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In recent years, environmental protection and sustainability have become significant issues and have attracted everyone's attention. And many organizations are now interested in using it as their strategy to gain customer satisfaction and market share and outperform competitors. This article aims to identify and prioritize the main factors that implement green supply chain management (GSCM) in the construction industry. To achieve the goal, the integrated approach combining is fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and fuzzy analysis network Process (FANP) developed. The parameters employed are in this approach identified through an extensive literature review, and validation is criteria introduced through the experts’ opinions to discuss data uncertainty. First, the FDEMATEL method sets up the interrelationships between the criteria, which used for determining are the most important factors in the GSCM approach. Then, the local weight of the criteria calculated using the FANP approach based on cause and effect relationships, and through the FDEMATEL method. The results of this study show that external factors are the most important and influential factors in the GSCM approach, Therefore, the findings of this study can guide managers to make better use of the GSCM approach in the Iranian construction industry.
Erfan Taghavi; Alireza Fallahpour; Kuan Yew Wong; Seyed Amirali Hoseini. Identifying and prioritizing the effective factors in the implementation of green supply chain management in the construction industry. Sustainable Operations and Computers 2021, 2, 97 -106.
AMA StyleErfan Taghavi, Alireza Fallahpour, Kuan Yew Wong, Seyed Amirali Hoseini. Identifying and prioritizing the effective factors in the implementation of green supply chain management in the construction industry. Sustainable Operations and Computers. 2021; 2 ():97-106.
Chicago/Turabian StyleErfan Taghavi; Alireza Fallahpour; Kuan Yew Wong; Seyed Amirali Hoseini. 2021. "Identifying and prioritizing the effective factors in the implementation of green supply chain management in the construction industry." Sustainable Operations and Computers 2, no. : 97-106.
The effectiveness of magnesium (Mg) alloys to improve the capability of bone tissue generation may be severely diminished if the required mechanical properties are not provided. Here, the effort is directed to model the mechanical performance of severely plastically deformed biodegradable ZK60 Mg alloy in bone regeneration protocols. For this purpose, the effects of parallel tubular channel angular pressing (PTCAP) on yield strength (σ YS ), ultimate tensile strength (σ UTS ), and elongation to failure (δ) were addressed. Given the multifaceted variables of the PTCAP with nonlinear interactions, a precise determination of the mechanical properties requires a large number of experiments. Therefore, gene expression programming (GEP) and genetic programming (GP) models were proposed to achieve appropriate combinations of mechanical properties for bone implant purposes based on a rational hypothesis that for correlation coefficient (|R|) higher than 0.8, a strong correlation is established between the predicted and measured values. The results verified that the highest mechanical performance was achieved at the second pass of PTCAP, thus has a great potential to be the most promising candidate for biodegradable implant material. Besides, the proposed models were capable of precisely predicting the mechanical performance of the SPD-processed biodegradable ZK60 Mg.
Yan Zhang; Ning Wang; Jingyi Li; Mohsen Mesbah; Kuan Yew Wong; Alireza Fallahpour; Bahman Nasiri-Tabrizi; Jiangfei Yang. Mechanical properties modeling of severely plastically deformed biodegradable ZK60 magnesium alloy for bone implants. Latin American Journal of Solids and Structures 2020, 17, 1 .
AMA StyleYan Zhang, Ning Wang, Jingyi Li, Mohsen Mesbah, Kuan Yew Wong, Alireza Fallahpour, Bahman Nasiri-Tabrizi, Jiangfei Yang. Mechanical properties modeling of severely plastically deformed biodegradable ZK60 magnesium alloy for bone implants. Latin American Journal of Solids and Structures. 2020; 17 (5):1.
Chicago/Turabian StyleYan Zhang; Ning Wang; Jingyi Li; Mohsen Mesbah; Kuan Yew Wong; Alireza Fallahpour; Bahman Nasiri-Tabrizi; Jiangfei Yang. 2020. "Mechanical properties modeling of severely plastically deformed biodegradable ZK60 magnesium alloy for bone implants." Latin American Journal of Solids and Structures 17, no. 5: 1.
Supplier evaluation and selection is a complicated multiple criteria decision-making process which affects supply chain management (SCM) directly. Recent studies emphasize that artificial intelligence approaches obtain better performance than conventional methods in evaluating the suppliers’ performance and determining the best suppliers. Hence, this study proposes a new robust genetic-based intelligent approach, namely gene expression programming (GEP), to improve the supplier selection process for a supply chain and to cope with the drawback of the other intelligent approaches in this area. The applicability of this method was exhibited by a case study in the textile manufacturing industry. To show the performance of the mathematical-genetic model, comparisons with four intelligent techniques, namely multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network, adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM), were performed. The results derived from the intelligent approaches were compared by using a collected dataset from a textile factory. The obtained results demonstrated that first the GEP-based model provides a mathematical model for the suppliers’ performance based on the determined criteria, and the developed GEP model is more accurate than the four other intelligent models in terms of accuracy in performance estimation. In addition, to verify the validity of the developed model, different statistical tests were used and the results showed that the GEP model is statistically powerful.
Alireza Fallahpour; Kuan Yew Wong; Ezutah Udoncy Olugu; Siti Nurmaya Musa. A Predictive Integrated Genetic-Based Model for Supplier Evaluation and Selection. International Journal of Fuzzy Systems 2017, 19, 1041 -1057.
AMA StyleAlireza Fallahpour, Kuan Yew Wong, Ezutah Udoncy Olugu, Siti Nurmaya Musa. A Predictive Integrated Genetic-Based Model for Supplier Evaluation and Selection. International Journal of Fuzzy Systems. 2017; 19 (4):1041-1057.
Chicago/Turabian StyleAlireza Fallahpour; Kuan Yew Wong; Ezutah Udoncy Olugu; Siti Nurmaya Musa. 2017. "A Predictive Integrated Genetic-Based Model for Supplier Evaluation and Selection." International Journal of Fuzzy Systems 19, no. 4: 1041-1057.
Supplier evaluation and selection constitutes a central issue in supply chain management (SCM). However, the data on which to base the corresponding choices in real life problems are often imprecise or vague, which has led to the introduction of fuzzy approaches. Predictive intelligent-based techniques, such as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS), have been recently applied in different research fields to model fuzzy multi-criteria decision processes where the understanding and learning of the relationships between the input and output data are the key to select suitable solutions. In this paper, a hybrid ANFIS-ANN model is proposed to assist managers in their supplier evaluation process. After aggregating the data set through the Analytical Hierarchy Process (AHP), the most influential criteria on the suppliers’ performance are determined by ANFIS. Then, Multi-Layer Perceptron (MLP) is used to predict and rank the suppliers’ performance based on the most effective criteria. A case study is presented to illustrate the main steps of the model and show its accuracy in prediction. A battery of parametric tests and sensitivity analyses has been implemented to evaluate the overall performance of several models based on different effective criteria combinations.
Madjid Tavana; Alireza Fallahpour; Debora DI Caprio; Francisco J. Santos-Arteaga. A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection. Expert Systems with Applications 2016, 61, 129 -144.
AMA StyleMadjid Tavana, Alireza Fallahpour, Debora DI Caprio, Francisco J. Santos-Arteaga. A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection. Expert Systems with Applications. 2016; 61 ():129-144.
Chicago/Turabian StyleMadjid Tavana; Alireza Fallahpour; Debora DI Caprio; Francisco J. Santos-Arteaga. 2016. "A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection." Expert Systems with Applications 61, no. : 129-144.
Supplier evaluation and selection is a complicated process which deals with conflicting attributes such as quality, cost. To mitigate the computational complexity, intelligent-based techniques have gained much popularity. But the main shortcoming of the existing models in this regard is to be a black box system. In this paper, we aim to combine analytical hierarchy process with multi-expression programming to both introduce a new evolutionary approach in the field of supplier evaluation and selection and cope with the earlier problem. To show the validity of the model, statistical test was carried out. The finding showed that the proposed model is accurate and acceptable for using in the evaluation process.
Alireza Fallahpour; Ezutah Udoncy Olugu; Siti Nurmaya Musa. A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP). Neural Computing and Applications 2015, 28, 499 -504.
AMA StyleAlireza Fallahpour, Ezutah Udoncy Olugu, Siti Nurmaya Musa. A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP). Neural Computing and Applications. 2015; 28 (3):499-504.
Chicago/Turabian StyleAlireza Fallahpour; Ezutah Udoncy Olugu; Siti Nurmaya Musa. 2015. "A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP)." Neural Computing and Applications 28, no. 3: 499-504.
Supplier evaluation plays a critical role in a successful supply chain management. Hence, the evaluation and selection of the right suppliers have become a central decision of manufacturing business activities around the world. Consequently, numerous individual and integrated methods have been presented to evaluate and select suppliers. The current literature shows that hybrid artificial intelligence (AI)-based models have received much attention for supplier evaluation. Integrated data envelopment analysis–artificial neural network (DEA–ANN) is one of the combined methods that have recently garnered great attention from academics and practitioners. However, DEA–ANN model has some drawbacks, which make some limitation in the evaluation process. In this study, we aim at improving the previous DEA–AI models by integrating the Kourosh and Arash method as a robust model of DEA with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA–AI models in supplier selection. Indeed, in this paper, GP provides a robust nonlinear mathematical equation for the suppliers’ efficiency using the determined criteria. To validate the model, adaptive neuro-fuzzy inference system as a powerful tool was used to compare the result with GP-based model. In addition, parametric analysis and unseen data set were used to validate the precision of the model.
Alireza Fallahpour; Ezutah Udoncy Olugu; Siti Nurmaya Musa; Dariush Khezrimotlagh; Kuan Yew Wong. An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach. Neural Computing and Applications 2015, 27, 707 -725.
AMA StyleAlireza Fallahpour, Ezutah Udoncy Olugu, Siti Nurmaya Musa, Dariush Khezrimotlagh, Kuan Yew Wong. An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach. Neural Computing and Applications. 2015; 27 (3):707-725.
Chicago/Turabian StyleAlireza Fallahpour; Ezutah Udoncy Olugu; Siti Nurmaya Musa; Dariush Khezrimotlagh; Kuan Yew Wong. 2015. "An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach." Neural Computing and Applications 27, no. 3: 707-725.
Assigning precise values for attributes in most engineering applications is almost impossible. It is therefore necessary to consider yarn properties as intervals. Hence, an extended version of VIKOR was employed in this study to select suitable spinning conditions for the weft knitting process among feasible alternatives. Three variables in rotor-spun box were considered and their performances were evaluated based on seven quality parameters using 16 yarn samples. The final ranking was presented according to the relative closeness coefficient of the ideal solution. Consequently, stability of the proposed final ranking was verified.
Alireza Fallahpour; Ezutah Udoncy Olugu; Abdolrasool Moghassem; Siti Nurmaya Musa. Ranking Alternatives in Rotor-Spun Knitting Process Using Extended VIKOR on Interval Data. Journal of Engineered Fibers and Fabrics 2014, 9, 1 .
AMA StyleAlireza Fallahpour, Ezutah Udoncy Olugu, Abdolrasool Moghassem, Siti Nurmaya Musa. Ranking Alternatives in Rotor-Spun Knitting Process Using Extended VIKOR on Interval Data. Journal of Engineered Fibers and Fabrics. 2014; 9 (4):1.
Chicago/Turabian StyleAlireza Fallahpour; Ezutah Udoncy Olugu; Abdolrasool Moghassem; Siti Nurmaya Musa. 2014. "Ranking Alternatives in Rotor-Spun Knitting Process Using Extended VIKOR on Interval Data." Journal of Engineered Fibers and Fabrics 9, no. 4: 1.