated model that applies Data Envelopment Analysis (DEA) and Support Vector Machine (SVM) is developed to select efficient suppliers based on their predicted efficiency scores. In the first step, fuzzy linguistic variables are changed to crisp data as initial dataset for DEA. Actual efficiency scores are then calculated for each Decision Making Unit (DMU) using CCR-DEA model. Afterwards, suppliers' performance-related data are used for training SVM-DEA model. A numerical example representing an actual case is provided to indicate the applicability of the model. [ABSTRACT FROM AUTHOR]

Evaluation and selection of candidate suppliers has become a major decision in business activities around the world. In this paper, a new hybrid approach based on integration of Gene Expression Programming (GEP) with Data Envelopment Analysis (DEA) (DEA-GEP) is presented to overcome the supplier selection problem. First, suppliers’ efficiencies are obtained through applying DEA. Then, the suppliers’ related data are utilized to train GEP to find the best trained DEA-GEP algorithm for predicting efficiency score of Decision Making Units (DMUs). The aforementioned data is also used to train Artificial Neural Network (ANN) to predict efficiency scores of DMUs. The proposed hybrid DEA-GEP is compared to integrated approach of Artificial Neural Network with Data Envelopment Analysis (DEA-ANN) to support the validity of the proposed model. The obtained results clearly show that the model based on GEP not only is more accurate than the DEA-ANN model, but also presents a mathematical function for efficiency score based on input and output data set. Finally, a real-life supplier selection problem is presented to show the applicability of the proposed hybrid DEA-GEP model. [ABSTRACT FROM AUTHOR]

Selecting the most resilient supplier is a crucial problem for organizations and managers in the supply chain. However, due to the inherited high degree of uncertainty in real-life projects, developing a decision-making framework in a crisp or fuzzy environment may not present accurate or reliable results for the managers. For this reason, it is better to evaluate the potential suppliers in an Interval Type-2 Fuzzy (IT2F) environment for better dealing with this ambiguity. This study developed an improved combined IT2F Best Worst Method (BWM) and IT2F technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model "Atieh Sazan" Co. as a case study, such that the IT2FBWM was employed for obtaining the weight of criteria. The IT2FTOPSIS was utilized for ranking the potential suppliers based on Hamming distance measure. In both phases, the opinions of experts as IT2F linguistic terms were employed for weighting the criteria and obtaining the relative importance of the alternatives in terms of the evaluative criteria. After obtaining the final results, the proposed model was validated by replacing Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) approaches separately instead of BWM for weighting the criteria. After executing both new models, it was found that the final ranking was similar to the final ranking of the proposed model, representing the reliability and accuracy of the obtained results. Moreover, it was concluded that the resilient criteria of "Reorganization" and "Redundancy" are the most determinant measures for selecting the best supplier rather than measures in the Iranian Construction Industry. [ABSTRACT FROM AUTHOR]

Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively. [ABSTRACT FROM AUTHOR]